1
|
Cantrell A, Chambers D, Booth A. Interventions to minimise hospital winter pressures related to discharge planning and integrated care: a rapid mapping review of UK evidence. HEALTH AND SOCIAL CARE DELIVERY RESEARCH 2024; 12:1-116. [PMID: 39267416 DOI: 10.3310/krwh4301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/17/2024]
Abstract
Background Winter pressures are a familiar phenomenon within the National Health Service and represent the most extreme of many regular demands placed on health and social care service provision. This review focuses on a part of the pathway that is particularly problematic: the discharge process from hospital to social care and the community. Although studies of discharge are plentiful, we identified a need to focus on identifying interventions and initiatives that are a specific response to 'winter pressures'. This mapping review focuses on interventions or initiatives in relation to hospital winter pressures in the United Kingdom with either discharge planning to increase smart discharge (both a reduction in patients waiting to be discharged and patients being discharged to the most appropriate place) and/or integrated care. Methods We conducted a mapping review of United Kingdom evidence published 2018-22. Initially, we searched MEDLINE, Health Management Information Consortium, Social Care Online, Social Sciences Citation Index and the King's Fund Library to find relevant interventions in conjunction with winter pressures. From these interventions we created a taxonomy of intervention types and a draft map. A second broader stage of searching was then undertaken for named candidate interventions on Google Scholar (Google Inc., Mountain View, CA, USA). For each taxonomy heading, we produced a table with definitions, findings from research studies, local initiatives and systematic reviews and evidence gaps. Results The taxonomy developed was split into structural, changing staff behaviour, changing community provision, integrated care, targeting carers, modelling and workforce planning. The last two categories were excluded from the scope. Within the different taxonomy sections we generated a total of 41 headings. These headings were further organised into the different stages of the patient pathway: hospital avoidance, alternative delivery site, facilitated discharge and cross-cutting. The evidence for each heading was summarised in tables and evidence gaps were identified. Conclusions Few initiatives identified were specifically identified as a response to winter pressures. Discharge to assess and hospital at home interventions are heavily used and well supported by the evidence but other responses, while also heavily used, were based on limited evidence. There is a lack of studies considering patient, family and provider needs when developing interventions aimed at improving delayed discharge. Additionally, there is a shortage of studies that measure the longer-term impact of interventions. Hospital avoidance and discharge planning are whole-system approaches. Considering the whole health and social care system is imperative to ensure that implementing an initiative in one setting does not just move the problem to another setting. Limitations Time limitations for completing the review constrained the period available for additional searches. This may carry implications for the completeness of the evidence base identified. Future work Further research to consider a realist review that views approaches across the different sectors within a whole system evaluation frame. Funding This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: NIHR130588) and is published in full in Health and Social Care Delivery Research; Vol. 12, No. 31. See the NIHR Funding and Awards website for further award information.
Collapse
Affiliation(s)
- Anna Cantrell
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Duncan Chambers
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Andrew Booth
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| |
Collapse
|
2
|
Teodorowski P, Jones E, Tahir N, Ahmed S, Rodgers SE, Frith L. Public Involvement and Engagement in Big Data Research: Scoping Review. J Particip Med 2024; 16:e56673. [PMID: 39150751 PMCID: PMC11364952 DOI: 10.2196/56673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 05/06/2024] [Accepted: 06/22/2024] [Indexed: 08/17/2024] Open
Abstract
BACKGROUND The success of big data initiatives depends on public support. Public involvement and engagement could be a way of establishing public support for big data research. OBJECTIVE This review aims to synthesize the evidence on public involvement and engagement in big data research. METHODS This scoping review mapped the current evidence on public involvement and engagement activities in big data research. We searched 5 electronic databases, followed by additional manual searches of Google Scholar and gray literature. In total, 2 public contributors were involved at all stages of the review. RESULTS A total of 53 papers were included in the scoping review. The review showed the ways in which the public could be involved and engaged in big data research. The papers discussed a broad range of involvement activities, who could be involved or engaged, and the importance of the context in which public involvement and engagement occur. The findings show how public involvement, engagement, and consultation could be delivered in big data research. Furthermore, the review provides examples of potential outcomes that were produced by involving and engaging the public in big data research. CONCLUSIONS This review provides an overview of the current evidence on public involvement and engagement in big data research. While the evidence is mostly derived from discussion papers, it is still valuable in illustrating how public involvement and engagement in big data research can be implemented and what outcomes they may yield. Further research and evaluation of public involvement and engagement in big data research are needed to better understand how to effectively involve and engage the public in big data research. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-https://doi.org/10.1136/bmjopen-2021-050167.
Collapse
Affiliation(s)
- Piotr Teodorowski
- Faculty of Health Sciences and Sport, University of Stirling, Stirling, United Kingdom
| | - Elisa Jones
- Department of Public Health, Policy & Systems, University of Liverpool, Liverpool, United Kingdom
| | - Naheed Tahir
- National Institute for Health and Care Research Applied Research Collaboration North West Coast, Liverpool, United Kingdom
| | - Saiqa Ahmed
- National Institute for Health and Care Research Applied Research Collaboration North West Coast, Liverpool, United Kingdom
| | - Sarah E Rodgers
- Department of Public Health, Policy & Systems, University of Liverpool, Liverpool, United Kingdom
| | - Lucy Frith
- Centre for Social Ethics and Policy, University of Manchester, Manchester, United Kingdom
| |
Collapse
|
3
|
Calcote MJ, Mann JR, Adcock KG, Duckworth S, Donald MC. Big Data in Health Care: An Interprofessional Course. Nurse Educ 2024; 49:E187-E191. [PMID: 37994454 DOI: 10.1097/nne.0000000000001571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2023]
Abstract
BACKGROUND The widespread adoption of the electronic health record (EHR) has resulted in vast repositories of EHR big data that are being used to identify patterns and correlations that translate into data-informed health care decision making. PROBLEM Health care professionals need the skills necessary to navigate a digitized, data-rich health care environment as big data plays an increasingly integral role in health care. APPROACH Faculty incorporated the concept of big data in an asynchronous online course allowing an interprofessional mix of students to analyze EHR big data on over a million patients. OUTCOMES Students conducted a descriptive analysis of cohorts of patients with selected diagnoses and presented their findings. CONCLUSIONS Students collaborated with an interprofessional team to analyze EHR big data on selected variables. The teams used data visualization tools to describe an assigned diagnosis patient population.
Collapse
Affiliation(s)
- Margaret J Calcote
- Author Affiliations: Assistant Professor (Dr Calcote), The University of Mississippi Medical Center School of Nursing, Jackson; Professor and Chair (Dr Mann), Department of Preventive Medicine, The University of Mississippi Medical Center School of Medicine, Jackson; Professor (Dr Adcock), Pharmacy Division, The University of Mississippi Medical Center School of Pharmacy, Jackson; Professor (Dr Duckworth), The University of Mississippi Medical Center Division of Internal Medicine, Jackson; and Medical Student M3 (Mr Donald), The University of Mississippi Medical Center School of Medicine, Jackson
| | | | | | | | | |
Collapse
|
4
|
Ikegami K, Imai S, Yasumuro O, Tsuchiya M, Henmi N, Suzuki M, Hayashi K, Miura C, Abe H, Kizaki H, Funakoshi R, Sato Y, Hori S. External Validation and Update of the Risk Prediction Model for Denosumab-Induced Hypocalcemia Developed From a Hospital-Based Administrative Database. JCO Clin Cancer Inform 2024; 8:e2400078. [PMID: 39008783 PMCID: PMC11371100 DOI: 10.1200/cci.24.00078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 04/23/2024] [Accepted: 05/10/2024] [Indexed: 07/17/2024] Open
Abstract
PURPOSE Denosumab is used to treat patients with bone metastasis from solid tumors, but sometimes causes severe hypocalcemia, so careful clinical management is important. This study aims to externally validate our previously developed risk prediction model for denosumab-induced hypocalcemia by using data from two facilities with different characteristics in Japan and to develop an updated model with improved performance and generalizability. METHODS In the external validation, retrospective data of Kameda General Hospital (KGH) and Miyagi Cancer Center (MCC) between June 2013 and June 2022 were used and receiver operating characteristic (ROC)-AUC was mainly evaluated. A scoring-based updated model was developed using the same data set from a hospital-based administrative database as previously employed. Selection of variables related to prediction of hypocalcemia was based on the results of external validation. RESULTS For the external validation, data from 235 KGH patients and 224 MCC patients were collected. ROC-AUC values in the original model were 0.879 and 0.774, respectively. The updated model consisting of clinical laboratory tests (calcium, albumin, and alkaline phosphatase) afforded similar ROC-AUC values in the two facilities (KGH, 0.837; MCC, 0.856). CONCLUSION We developed an updated risk prediction model for denosumab-induced hypocalcemia with small interfacility differences. Our results indicate the importance of using data from plural facilities with different characteristics in the external validation of generalized prediction models and may be generally relevant to the clinical application of risk prediction models. Our findings are expected to contribute to improved management of bone metastasis treatment.
Collapse
Affiliation(s)
- Keisuke Ikegami
- Keio University Faculty of Pharmacy/Graduate School of Pharmaceutical Sciences, Tokyo, Japan
| | - Shungo Imai
- Keio University Faculty of Pharmacy/Graduate School of Pharmaceutical Sciences, Tokyo, Japan
| | - Osamu Yasumuro
- Department of Pharmacy, Kameda General Hospital, Chiba, Japan
| | - Masami Tsuchiya
- Keio University Faculty of Pharmacy/Graduate School of Pharmaceutical Sciences, Tokyo, Japan
- Department of Pharmacy, Miyagi Cancer Center, Miyagi, Japan
| | - Naomi Henmi
- Department of Pharmacy, Miyagi Cancer Center, Miyagi, Japan
| | - Mariko Suzuki
- Department of Pharmacy, Miyagi Cancer Center, Miyagi, Japan
| | | | - Chisato Miura
- Department of Pharmacy, Miyagi Cancer Center, Miyagi, Japan
| | - Haruna Abe
- Department of Pharmacy, Miyagi Cancer Center, Miyagi, Japan
| | - Hayato Kizaki
- Keio University Faculty of Pharmacy/Graduate School of Pharmaceutical Sciences, Tokyo, Japan
| | | | - Yasunori Sato
- Department of Biostatistics, Keio University School of Medicine, Tokyo, Japan
| | - Satoko Hori
- Keio University Faculty of Pharmacy/Graduate School of Pharmaceutical Sciences, Tokyo, Japan
| |
Collapse
|
5
|
Wieland-Jorna Y, van Kooten D, Verheij RA, de Man Y, Francke AL, Oosterveld-Vlug MG. Natural language processing systems for extracting information from electronic health records about activities of daily living. A systematic review. JAMIA Open 2024; 7:ooae044. [PMID: 38798774 PMCID: PMC11126158 DOI: 10.1093/jamiaopen/ooae044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 03/21/2024] [Accepted: 05/07/2024] [Indexed: 05/29/2024] Open
Abstract
Objective Natural language processing (NLP) can enhance research on activities of daily living (ADL) by extracting structured information from unstructured electronic health records (EHRs) notes. This review aims to give insight into the state-of-the-art, usability, and performance of NLP systems to extract information on ADL from EHRs. Materials and Methods A systematic review was conducted based on searches in Pubmed, Embase, Cinahl, Web of Science, and Scopus. Studies published between 2017 and 2022 were selected based on predefined eligibility criteria. Results The review identified 22 studies. Most studies (65%) used NLP for classifying unstructured EHR data on 1 or 2 ADL. Deep learning, combined with a ruled-based method or machine learning, was the approach most commonly used. NLP systems varied widely in terms of the pre-processing and algorithms. Common performance evaluation methods were cross-validation and train/test datasets, with F1, precision, and sensitivity as the most frequently reported evaluation metrics. Most studies reported relativity high overall scores on the evaluation metrics. Discussion NLP systems are valuable for the extraction of unstructured EHR data on ADL. However, comparing the performance of NLP systems is difficult due to the diversity of the studies and challenges related to the dataset, including restricted access to EHR data, inadequate documentation, lack of granularity, and small datasets. Conclusion This systematic review indicates that NLP is promising for deriving information on ADL from unstructured EHR notes. However, what the best-performing NLP system is, depends on characteristics of the dataset, research question, and type of ADL.
Collapse
Affiliation(s)
- Yvonne Wieland-Jorna
- Netherlands Institute for Health Services Research (Nivel), Utrecht, Postbus 1568, 3500 BN, The Netherlands
- Tranzo, School of Social Sciences and Behavioural Research, Tilburg University, Tilburg, Postbus 90153, 5000 LE, The Netherlands
| | - Daan van Kooten
- Netherlands Institute for Health Services Research (Nivel), Utrecht, Postbus 1568, 3500 BN, The Netherlands
| | - Robert A Verheij
- Netherlands Institute for Health Services Research (Nivel), Utrecht, Postbus 1568, 3500 BN, The Netherlands
- Tranzo, School of Social Sciences and Behavioural Research, Tilburg University, Tilburg, Postbus 90153, 5000 LE, The Netherlands
| | - Yvonne de Man
- Netherlands Institute for Health Services Research (Nivel), Utrecht, Postbus 1568, 3500 BN, The Netherlands
| | - Anneke L Francke
- Netherlands Institute for Health Services Research (Nivel), Utrecht, Postbus 1568, 3500 BN, The Netherlands
- Department of Public and Occupational Health, Location Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Postbus 7057, 1007 MB, The Netherlands
| | - Mariska G Oosterveld-Vlug
- Netherlands Institute for Health Services Research (Nivel), Utrecht, Postbus 1568, 3500 BN, The Netherlands
| |
Collapse
|
6
|
Jonathan J, Barakabitze AA, Fast CD, Cox C. Machine Learning for Prediction of Tuberculosis Detection: Case Study of Trained African Giant Pouched Rats. Online J Public Health Inform 2024; 16:e50771. [PMID: 38625737 PMCID: PMC11061786 DOI: 10.2196/50771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 08/27/2023] [Accepted: 03/15/2024] [Indexed: 04/17/2024] Open
Abstract
BACKGROUND Technological advancement has led to the growth and rapid increase of tuberculosis (TB) medical data generated from different health care areas, including diagnosis. Prioritizing better adoption and acceptance of innovative diagnostic technology to reduce the spread of TB significantly benefits developing countries. Trained TB-detection rats are used in Tanzania and Ethiopia for operational research to complement other TB diagnostic tools. This technology has increased new TB case detection owing to its speed, cost-effectiveness, and sensitivity. OBJECTIVE During the TB detection process, rats produce vast amounts of data, providing an opportunity to identify interesting patterns that influence TB detection performance. This study aimed to develop models that predict if the rat will hit (indicate the presence of TB within) the sample or not using machine learning (ML) techniques. The goal was to improve the diagnostic accuracy and performance of TB detection involving rats. METHODS APOPO (Anti-Persoonsmijnen Ontmijnende Product Ontwikkeling) Center in Morogoro provided data for this study from 2012 to 2019, and 366,441 observations were used to build predictive models using ML techniques, including decision tree, random forest, naïve Bayes, support vector machine, and k-nearest neighbor, by incorporating a variety of variables, such as the diagnostic results from partner health clinics using methods endorsed by the World Health Organization (WHO). RESULTS The support vector machine technique yielded the highest accuracy of 83.39% for prediction compared to other ML techniques used. Furthermore, this study found that the inclusion of variables related to whether the sample contained TB or not increased the performance accuracy of the predictive model. CONCLUSIONS The inclusion of variables related to the diagnostic results of TB samples may improve the detection performance of the trained rats. The study results may be of importance to TB-detection rat trainers and TB decision-makers as the results may prompt them to take action to maintain the usefulness of the technology and increase the TB detection performance of trained rats.
Collapse
Affiliation(s)
- Joan Jonathan
- Department of Informatics and Information Technology, Sokoine University of Agriculture, Morogoro, United Republic of Tanzania
| | - Alcardo Alex Barakabitze
- Department of Informatics and Information Technology, Sokoine University of Agriculture, Morogoro, United Republic of Tanzania
| | - Cynthia D Fast
- APOPO Rodent Project, Sokoine University of Agriculture, Morogoro, United Republic of Tanzania
- Evolutionary Ecology Group, Department of Biology, University of Antwerp, Antwerp, Belgium
- Rutgers Center for Cognitive Science, Piscataway, NJ, United States
| | - Christophe Cox
- APOPO Rodent Project, Sokoine University of Agriculture, Morogoro, United Republic of Tanzania
| |
Collapse
|
7
|
Rao GM, Ramesh D, Sharma V, Sinha A, Hassan MM, Gandomi AH. AttGRU-HMSI: enhancing heart disease diagnosis using hybrid deep learning approach. Sci Rep 2024; 14:7833. [PMID: 38570560 PMCID: PMC10991318 DOI: 10.1038/s41598-024-56931-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Accepted: 03/12/2024] [Indexed: 04/05/2024] Open
Abstract
Heart disease is a major global cause of mortality and a major public health problem for a large number of individuals. A major issue raised by regular clinical data analysis is the recognition of cardiovascular illnesses, including heart attacks and coronary artery disease, even though early identification of heart disease can save many lives. Accurate forecasting and decision assistance may be achieved in an effective manner with machine learning (ML). Big Data, or the vast amounts of data generated by the health sector, may assist models used to make diagnostic choices by revealing hidden information or intricate patterns. This paper uses a hybrid deep learning algorithm to describe a large data analysis and visualization approach for heart disease detection. The proposed approach is intended for use with big data systems, such as Apache Hadoop. An extensive medical data collection is first subjected to an improved k-means clustering (IKC) method to remove outliers, and the remaining class distribution is then balanced using the synthetic minority over-sampling technique (SMOTE). The next step is to forecast the disease using a bio-inspired hybrid mutation-based swarm intelligence (HMSI) with an attention-based gated recurrent unit network (AttGRU) model after recursive feature elimination (RFE) has determined which features are most important. In our implementation, we compare four machine learning algorithms: SAE + ANN (sparse autoencoder + artificial neural network), LR (logistic regression), KNN (K-nearest neighbour), and naïve Bayes. The experiment results indicate that a 95.42% accuracy rate for the hybrid model's suggested heart disease prediction is attained, which effectively outperforms and overcomes the prescribed research gap in mentioned related work.
Collapse
Affiliation(s)
- G Madhukar Rao
- Department of Computer Science and Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, 826004, India
- Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, 500075, India
| | - Dharavath Ramesh
- Department of Computer Science and Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, 826004, India
- Department of Computer Science, University of Economics and Human Sciences, Warsaw, Poland
| | - Vandana Sharma
- Computer Science Department, Christ University, Delhi NCR Campus, Ghaziabad, Delhi NCR, India
| | - Anurag Sinha
- Department of Computer Science, ICFAI Tech School, ICFAI University, Ranchi, Jharkhand, India
| | - Md Mehedi Hassan
- Computer Science and Engineering, Discipline Khulna University, Khulna, 9208, Bangladesh
| | - Amir H Gandomi
- Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, 2007, Australia.
- University Research and Innovation Center (EKIK), Óbuda University, 1034, Budapest, Hungary.
| |
Collapse
|
8
|
Cossio-Gil Y, Pérez-Sádaba FJ, Ribera J, Giménez E, Marte L, Ramos R, Aurin E, Peterlunger M, Steinbrink J, Bottinelli EAM, Nelson N, Seveke L, Garin N, Velasco C. Identifying potential predictable indicators for the management of tertiary hospitals. Int J Health Plann Manage 2024; 39:278-292. [PMID: 37910590 DOI: 10.1002/hpm.3710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 07/28/2023] [Accepted: 09/19/2023] [Indexed: 11/03/2023] Open
Abstract
BACKGROUND The European University Hospitals Alliance (EUHA) recognises the need to move from the classical approach of measuring key performance indicators (KPIs) to an anticipative approach based on predictable indicators to take decisions (Key Decision Indicators, KDIs). It might help managers to anticipate poor results before they occur to prevent or correct them early. OBJECTIVE This paper aims to identify potential KDIs and to prioritize those most relevant for high complexity hospitals. METHODS A narrative review was performed to identify KPIs with the potential to become KDIs. Then, two surveys were conducted with EUHA hospital managers (n = 51) to assess potential KDIs according to their relevance for decision-making (Value) and their availability and effort required to be predicted (Feasibility). Potential KDIs are prioritized for testing as predictable indicators and developing in the short term if they were classified as highly Value and Feasible. RESULTS The narrative review identified 45 potential KDIs out of 153 indicators and 11 were prioritized. Of nine EUHA hospitals, 25 members from seven answered, prioritizing KDIs related to the emergency department (ED), hospitalisation and surgical processes (n = 8), infrastructure and resources (n = 2) and health outcomes and quality (n = 1). The highest scores in this group were for those related to ED. The results were homogeneous among the different hospitals. CONCLUSIONS Potential KDIs related to care processes and hospital patient flow was the most prioritized ones to test as being predictable. KDIs represent a new approach to decision-making, whose potential to be predicted could impact the planning and management of hospital resources and, therefore, healthcare quality.
Collapse
Affiliation(s)
- Yolima Cossio-Gil
- Department of Evaluation and Information Systems, Vall d'Hebron University Hospital, Barcelona, Spain
- Grup de Recerca en Serveis Sanitaris, Vall d'Hebron University Hospital, Barcelona, Spain
- European University Hospitals Alliance, Barcelona, Spain
| | | | - Jaume Ribera
- Center for Research in Healthcare Innovation Management (CRHIM), IESE Business School, Barcelona, Spain
| | - Emmanuel Giménez
- Department of Evaluation and Information Systems, Vall d'Hebron University Hospital, Barcelona, Spain
- Grup de Recerca en Serveis Sanitaris, Vall d'Hebron University Hospital, Barcelona, Spain
- European University Hospitals Alliance, Barcelona, Spain
| | - Luís Marte
- Department of Evaluation and Information Systems, Vall d'Hebron University Hospital, Barcelona, Spain
- Grup de Recerca en Serveis Sanitaris, Vall d'Hebron University Hospital, Barcelona, Spain
- European University Hospitals Alliance, Barcelona, Spain
| | - Rosa Ramos
- Department of Evaluation and Information Systems, Vall d'Hebron University Hospital, Barcelona, Spain
- Grup de Recerca en Serveis Sanitaris, Vall d'Hebron University Hospital, Barcelona, Spain
- European University Hospitals Alliance, Barcelona, Spain
| | - Eva Aurin
- Department of Evaluation and Information Systems, Vall d'Hebron University Hospital, Barcelona, Spain
- European University Hospitals Alliance, Barcelona, Spain
- Department of Innovation and Digital Health, Vall d'Hebron University Hospital, Barcelona, Spain
| | - Michael Peterlunger
- European University Hospitals Alliance, Barcelona, Spain
- Medical University of Vienna and Vienna General Hospital, Vienna, Austria
| | - Jens Steinbrink
- European University Hospitals Alliance, Barcelona, Spain
- Corporate Strategic Development, Charité - Universitätsmedizin, Berlin, Germany
| | | | - Nina Nelson
- European University Hospitals Alliance, Barcelona, Spain
- Karolinska University Hospital, Stockholm, Sweden
| | - Lynn Seveke
- European University Hospitals Alliance, Barcelona, Spain
| | - Noe Garin
- Hospital de la Santa Creu i Sant Pau, Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Cesar Velasco
- Health Evaluation and Quality Agency of Catalonia (AQuAS), Barcelona, Spain
| |
Collapse
|
9
|
Margetta J, Sale A. Distinguishing cardiac catheter ablation energy modalities by applying natural language processing to electronic health records. J Comp Eff Res 2024; 13:e230053. [PMID: 38261335 PMCID: PMC10945417 DOI: 10.57264/cer-2023-0053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 01/03/2024] [Indexed: 01/24/2024] Open
Abstract
Aim: Catheter ablation is used to treat symptomatic atrial fibrillation (AF) and is performed using either cryoballoon (CB) or radiofrequency (RF) ablation. There is limited real world data of CB and RF in the US as healthcare codes are agnostic of energy modality. An alternative method is to analyze patients' electronic health records (EHRs) using Optum's EHR database. Objective: To determine the feasibility of using patients' EHRs with natural language processing (NLP) to distinguish CB versus RF ablation procedures. Data Source: Optum® de-identified EHR dataset, Optum® Cardiac Ablation NLP Table. Methods: This was a retrospective analysis of existing de-identified EHR data. Medical codes were used to create an ablation validation table. Frequency analysis was used to assess ablation procedures and their associated note terms. Two cohorts were created (1) index procedures, (2) multiple procedures. Possible note term combinations included (1) cryoablation (2) radiofrequency (3) ablation, or (4) both. Results: Of the 40,810 validated cardiac ablations, 3777 (9%) index ablation procedures had available and matching NLP note terms. Of these, 22% (n = 844) were classified as ablation, 27% (n = 1016) as cryoablation, 49% (n = 1855) as radiofrequency ablation, and 1.6% (n = 62) as both. In the multiple procedures analysis, 5691 (14%) procedures had matching note terms. 24% (n = 1362) were classified as ablation, 27% as cryoablation, 47% as radiofrequency ablation, and 2% as both. Conclusion: NLP has potential to evaluate the frequency of cardiac ablation by type, however, for this to be a reliable real-world data source, mandatory data entry by providers and standardized electronic health reporting must occur.
Collapse
Affiliation(s)
- Jamie Margetta
- Department of Health Economics & Outcomes Research, Medtronic, Mounds View, MN 55112, USA
| | - Alicia Sale
- Department of Health Economics & Outcomes Research, Medtronic, Mounds View, MN 55112, USA
| |
Collapse
|
10
|
Haley LC, Boyd AK, Hebballi NB, Reynolds EW, Smith KG, Scully PT, Nguyen TL, Bernstam EV, Li LT. Attitudes on Artificial Intelligence use in Pediatric Care From Parents of Hospitalized Children. J Surg Res 2024; 295:158-167. [PMID: 38016269 DOI: 10.1016/j.jss.2023.10.027] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 09/27/2023] [Accepted: 10/27/2023] [Indexed: 11/30/2023]
Abstract
INTRODUCTION Artificial intelligence (AI) may benefit pediatric healthcare, but it also raises ethical and pragmatic questions. Parental support is important for the advancement of AI in pediatric medicine. However, there is little literature describing parental attitudes toward AI in pediatric healthcare, and existing studies do not represent parents of hospitalized children well. METHODS We administered the Attitudes toward Artificial Intelligence in Pediatric Healthcare, a validated survey, to parents of hospitalized children in a single tertiary children's hospital. Surveys were administered by trained study personnel (11/2/2021-5/1/2022). Demographic data were collected. An Attitudes toward Artificial Intelligence in Pediatric Healthcare score, assessing openness toward AI-assisted medicine, was calculated for seven areas of concern. Subgroup analyses were conducted using Mann-Whitney U tests to assess the effect of race, gender, education, insurance, length of stay, and intensive care unit (ICU) admission on AI use. RESULTS We approached 90 parents and conducted 76 surveys for a response rate of 84%. Overall, parents were open to the use of AI in pediatric medicine. Social justice, convenience, privacy, and shared decision-making were important concerns. Parents of children admitted to an ICU expressed the most significantly different attitudes compared to parents of children not admitted to an ICU. CONCLUSIONS Parents were overall supportive of AI-assisted healthcare decision-making. In particular, parents of children admitted to ICU have significantly different attitudes, and further study is needed to characterize these differences. Parents value transparency and disclosure pathways should be developed to support this expectation.
Collapse
Affiliation(s)
- Lauren C Haley
- Department of Pediatric Surgery, McGovern Medical School at the University of Texas Health Science Center at Houston, Houston, Texas
| | - Alexandra K Boyd
- Department of Pediatric Surgery, McGovern Medical School at the University of Texas Health Science Center at Houston, Houston, Texas
| | - Nutan B Hebballi
- Department of Pediatric Surgery, McGovern Medical School at the University of Texas Health Science Center at Houston, Houston, Texas
| | - Eric W Reynolds
- Department of Pediatrics, McGovern Medical School at the University of Texas Health Science Center at Houston, Houston, Texas
| | - Keely G Smith
- Department of Pediatrics, McGovern Medical School at the University of Texas Health Science Center at Houston, Houston, Texas
| | - Peter T Scully
- Department of Pediatrics, McGovern Medical School at the University of Texas Health Science Center at Houston, Houston, Texas
| | - Thao L Nguyen
- Department of Pediatrics, McGovern Medical School at the University of Texas Health Science Center at Houston, Houston, Texas
| | - Elmer V Bernstam
- Department of Pediatric Surgery, McGovern Medical School at the University of Texas Health Science Center at Houston, Houston, Texas; School of Biomedical Informatics, University of Texas at Houston, Houston, Texas
| | - Linda T Li
- Division of Pediatric Surgery, Department of Surgery, Icahn School of Medicine at Mount Sinai, New York, New York.
| |
Collapse
|
11
|
Al Zoubi F, Beaulé PE, Fallavollita P. Factors influencing delays and overtime during surgery: a descriptive analytics for high volume arthroplasty procedures. Front Surg 2024; 10:1242287. [PMID: 38249310 PMCID: PMC10797887 DOI: 10.3389/fsurg.2023.1242287] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 12/06/2023] [Indexed: 01/23/2024] Open
Abstract
The aim of this article is to analyze factors influencing delays and overtime during surgery. We utilized descriptive analytics and divided the factors into three levels. In level one, we analyzed each surgical metrics individually and how it may influence the Surgical Success Rate (SSR) of each operating day. In level two, we compared up to three metrics at once, and in level three, we analyzed four metrics to identify more complex patterns in data including correlations. Within each level, factors were categorized as patient, surgical team, and time specific. Retrospective data on 788 high volume arthroplasty procedures was compiled and analyzed from the 4-joint arthroplasty operating room at our institution. Results demonstrated that surgical team performance had the highest impact on SSR whereas patient metrics had the least influence on SSR. Additionally, beginning the surgical day on time has a prominent effect on the SSR. Finally, the experience of the surgeon had almost no impact on the SSR. In conclusion, we gathered a list of insights that can help influence the re-allocation of resources in daily clinical practice to offset inefficiencies in arthroplasty surgeries.
Collapse
Affiliation(s)
- Farid Al Zoubi
- School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada
| | - Paul E. Beaulé
- Division of Orthopedic Surgery, Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - Pascal Fallavollita
- Interdisciplinary School of Health Sciences, University of Ottawa, Ottawa, ON, Canada
| |
Collapse
|
12
|
Varhol RJ, Norman R, Randall S, Man Ying Lee C, Trevenen L, Boyd JH, Robinson S. Public preference on sharing health data to inform research, health policy and clinical practice in Australia: A stated preference experiment. PLoS One 2023; 18:e0290528. [PMID: 37972118 PMCID: PMC10653479 DOI: 10.1371/journal.pone.0290528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 08/10/2023] [Indexed: 11/19/2023] Open
Abstract
OBJECTIVE To investigate public willingness to share sensitive health information for research, health policy and clinical practice. METHODS A total of 1,003 Australian respondents answered an online, attribute-driven, survey in which participants were asked to accept or reject hypothetical choice sets based on a willingness to share their health data for research and frontline-medical support as part of an integrated health system. The survey consisted of 5 attributes: Stakeholder access for analysis (Analysing group); Type of information collected; Purpose of data collection; Information governance; and Anticipated benefit; the results of which were analysed using logistic regression. RESULTS When asked about their preference for sharing their health data, respondents had no preference between data collection for the purposes of clinical practice, health policy or research, with a slight preference for having government organisations manage, govern and curate the integrated datasets from which the analysis was being conducted. The least preferred option was for personal health records to be integrated with insurance records or for their data collected by privately owned corporate organisations. Individuals preferred their data to be analysed by a public healthcare provider or government staff and expressed a dislike for any private company involvement. CONCLUSIONS The findings from this study suggest that Australian consumers prefer to share their health data when there is government oversight, and have concerns about sharing their anonymised health data for clinical practice, health policy or research purposes unless clarity is provided pertaining to its intended purpose, limitations of use and restrictions to access. Similar findings have been observed in the limited set of existing international studies utilising a stated preference approach. Evident from this study, and supported by national and international research, is that the establishment and preservation of a social license for data linkage in health research will require routine public engagement as a result of continuously evolving technological advancements and fluctuating risk tolerance. Without more work to understand and address stakeholder concerns, consumers risk being reluctant to participate in data-sharing and linkage programmes.
Collapse
Affiliation(s)
- Richard J. Varhol
- School of Population Health, Curtin University, Perth, Western Australia, Australia
| | - Richard Norman
- School of Population Health, Curtin University, Perth, Western Australia, Australia
| | - Sean Randall
- Deakin Health Economics, Institute for Health Transformation, Deakin University, Melbourne, Victoria, Australia
| | - Crystal Man Ying Lee
- School of Population Health, Curtin University, Perth, Western Australia, Australia
| | - Luke Trevenen
- School of Population Health, Curtin University, Perth, Western Australia, Australia
| | - James H. Boyd
- School of Psychology and Public Health, La Trobe University, Melbourne, Australia
| | - Suzanne Robinson
- School of Population Health, Curtin University, Perth, Western Australia, Australia
- Deakin Health Economics, Institute for Health Transformation, Deakin University, Melbourne, Victoria, Australia
| |
Collapse
|
13
|
Li LT, Haley LC, Boyd AK, Bernstam EV. Technical/Algorithm, Stakeholder, and Society (TASS) barriers to the application of artificial intelligence in medicine: A systematic review. J Biomed Inform 2023; 147:104531. [PMID: 37884177 DOI: 10.1016/j.jbi.2023.104531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 09/14/2023] [Accepted: 10/22/2023] [Indexed: 10/28/2023]
Abstract
INTRODUCTION The use of artificial intelligence (AI), particularly machine learning and predictive analytics, has shown great promise in health care. Despite its strong potential, there has been limited use in health care settings. In this systematic review, we aim to determine the main barriers to successful implementation of AI in healthcare and discuss potential ways to overcome these challenges. METHODS We conducted a literature search in PubMed (1/1/2001-1/1/2023). The search was restricted to publications in the English language, and human study subjects. We excluded articles that did not discuss AI, machine learning, predictive analytics, and barriers to the use of these techniques in health care. Using grounded theory methodology, we abstracted concepts to identify major barriers to AI use in medicine. RESULTS We identified a total of 2,382 articles. After reviewing the 306 included papers, we developed 19 major themes, which we categorized into three levels: the Technical/Algorithm, Stakeholder, and Social levels (TASS). These themes included: Lack of Explainability, Need for Validation Protocols, Need for Standards for Interoperability, Need for Reporting Guidelines, Need for Standardization of Performance Metrics, Lack of Plan for Updating Algorithm, Job Loss, Skills Loss, Workflow Challenges, Loss of Patient Autonomy and Consent, Disturbing the Patient-Clinician Relationship, Lack of Trust in AI, Logistical Challenges, Lack of strategic plan, Lack of Cost-effectiveness Analysis and Proof of Efficacy, Privacy, Liability, Bias and Social Justice, and Education. CONCLUSION We identified 19 major barriers to the use of AI in healthcare and categorized them into three levels: the Technical/Algorithm, Stakeholder, and Social levels (TASS). Future studies should expand on barriers in pediatric care and focus on developing clearly defined protocols to overcome these barriers.
Collapse
Affiliation(s)
- Linda T Li
- Department of Surgery, Division of Pediatric Surgery, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, United States; McWilliams School of Biomedical Informatics at UT Health Houston, 7000 Fannin St, Suite 600, Houston, TX 77030, United States.
| | - Lauren C Haley
- McGovern Medical School at the University of Texas Health Science Center at Houston, 6431 Fannin St, Houston, TX 77030, United States.
| | - Alexandra K Boyd
- McGovern Medical School at the University of Texas Health Science Center at Houston, 6431 Fannin St, Houston, TX 77030, United States.
| | - Elmer V Bernstam
- McWilliams School of Biomedical Informatics at UT Health Houston, 7000 Fannin St, Suite 600, Houston, TX 77030, United States; McGovern Medical School at the University of Texas Health Science Center at Houston, 6431 Fannin St, Houston, TX 77030, United States.
| |
Collapse
|
14
|
Kim C, Chen B, Mohandas S, Rehman J, Sherif ZA, Coombs K. The importance of patient-partnered research in addressing long COVID: Takeaways for biomedical research study design from the RECOVER Initiative's Mechanistic Pathways taskforce. eLife 2023; 12:e86043. [PMID: 37737716 PMCID: PMC10516599 DOI: 10.7554/elife.86043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 08/29/2023] [Indexed: 09/23/2023] Open
Abstract
The NIH-funded RECOVER study is collecting clinical data on patients who experience a SARS-CoV-2 infection. As patient representatives of the RECOVER Initiative's Mechanistic Pathways task force, we offer our perspectives on patient motivations for partnering with researchers to obtain results from mechanistic studies. We emphasize the challenges of balancing urgency with scientific rigor. We recognize the importance of such partnerships in addressing post-acute sequelae of SARS-CoV-2 infection (PASC), which includes 'long COVID,' through contrasting objective and subjective narratives. Long COVID's prevalence served as a call to action for patients like us to become actively involved in efforts to understand our condition. Patient-centered and patient-partnered research informs the balance between urgency and robust mechanistic research. Results from collaborating on protocol design, diverse patient inclusion, and awareness of community concerns establish a new precedent in biomedical research study design. With a public health matter as pressing as the long-term complications that can emerge after SARS-CoV-2 infection, considerate and equitable stakeholder involvement is essential to guiding seminal research. Discussions in the RECOVER Mechanistic Pathways task force gave rise to this commentary as well as other review articles on the current scientific understanding of PASC mechanisms.
Collapse
Affiliation(s)
- C Kim
- Department of Population Health, NYU Grossman School of MedicineNew YorkUnited States
| | - Benjamin Chen
- Division of Infectious Diseases, Department of Medicine, Icahn School of Medicine at Mount SinaiNew YorkUnited States
| | - Sindhu Mohandas
- Department of Pediatrics, Division of Infectious Diseases, Children’s Hospital Los Angeles, Keck School of Medicine, University of Southern CaliforniaLos AngelesUnited States
| | - Jalees Rehman
- Department of Biochemistry and Molecular Genetics, University of Illinois, College of MedicineChicagoUnited States
| | - Zaki A Sherif
- Department of Biochemistry & Molecular Biology, Howard University College of MedicineWashingtonUnited States
| | - K Coombs
- Department of Pandemic Equity, Vermont Center for Independent LivingMontpelierUnited States
| |
Collapse
|
15
|
Abu-Salih B, AL-Qurishi M, Alweshah M, AL-Smadi M, Alfayez R, Saadeh H. Healthcare knowledge graph construction: A systematic review of the state-of-the-art, open issues, and opportunities. JOURNAL OF BIG DATA 2023; 10:81. [PMID: 37274445 PMCID: PMC10225120 DOI: 10.1186/s40537-023-00774-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 05/17/2023] [Indexed: 06/06/2023]
Abstract
The incorporation of data analytics in the healthcare industry has made significant progress, driven by the demand for efficient and effective big data analytics solutions. Knowledge graphs (KGs) have proven utility in this arena and are rooted in a number of healthcare applications to furnish better data representation and knowledge inference. However, in conjunction with a lack of a representative KG construction taxonomy, several existing approaches in this designated domain are inadequate and inferior. This paper is the first to provide a comprehensive taxonomy and a bird's eye view of healthcare KG construction. Additionally, a thorough examination of the current state-of-the-art techniques drawn from academic works relevant to various healthcare contexts is carried out. These techniques are critically evaluated in terms of methods used for knowledge extraction, types of the knowledge base and sources, and the incorporated evaluation protocols. Finally, several research findings and existing issues in the literature are reported and discussed, opening horizons for future research in this vibrant area.
Collapse
Affiliation(s)
| | | | | | - Mohammad AL-Smadi
- Jordan University of Science and Technology, Irbid, Jordan
- Qatar University, Doha, Qatar
| | | | | |
Collapse
|
16
|
Kumar P, Sharma SK, Dutot V. Artificial intelligence (AI)-enabled CRM capability in healthcare: The impact on service innovation. INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT 2023. [DOI: 10.1016/j.ijinfomgt.2022.102598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
17
|
Teodorowski P, Rodgers SE, Fleming K, Tahir N, Ahmed S, Frith L. 'To me, it's ones and zeros, but in reality that one is death': A qualitative study exploring researchers' experience of involving and engaging seldom-heard communities in big data research. Health Expect 2023; 26:882-891. [PMID: 36691930 PMCID: PMC10010102 DOI: 10.1111/hex.13713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 12/21/2022] [Accepted: 01/05/2023] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Big data research requires public support. It has been argued that this can be achieved by public involvement and engagement to ensure that public views are at the centre of research projects. Researchers should aim to include diverse communities, including seldom-heard voices, to ensure that a range of voices are heard and that research is meaningful to them. OBJECTIVE We explored how researchers involve and engage seldom-heard communities around big data research. METHODS This is a qualitative study. Researchers who had experience of involving or engaging seldom-heard communities in big data research were recruited. They were based in England (n = 5), Scotland (n = 4), Belgium (n = 2) and Canada (n = 1). Twelve semistructured interviews were conducted on Zoom. All interviews were audio-recorded and transcribed, and we used reflexive thematic analysis to analyse participants' experiences. RESULTS The analysis highlighted the complexity of involving and engaging seldom-heard communities around big data research. Four themes were developed to represent participants' experiences: (1) abstraction and complexity of big data, (2) one size does not fit all, (3) working in partnership and (4) empowering the public contribution. CONCLUSION The study offers researchers a better understanding of how to involve and engage seldom-heard communities in a meaningful way around big data research. There is no one right approach, with involvement and engagement activities required to be project-specific and dependent on the public contributors, researchers' needs, resources and time available. PATIENT AND PUBLIC INVOLVEMENT Two public contributors are authors of the paper and they were involved in the study design, analysis and writing.
Collapse
Affiliation(s)
- Piotr Teodorowski
- Department of Public Health, Policy & Systems, University of Liverpool, Liverpool, UK
| | - Sarah E Rodgers
- Department of Public Health, Policy & Systems, University of Liverpool, Liverpool, UK
| | - Kate Fleming
- National Disease Registration Service, NHS Digital, Liverpool, UK
| | | | | | - Lucy Frith
- Department of Law, University of Manchester, Manchester, UK
| |
Collapse
|
18
|
Soh ZD, Cheng CY. Application of big data in ophthalmology. Taiwan J Ophthalmol 2023; 13:123-132. [PMID: 37484625 PMCID: PMC10361443 DOI: 10.4103/tjo.tjo-d-23-00012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 04/02/2023] [Indexed: 07/25/2023] Open
Abstract
The advents of information technologies have led to the creation of ever-larger datasets. Also known as big data, these large datasets are characterized by its volume, variety, velocity, veracity, and value. More importantly, big data has the potential to expand traditional research capabilities, inform clinical practice based on real-world data, and improve the health system and service delivery. This review first identified the different sources of big data in ophthalmology, including electronic medical records, data registries, research consortia, administrative databases, and biobanks. Then, we provided an in-depth look at how big data analytics have been applied in ophthalmology for disease surveillance, and evaluation on disease associations, detection, management, and prognostication. Finally, we discussed the challenges involved in big data analytics, such as data suitability and quality, data security, and analytical methodologies.
Collapse
Affiliation(s)
- Zhi Da Soh
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Ching-Yu Cheng
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| |
Collapse
|
19
|
Schulte T, Wurz T, Groene O, Bohnet-Joschko S. Big Data Analytics to Reduce Preventable Hospitalizations-Using Real-World Data to Predict Ambulatory Care-Sensitive Conditions. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:4693. [PMID: 36981600 PMCID: PMC10049041 DOI: 10.3390/ijerph20064693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 03/01/2023] [Accepted: 03/04/2023] [Indexed: 06/18/2023]
Abstract
The purpose of this study was to develop a prediction model to identify individuals and populations with a high risk of being hospitalized due to an ambulatory care-sensitive condition who might benefit from preventative actions or tailored treatment options to avoid subsequent hospital admission. A rate of 4.8% of all individuals observed had an ambulatory care-sensitive hospitalization in 2019 and 6389.3 hospital cases per 100,000 individuals could be observed. Based on real-world claims data, the predictive performance was compared between a machine learning model (Random Forest) and a statistical logistic regression model. One result was that both models achieve a generally comparable performance with c-values above 0.75, whereas the Random Forest model reached slightly higher c-values. The prediction models developed in this study reached c-values comparable to existing study results of prediction models for (avoidable) hospitalization from the literature. The prediction models were designed in such a way that they can support integrated care or public and population health interventions with little effort with an additional risk assessment tool in the case of availability of claims data. For the regions analyzed, the logistic regression revealed that switching to a higher age class or to a higher level of long-term care and unit from prior hospitalizations (all-cause and due to an ambulatory care-sensitive condition) increases the odds of having an ambulatory care-sensitive hospitalization in the upcoming year. This is also true for patients with prior diagnoses from the diagnosis groups of maternal disorders related to pregnancy, mental disorders due to alcohol/opioids, alcoholic liver disease and certain diseases of the circulatory system. Further model refinement activities and the integration of additional data, such as behavioral, social or environmental data would improve both model performance and the individual risk scores. The implementation of risk scores identifying populations potentially benefitting from public health and population health activities would be the next step to enable an evaluation of whether ambulatory care-sensitive hospitalizations can be prevented.
Collapse
Affiliation(s)
- Timo Schulte
- Faculty of Management, Economics and Society, Witten/Herdecke University, 58455 Witten, Germany
- Faculty of Health, Witten/Herdecke University, 58455 Witten, Germany
- Department of Business Analytics, Clinics of Maerkischer Kreis, 58515 Luedenscheid, Germany
| | - Tillmann Wurz
- Department of Project and Change Management, University Clinic Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Oliver Groene
- Faculty of Management, Economics and Society, Witten/Herdecke University, 58455 Witten, Germany
- Department of Research & Innovation, OptiMedis AG, 20095 Hamburg, Germany
| | - Sabine Bohnet-Joschko
- Faculty of Management, Economics and Society, Witten/Herdecke University, 58455 Witten, Germany
- Faculty of Health, Witten/Herdecke University, 58455 Witten, Germany
| |
Collapse
|
20
|
Sharma M, Patel RK, Garg A, SanTan R, Acharya UR. Automated detection of schizophrenia using deep learning: a review for the last decade. Physiol Meas 2023; 44. [PMID: 36630717 DOI: 10.1088/1361-6579/acb24d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 01/11/2023] [Indexed: 01/12/2023]
Abstract
Schizophrenia (SZ) is a devastating mental disorder that disrupts higher brain functions like thought, perception, etc., with a profound impact on the individual's life. Deep learning (DL) can detect SZ automatically by learning signal data characteristics hierarchically without the need for feature engineering associated with traditional machine learning. We performed a systematic review of DL models for SZ detection. Various deep models like long short-term memory, convolution neural networks, AlexNet, etc., and composite methods have been published based on electroencephalographic signals, and structural and/or functional magnetic resonance imaging acquired from SZ patients and healthy patients control subjects in diverse public and private datasets. The studies, the study datasets, and model methodologies are reported in detail. In addition, the challenges of DL models for SZ diagnosis and future works are discussed.
Collapse
Affiliation(s)
- Manish Sharma
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad 380026, India
| | - Ruchit Kumar Patel
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad 380026, India
| | - Akshat Garg
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad 380026, India
| | - Ru SanTan
- Department of Cardiology, National Heart Centre Singapore, Singapore 169609, Singapore
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 639798, Singapore.,Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan.,Department of Biomedical Engineering, School of Science and Technology, Singapore 639798, Singapore
| |
Collapse
|
21
|
Kaas-Hansen BS, Gentile S, Caioli A, Andersen SE. Exploratory pharmacovigilance with machine learning in big patient data: A focused scoping review. Basic Clin Pharmacol Toxicol 2023; 132:233-241. [PMID: 36541054 DOI: 10.1111/bcpt.13828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 12/15/2022] [Accepted: 12/15/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND Machine learning can operationalize the rich and complex data in electronic patient records for exploratory pharmacovigilance endeavours. OBJECTIVE The objective of this review is to identify applications of machine learning and big patient data in exploratory pharmacovigilance. METHODS We searched PubMed and Embase and included original articles with an exploratory pharmacovigilance purpose, focusing on medicinal interventions and reporting the use of machine learning in electronic patient records with ≥1000 patients collected after market entry. FINDINGS Of 2557 studies screened, seven were included. Those covered six countries and were published between 2015 and 2021. The most prominent machine learning methods were random forests, logistic regressions, and support vector machines. Two studies used artificial neural networks or naive Bayes classifiers. One study used formal concept analysis for association mining, and another used temporal difference learning. Five studies compared several methods against each other. The numbers of patients in most data sets were in the order of thousands; two studies used what can more reasonably be considered big data with >1 000 000 patients records. CONCLUSION Despite years of great aspirations for combining machine learning and clinical data for exploratory pharmacovigilance, only few studies still seem to deliver somewhat on these expectations.
Collapse
Affiliation(s)
- Benjamin Skov Kaas-Hansen
- Department of Intensive Care, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark.,Section for Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Simona Gentile
- Department of Radiology, Zealand University Hospital, Roskilde, Denmark
| | - Alessandro Caioli
- Department of Infectious Diseases - Hepatology, National Institute of Infectious Diseases Lazzaro Spallanzani, Rome, Italy
| | - Stig Ejdrup Andersen
- Clinical Pharmacology Unit, Zealand University Hospital Roskilde, Roskilde, Denmark
| |
Collapse
|
22
|
Peng M, Southern DA, Ocampo W, Kaufman J, Hogan DB, Conly J, Baylis BW, Stelfox HT, Ho C, Ghali WA. Exploring data reduction strategies in the analysis of continuous pressure imaging technology. BMC Med Res Methodol 2023; 23:56. [PMID: 36859239 PMCID: PMC9976437 DOI: 10.1186/s12874-023-01875-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 02/21/2023] [Indexed: 03/03/2023] Open
Abstract
BACKGROUND Science is becoming increasingly data intensive as digital innovations bring new capacity for continuous data generation and storage. This progress also brings challenges, as many scientific initiatives are challenged by the shear volumes of data produced. Here we present a case study of a data intensive randomized clinical trial assessing the utility of continuous pressure imaging (CPI) for reducing pressure injuries. OBJECTIVE To explore an approach to reducing the amount of CPI data required for analyses to a manageable size without loss of critical information using a nested subset of pressure data. METHODS Data from four enrolled study participants excluded from the analytical phase of the study were used to develop an approach to data reduction. A two-step data strategy was used. First, raw data were sampled at different frequencies (5, 30, 60, 120, and 240 s) to identify optimal measurement frequency. Second, similarity between adjacent frames was evaluated using correlation coefficients to identify position changes of enrolled study participants. Data strategy performance was evaluated through visual inspection using heat maps and time series plots. RESULTS A sampling frequency of every 60 s provided reasonable representation of changes in interface pressure over time. This approach translated to using only 1.7% of the collected data in analyses. In the second step it was found that 160 frames within 24 h represented the pressure states of study participants. In total, only 480 frames from the 72 h of collected data would be needed for analyses without loss of information. Only ~ 0.2% of the raw data collected would be required for assessment of the primary trial outcome. CONCLUSIONS Data reduction is an important component of big data analytics. Our two-step strategy markedly reduced the amount of data required for analyses without loss of information. This data reduction strategy, if validated, could be used in other CPI and other settings where large amounts of both temporal and spatial data must be analysed.
Collapse
Affiliation(s)
- Mingkai Peng
- Libin Cardiovascular Institute of Alberta, University of Calgary, Calgary, AB, Canada
| | - Danielle A Southern
- O'Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada
| | - Wrechelle Ocampo
- W21C Research and Innovation Centre, Cumming School of Medicine, GD01 Teaching Research & Wellness Building, University of Calgary, 3280 Hospital Drive, Calgary, NW, Canada
| | - Jaime Kaufman
- W21C Research and Innovation Centre, Cumming School of Medicine, GD01 Teaching Research & Wellness Building, University of Calgary, 3280 Hospital Drive, Calgary, NW, Canada
| | - David B Hogan
- O'Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada.,W21C Research and Innovation Centre, Cumming School of Medicine, GD01 Teaching Research & Wellness Building, University of Calgary, 3280 Hospital Drive, Calgary, NW, Canada.,Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - John Conly
- O'Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada.,W21C Research and Innovation Centre, Cumming School of Medicine, GD01 Teaching Research & Wellness Building, University of Calgary, 3280 Hospital Drive, Calgary, NW, Canada.,Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Infection Prevention and Control, Alberta Health Services, Calgary, AB, Canada.,Snyder Institute for Chronic Diseases, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Foothills Medical Centre, Special Services Building, Ground Floor, AGW5, Calgary, AB, T2N 2T9, Canada
| | - Barry W Baylis
- O'Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada.,W21C Research and Innovation Centre, Cumming School of Medicine, GD01 Teaching Research & Wellness Building, University of Calgary, 3280 Hospital Drive, Calgary, NW, Canada.,Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Foothills Medical Centre, Special Services Building, Ground Floor, AGW5, Calgary, AB, T2N 2T9, Canada
| | - Henry T Stelfox
- O'Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Critical Care Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Alberta Health Services, Alberta, Canada
| | - Chester Ho
- Department of Medicine, Division of Physical Medicine & Rehabilitation, University of Alberta, Edmonton, AB, Canada
| | - William A Ghali
- O'Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada. .,W21C Research and Innovation Centre, Cumming School of Medicine, GD01 Teaching Research & Wellness Building, University of Calgary, 3280 Hospital Drive, Calgary, NW, Canada. .,Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada. .,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada. .,Division of General Internal Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
| |
Collapse
|
23
|
Riester MR, Zullo AR. Prediction tool Development and Implementation in pharmacy praCTice (PreDICT) proposed guidance. Am J Health Syst Pharm 2023; 80:111-123. [PMID: 36242567 PMCID: PMC10060697 DOI: 10.1093/ajhp/zxac298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Indexed: 01/26/2023] Open
Abstract
PURPOSE Proposed guidance is presented for Prediction tool Development and Implementation in pharmacy praCTice (PreDICT). This guidance aims to assist pharmacists and their collaborators with planning, developing, and implementing custom risk prediction tools for use by pharmacists in their own health systems or practice settings. We aimed to describe general considerations that would be relevant to most prediction tools designed for use in health systems or other pharmacy practice settings. SUMMARY The PreDICT proposed guidance is organized into 3 sequential phases: (1) planning, (2) development and validation, and (3) testing and refining prediction tools for real-world use. Each phase is accompanied by a checklist of considerations designed to be used by pharmacists or their trainees (eg, residents) during the planning or conduct of a prediction tool project. Commentary and a worked example are also provided to highlight some of the most relevant and impactful considerations for each phase. CONCLUSION The proposed guidance for PreDICT is a pharmacist-focused set of checklists for planning, developing, and implementing prediction tools in pharmacy practice. The list of considerations and accompanying commentary can be used as a reference by pharmacists or their trainees before or during the completion of a prediction tool project.
Collapse
Affiliation(s)
- Melissa R Riester
- Department of Health Services, Policy, and Practice, Brown University School of Public Health, Providence, RI, USA
| | - Andrew R Zullo
- Departments of Health Services, Policy, and Practice and Epidemiology, Brown University School of Public Health, Providence, RI
- Department of Pharmacy, Rhode Island Hospital, Providence, RI, USA
| |
Collapse
|
24
|
Garg J, Singh AK, Gupta A. Human capital in knowledge-based firms: Re-creating value post-pandemic. HUMAN SYSTEMS MANAGEMENT 2023. [DOI: 10.3233/hsm-220156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
BACKGROUND/OBJECTIVE: In today’s volatile business environment, the competitive advantages of firms are temporary. The top management does not, and cannot, have all the answers to increasingly complex and rapidly changing problem situations facing their firms. Since the COVID-19 crisis, organizations have been under pressure to improve their knowledge management practices to continue creating value. Knowledge management capabilities are essential for business performance and competitive advantage. In order to ensure continuous value creation, we conducted research to identify various drivers and dimensions that were revitalized in the ongoing KM practices post-pandemic. METHODOLOGY: In this study, 81 research papers published between January 2010 and March 2022, have been examined from a knowledge management, human capital, and value creation perspective, aiming to understand how a firm can continue to create value before, during, and after the pandemic. RESULTS/CONCLUSION: Our review identifies critical factors in knowledge management and value creation and how companies generate value by leveraging KM during the COVID-19 pandemic. As a result of the research, the authors describe their findings in the form of a conceptual framework which deals with the various drivers and the factors within the KM architecture.
Collapse
Affiliation(s)
| | | | - Ashish Gupta
- Indian Institute of Foreign Trade, New Delhi, India
| |
Collapse
|
25
|
Singh M, Tandon U, Mittal A. Modeling users’ and practitioners’ intention for continued usage of the Internet of Medical Devices (IoMD): an empirical investigation. INFORMATION DISCOVERY AND DELIVERY 2023. [DOI: 10.1108/idd-02-2022-0016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Purpose
The purpose of this paper is to identify the antecedents of continued usage intentions in the connected devices ecosystem in health care by analyzing the users' and physicians' expectations in a new ecosystem where one prefers to connect digitally rather than physically.
Design/methodology/approach
This is a unique study in which data was collected from 242 doctors and 215 end-users to gauge the expectations from the connected devices in health care. Further, these responses were hypothesised using UTAUT-2 and ECT theories to analyze general users’ and professional users’ or doctors’ expectations for continued usage in connected devices ecosystem in the health-care ecosystem.
Findings
Performance expectancy, social influence, facilitating conditions and price value emerged as significant predictors of satisfaction in both user groups. But habit and hedonic motivation reflected an insignificant impact on user satisfaction. Surprisingly, effort expectancy emerged as a significant factor for end-user satisfaction, and this became insignificant for professional user satisfaction. Satisfaction was positively related to continued usage for both user groups, and app quality has a positive impact on all the predictors.
Practical implications
To the best of the authors’ knowledge, this is the first comparative study to understand the factors which influence consumer behavior leading to a holistic model and can be imbibed for creating a better customer experience in an era where we are more comfortable connecting digitally rather than physically.
Originality/value
This study has used the Unified Theory of Acceptance and Use of Technology-2 model and expectation confirmation theory to analyze the key factors influencing the intentions for continued usage of devices in the Internet of Medical Devices setup.
Collapse
|
26
|
Gomes MAS, Kovaleski JL, Pagani RN, da Silva VL, Pasquini TCDS. Transforming healthcare with big data analytics: technologies, techniques and prospects. J Med Eng Technol 2023; 47:1-11. [PMID: 35852400 DOI: 10.1080/03091902.2022.2096133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
In different studies in the field of healthcare, big data analytics technology has been shown to be effective in observing the behaviour of data, of which analysed to allow the discovery of relevant insights for strategy and decision making. The objective of this study is to present the results of a systematic review of the literature on big data analytics in healthcare, focussing in technologies, main areas and purposes of adoption. To reach its objective, the study conducts an exploratory research, through a systematic review of the literature, using the Methodi Ordinatio protocol supported by content analysis. The results reveal that the use of tools implies work performance at the clinical and managerial level, improving the cost-benefit ratio and reducing the time factor in the practice of the workforce in health services. Thus, this study hopes to contribute to the technological advancement of computational intelligence applied to healthcare.
Collapse
Affiliation(s)
- Myller Augusto Santos Gomes
- Production Engineering, Federal University of Technology of Paraná, State University of Ponta Grossa, Ponta Grossa, Brazil
| | - João Luiz Kovaleski
- Production Engineering, Federal University of Technology of Paraná, State University of Ponta Grossa, Ponta Grossa, Brazil
| | - Regina Negri Pagani
- Production Engineering, Federal University of Technology of Paraná, State University of Ponta Grossa, Ponta Grossa, Brazil
| | - Vander Luiz da Silva
- Production Engineering, Federal University of Technology of Paraná, State University of Ponta Grossa, Ponta Grossa, Brazil
| | | |
Collapse
|
27
|
Nawaz M, Ahmed J. Cloud-based healthcare framework for real-time anomaly detection and classification of 1-D ECG signals. PLoS One 2022; 17:e0279305. [PMID: 36574391 PMCID: PMC9794080 DOI: 10.1371/journal.pone.0279305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 12/05/2022] [Indexed: 12/28/2022] Open
Abstract
Real-time data collection and pre-processing have enabled the recognition, realization, and prediction of diseases by extracting and analysing the important features of physiological data. In this research, an intelligent end-to-end system for anomaly detection and classification of raw, one-dimensional (1D) electrocardiogram (ECG) signals is given to assess cardiovascular activity automatically. The acquired raw ECG data is pre-processed carefully before storing it in the cloud, and then deeply analyzed for anomaly detection. A deep learning-based auto-encoder(AE) algorithm is applied for the anomaly detection of 1D ECG time-series signals. As a next step, the implemented system identifies it by a multi-label classification algorithm. To improve the classification accuracy and model robustness the improved feature-engineered parameters of the large and diverse datasets have been incorporated. The training has been done using the amazon web service (AWS) machine learning services and cloud-based storage for a unified solution. Multi-class classification of raw ECG signals is challenging due to a large number of possible label combinations and noise susceptibility. To overcome this problem, a performance comparison of a large set of machine algorithms in terms of classification accuracy is presented on an improved feature-engineered dataset. The proposed system reduces the raw signal size up to 95% using wavelet time scattering features to make it less compute-intensive. The results show that among several state-of-the-art techniques, the long short-term memory (LSTM) method has shown 100% classification accuracy, and an F1 score on the three-class test dataset. The ECG signal anomaly detection algorithm shows 98% accuracy using deep LSTM auto-encoders with a reconstructed error threshold of 0.02 in terms of absolute error loss. Our approach provides performance and predictive improvement with an average mean absolute error loss of 0.0072 for normal signals and 0.078 for anomalous signals.
Collapse
Affiliation(s)
- Menaa Nawaz
- Department of electrical engineering, Riphah International University, Islamabad, Pakistan
- * E-mail:
| | - Jameel Ahmed
- Department of electrical engineering, Riphah International University, Islamabad, Pakistan
| |
Collapse
|
28
|
Khan S, Khan HU, Nazir S. Systematic analysis of healthcare big data analytics for efficient care and disease diagnosing. Sci Rep 2022; 12:22377. [PMID: 36572709 PMCID: PMC9792582 DOI: 10.1038/s41598-022-26090-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 12/09/2022] [Indexed: 12/27/2022] Open
Abstract
Big data has revolutionized the world by providing tremendous opportunities for a variety of applications. It contains a gigantic amount of data, especially a plethora of data types that has been significantly useful in diverse research domains. In healthcare domain, the researchers use computational devices to extract enriched relevant information from this data and develop smart applications to solve real-life problems in a timely fashion. Electronic health (eHealth) and mobile health (mHealth) facilities alongwith the availability of new computational models have enabled the doctors and researchers to extract relevant information and visualize the healthcare big data in a new spectrum. Digital transformation of healthcare systems by using of information system, medical technology, handheld and smart wearable devices has posed many challenges to researchers and caretakers in the form of storage, minimizing treatment cost, and processing time (to extract enriched information, and minimize error rates to make optimum decisions). In this research work, the existing literature is analysed and assessed, to identify gaps that result in affecting the overall performance of the available healthcare applications. Also, it aims to suggest enhanced solutions to address these gaps. In this comprehensive systematic research work, the existing literature reported during 2011 to 2021, is thoroughly analysed for identifying the efforts made to facilitate the doctors and practitioners for diagnosing diseases using healthcare big data analytics. A set of rresearch questions are formulated to analyse the relevant articles for identifying the key features and optimum management solutions, and laterally use these analyses to achieve effective outcomes. The results of this systematic mapping conclude that despite of hard efforts made in the domains of healthcare big data analytics, the newer hybrid machine learning based systems and cloud computing-based models should be adapted to reduce treatment cost, simulation time and achieve improved quality of care. This systematic mapping will also result in enhancing the capabilities of doctors, practitioners, researchers, and policymakers to use this study as evidence for future research.
Collapse
Affiliation(s)
- Sulaiman Khan
- Department of Accounting and Information Systems, College of Business and Economics, Qatar University, Doha, Qatar
| | - Habib Ullah Khan
- Department of Accounting and Information Systems, College of Business and Economics, Qatar University, Doha, Qatar
| | - Shah Nazir
- Department of Computer Science, University of Swabi, Swabi, Pakistan
| |
Collapse
|
29
|
Santillan DA, Davis HA, Faro EZ, Knosp BM, Santillan MK. Need for Improved Collection and Harmonization of Rural Maternal Healthcare Data. Clin Obstet Gynecol 2022; 65:856-867. [PMID: 36260014 PMCID: PMC9586468 DOI: 10.1097/grf.0000000000000752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Representation in data sets is critical to improving healthcare for the largest possible number of people. Unfortunately, pregnancy is a very understudied period of time. Further, the gap in available data is wide between pregnancies in urban areas versus rural areas. There are many limitations in the current data that is available. Herein, we review these limitations and strengths of available data sources. In addition, we propose a new mechanism to enhance the granularity, depth, and speed with which data is made available regarding rural pregnancy.
Collapse
Affiliation(s)
- Donna A. Santillan
- Department of Obstetrics & Gynecology, University of Iowa Hospitals & Clinics
| | - Heather A. Davis
- Institute for Clinical and Translational Science, University of Iowa
- Carver College of Medicine, University of Iowa
| | - Elissa Z. Faro
- Department of Internal Medicine, University of Iowa Hospitals & Clinics
| | - Boyd M. Knosp
- Institute for Clinical and Translational Science, University of Iowa
- Carver College of Medicine, University of Iowa
| | - Mark K. Santillan
- Department of Obstetrics & Gynecology, University of Iowa Hospitals & Clinics
- Institute for Clinical and Translational Science, University of Iowa
| |
Collapse
|
30
|
The natural language processing of radiology requests and reports of chest imaging: Comparing five transformer models’ multilabel classification and a proof-of-concept study. Health Informatics J 2022; 28:14604582221131198. [DOI: 10.1177/14604582221131198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Background Radiology requests and reports contain valuable information about diagnostic findings and indications, and transformer-based language models are promising for more accurate text classification. Methods In a retrospective study, 2256 radiologist-annotated radiology requests (8 classes) and reports (10 classes) were divided into training and testing datasets (90% and 10%, respectively) and used to train 32 models. Performance metrics were compared by model type (LSTM, Bertje, RobBERT, BERT-clinical, BERT-multilingual, BERT-base), text length, data prevalence, and training strategy. The best models were used to predict the remaining 40,873 cases’ categories of the datasets of requests and reports. Results The RobBERT model performed the best after 4000 training iterations, resulting in AUC values ranging from 0.808 [95% CI (0.757–0.859)] to 0.976 [95% CI (0.956–0.996)] for the requests and 0.746 [95% CI (0.689–0.802)] to 1.0 [95% CI (1.0–1.0)] for the reports. The AUC for the classification of normal reports was 0.95 [95% CI (0.922–0.979)]. The predicted data demonstrated variability of both diagnostic yield for various request classes and request patterns related to COVID-19 hospital admission data. Conclusion Transformer-based natural language processing is feasible for the multilabel classification of chest imaging request and report items. Diagnostic yield varies with the information in the requests.
Collapse
|
31
|
Venkatraman S, Sundarraj RP, Seethamraju R. Exploring health-analytics adoption in indian private healthcare organizations: An institutional-theoretic perspective. INFORMATION AND ORGANIZATION 2022. [DOI: 10.1016/j.infoandorg.2022.100430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
|
32
|
Piovani D, Bonovas S. Real World-Big Data Analytics in Healthcare. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph191811677. [PMID: 36141962 PMCID: PMC9517048 DOI: 10.3390/ijerph191811677] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 09/15/2022] [Indexed: 06/01/2023]
Abstract
The term Big Data is used to describe extremely large datasets that are complex, multi-dimensional, unstructured, and heterogeneous and that are accumulating rapidly and may be analyzed with appropriate informatic and statistical methodologies to reveal patterns, trends, and associations [...].
Collapse
Affiliation(s)
- Daniele Piovani
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20090 Milan, Italy
- IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy
| | - Stefanos Bonovas
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20090 Milan, Italy
- IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy
| |
Collapse
|
33
|
Augustine CA, Keikhosrokiani P. A Hospital Information Management System With Habit-Change Features and Medial Analytical Support for Decision Making. INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGIES AND SYSTEMS APPROACH 2022. [DOI: 10.4018/ijitsa.307019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
A hospital information management system (Doctive) with habit-change features and medial analytical support for decision making is developed in this study to reduce the risks of heart diseases. Doctive is targeted for hospital authorities to monitor patients’ habits and to prescribe medication and advice accordingly. Furthermore, this system provides emergency assistance for patients based on their current location. The proposed system would be beneficial for monitoring and organizing patients’ information to ease data entry, data management, data access, data retrieval and finally decision making. Doctive is tested and evaluated by 41 people who are either medical experts or professionals in the field of data analytics and visualization. The results indicate a high acceptance rate towards using Doctive system in hospitals and very good usability of the system. Doctive can be useful for healthcare providers and developers to track users’ habits for reducing the risk of heart disease. In the future.
Collapse
|
34
|
Pyrrho M, Cambraia L, de Vasconcelos VF. Privacy and Health Practices in the Digital Age. THE AMERICAN JOURNAL OF BIOETHICS : AJOB 2022; 22:50-59. [PMID: 35254963 DOI: 10.1080/15265161.2022.2040648] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Increasing privacy concerns are arising from expanding use of aggregated personal information in health practices. Conversely, in light of the promising benefits of data driven healthcare, privacy is being frequently dismissed as outdated, costly and ultimately egotistical. This paper aims to review the theoretical framework on privacy in order to overcome the often simplistic debate between the primacy of individual or collective interests. As a result, it is argued that although privacy can be understood as freedom of personal choice in matters of sharing intimacy, it is foundational to both community belonging and to social and political organizations at large. Ethical decisions on the use of data analytics technologies in health practices should also take into account the social effects of violating privacy.
Collapse
|
35
|
Martinez-Sanchez L, Cobbaert CM, Noordam R, Brouwer N, Blanco-Grau A, Villena-Ortiz Y, Thelen M, Ferrer-Costa R, Casis E, Rodríguez-Frias F, den Elzen WPJ. Indirect determination of biochemistry reference intervals using outpatient data. PLoS One 2022; 17:e0268522. [PMID: 35588100 PMCID: PMC9119462 DOI: 10.1371/journal.pone.0268522] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 05/02/2022] [Indexed: 12/22/2022] Open
Abstract
The aim of this study was to determine reference intervals in an outpatient population from Vall d'Hebron laboratory using an indirect approach previously described in a Dutch population (NUMBER project). We used anonymized test results from individuals visiting general practitioners and analysed during 2018. Analytical quality was assured by EQA performance, daily average monitoring and by assessing longitudinal accuracy between 2018 and 2020 (using trueness verifiers from Dutch EQA). Per test, outliers by biochemically related tests were excluded, data were transformed to a normal distribution (if necessary) and means and standard deviations were calculated, stratified by age and sex. In addition, the reference limit estimator method was also used to calculate reference intervals using the same dataset. Finally, for standardized tests reference intervals obtained were compared with the published NUMBER results. Reference intervals were calculated using data from 509,408 clinical requests. For biochemical tests following a normal distribution, similar reference intervals were found between Vall d'Hebron and the Dutch study. For creatinine and urea, reference intervals increased with age in both populations. The upper limits of Gamma-glutamyl transferase were markedly higher in the Dutch study compared to Vall d'Hebron results. Creatine kinase and uric acid reference intervals were higher in both populations compared to conventional reference intervals. Medical test results following a normal distribution showed comparable and consistent reference intervals between studies. Therefore a simple indirect method is a feasible and cost-efficient approach for calculating reference intervals. Yet, for generating standardized calculated reference intervals that are traceable to higher order materials and methods, efforts should also focus on test standardization and bias assessment using commutable trueness verifiers.
Collapse
Affiliation(s)
- Luisa Martinez-Sanchez
- Clinical Laboratories, Biochemistry Department, Vall d’Hebron University Hospital, Barcelona, Spain
- Department of Clinical Chemistry and Laboratory Medicine, Leiden University Medical Centre, Leiden, The Netherlands
- Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Christa M. Cobbaert
- Department of Clinical Chemistry and Laboratory Medicine, Leiden University Medical Centre, Leiden, The Netherlands
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
| | - Nannette Brouwer
- Diagnost-IQ, Expert Centre for Clinical Chemistry, Purmerend, The Netherlands
| | - Albert Blanco-Grau
- Clinical Laboratories, Biochemistry Department, Vall d’Hebron University Hospital, Barcelona, Spain
| | - Yolanda Villena-Ortiz
- Clinical Laboratories, Biochemistry Department, Vall d’Hebron University Hospital, Barcelona, Spain
- Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Marc Thelen
- Laboratory for Clinical Chemistry and Hematology, Amphia, Breda, The Netherlands
- Stichting Kwaliteitsbewaking Medische Laboratoriumdiagnostiek, Nijmegen, The Netherlands
- Department of Laboratory Medicine, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Roser Ferrer-Costa
- Clinical Laboratories, Biochemistry Department, Vall d’Hebron University Hospital, Barcelona, Spain
| | - Ernesto Casis
- Clinical Laboratories, Biochemistry Department, Vall d’Hebron University Hospital, Barcelona, Spain
| | - Francisco Rodríguez-Frias
- Clinical Laboratories, Biochemistry Department, Vall d’Hebron University Hospital, Barcelona, Spain
- Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Wendy P. J. den Elzen
- Clinical Laboratories, Biochemistry Department, Vall d’Hebron University Hospital, Barcelona, Spain
- Department of Clinical Chemistry and Laboratory Medicine, Leiden University Medical Centre, Leiden, The Netherlands
- Atalmedial Diagnostics Centre, Amsterdam, The Netherlands
- Department of Clinical Chemistry, Amsterdam Public Health research institute, Amsterdam UMC, Amsterdam, The Netherlands
| |
Collapse
|
36
|
Heins MJ, de Ligt KM, Verloop J, Siesling S, Korevaar JC. Opportunities and obstacles in linking large health care registries: the primary secondary cancer care registry - breast cancer. BMC Med Res Methodol 2022; 22:124. [PMID: 35477392 PMCID: PMC9044735 DOI: 10.1186/s12874-022-01601-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 03/30/2022] [Indexed: 11/10/2022] Open
Abstract
Background The growing volume of health data provides new opportunities for medical research. By using existing registries, large populations can be studied over a long period of time. Patient-level linkage of registries leads to even more detailed and extended information per patient, but brings challenges regarding responsibilities, privacy and security, and quality of data linkage. In this paper we describe how we dealt with these challenges when creating the Primary Secondary Cancer Care Registry (PSCCR)- Breast Cancer. Methods The PSCCR – Breast Cancer was created by linking two existing registries containing data on 1) diagnosis, tumour and treatment characteristics of all Dutch breast cancer patients (NCR), and 2) consultations and diagnoses from primary care electronic health records of about 10% of Dutch GP practices (Nivel-PCD). The existing registry governance structures and privacy regulations were incorporated in those of the new registry. Privacy and security risks were reassessed. Data were restricted to females and linked using postal code and date of birth. The breast cancer diagnosis was verified in both registries and for a subsample of 44 patients with the GP as well. Results A collaboration agreement was signed in which the organisations retained data responsibility and accountability for ‘their’ registry. A Trusted Third Party performed the record linkage. Ten percent of the patients with breast cancer could be linked to the primary care registry, as was expected based on the coverage of Nivel-PCD, and finally 7 % could be included. The breast cancer diagnosis was verified by the GP in 42 of the 44 patients. Conclusions We developed and validated a procedure for patient-level linkage of health data registries without a unique identifier, while preserving the integrity and privacy of the original registries. The method described may help researchers wishing to link existing health data registries.
Collapse
Affiliation(s)
- Marianne J Heins
- Nivel, Netherlands Institute of Health Services Research, P.O Box 1568, 3500 BN, Utrecht, Netherlands.
| | - Kelly M de Ligt
- Department of Research and Development, Netherlands Comprehensive Cancer Organisation, Utrecht, The Netherlands.,Department of Health Technology and Services Research, Technical Medical Centre, University of Twente, Enschede, The Netherlands
| | - Janneke Verloop
- Department of Research and Development, Netherlands Comprehensive Cancer Organisation, Utrecht, The Netherlands
| | - Sabine Siesling
- Department of Research and Development, Netherlands Comprehensive Cancer Organisation, Utrecht, The Netherlands.,Department of Health Technology and Services Research, Technical Medical Centre, University of Twente, Enschede, The Netherlands
| | - Joke C Korevaar
- Nivel, Netherlands Institute of Health Services Research, P.O Box 1568, 3500 BN, Utrecht, Netherlands
| |
Collapse
|
37
|
Saeed H, Malik H, Bashir U, Ahmad A, Riaz S, Ilyas M, Bukhari WA, Khan MIA. Blockchain technology in healthcare: A systematic review. PLoS One 2022; 17:e0266462. [PMID: 35404955 PMCID: PMC9000089 DOI: 10.1371/journal.pone.0266462] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 03/21/2022] [Indexed: 11/18/2022] Open
Abstract
Blockchain technology (BCT) has emerged in the last decade and added a lot of interest in the healthcare sector. The purpose of this systematic literature review (SLR) is to explore the potential paradigm shift in healthcare utilizing BCT. The study is compiled by reviewing research articles published in nine well-reputed venues such as IEEE Xplore, ACM Digital Library, Springs Link, Scopus, Taylor & Francis, Science Direct, PsycINFO, Ovid Medline, and MDPI between January 2016 to August 2021. A total of 1,192 research studies were identified out of which 51 articles were selected based on inclusion criteria for this SLR that presents the modern information on the recent implications and gaps in the use of BCT for enhancing the healthcare procedures. According to the outcomes, BCT is being applied to design the novel and advanced interventions to enrich the current protocol of managing, distributing, and processing clinical records and personal medical information. BCT is enduring the conceptual development in the healthcare domain, where it has summed up the substantial elements through better and enhanced efficiency, technological innovation, access control, data privacy, and security. A framework is developed to address the probable field where future researchers can add considerable value, such as data protection, system architecture, and regulatory compliance. Finally, this SLR concludes that the upcoming research can support the pervasive implementation of BCT to address the critical dilemmas related to health diagnostics, enhancing the patient healthcare process in remote monitoring or emergencies, data integrity, and avoiding fraud.
Collapse
Affiliation(s)
- Huma Saeed
- Department of Computer Science, National College of Business Administration & Economics Lahore, Multan, Pakistan
| | - Hassaan Malik
- Department of Computer Science, National College of Business Administration & Economics Lahore, Multan, Pakistan
- Department of Computer Science, University of Management and Technology, Lahore, Pakistan
- * E-mail:
| | - Umair Bashir
- Department of Computer Science, National College of Business Administration & Economics Lahore, Multan, Pakistan
| | - Aiesha Ahmad
- Department of Computer Science, National College of Business Administration & Economics Lahore, Multan, Pakistan
| | - Shafia Riaz
- Department of Computer Science, National College of Business Administration & Economics Lahore, Multan, Pakistan
| | - Maheen Ilyas
- Department of Computer Science, National College of Business Administration & Economics Lahore, Multan, Pakistan
| | - Wajahat Anwaar Bukhari
- Department of Computer Science, National College of Business Administration & Economics Lahore, Multan, Pakistan
| | - Muhammad Imran Ali Khan
- Department of Computer Science, National College of Business Administration & Economics Lahore, Multan, Pakistan
| |
Collapse
|
38
|
How can Big Data Analytics Support People-Centred and Integrated Health Services: A Scoping Review. Int J Integr Care 2022; 22:23. [PMID: 35756337 PMCID: PMC9205381 DOI: 10.5334/ijic.5543] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 06/08/2022] [Indexed: 01/16/2023] Open
Abstract
Introduction: Health systems in high-income countries face a variety of challenges calling for a systemic approach to improve quality and efficiency. Putting people in the centre is the main idea of the WHO model of people-centred and integrated health services. Integrating health services is fuelled by an integration of health data with great potentials for decision support based on big data analytics. The research question of this paper is “How can big data analytics support people-centred and integrated health services?” Methods: A scoping review following the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses – Scoping Review (PRISMA-ScR) statement was conducted to gather information on how big data analytics can support people-centred and integrated health services. The results were summarized in a role model of a people-centred and integrated health services platform illustrating which data sources might be integrated and which types of analytics might be applied to support the strategies of the people-centred and integrated health services framework to become more integrated across the continuum of care. Additional rapid literature reviews were conducted to generate frequency distributions of the most often used data types and analytical methods in the medical literature. Finally, the main challenges connected with big data analytics were worked out based on a content analysis of the results from the scoping literature review. Results: Based on the results from the rapid literature reviews the most often used data sources for big data analytics (BDA) in healthcare were biomarkers (39.3%) and medical images (30.9%). The most often used analytical models were support vector machines (27.3%) and neural networks (20.4%). The people-centred and integrated health services framework defines different strategic interventions for health services to become more integrated. To support all aspects of these interventions a comparably integrated platform of health-related data would be needed, so that a role model labelled as people-centred health platform was developed. Based on integrated data the results of the scoping review (n = 72) indicate, that big data analytics could for example support the strategic intervention of tailoring personalized health plans (43.1%), e.g. by predicting individual risk factors for different therapy options. Also BDA might enhance clinical decision support tools (31.9%), e.g. by calculating risk factors for disease uptake or progression. BDA might also assist in designing population-based services (26.4% by clustering comparable individuals in manageable risk groups e.g. mentored by specifically trained, non-medical professionals. The main challenges of big data analytics in healthcare were categorized in regulatory, (information-) technological, methodological, and cultural issues, whereas methodological challenges were mentioned most often (55.0%), followed by regulatory challenges (43.7%). Discussion: The BDA applications presented in this literature review are based on findings which have already been published. For some important components of the framework on people-centred care like enhancing the role of community care or establishing intersectoral partnerships between health and social care institutions only few examples of enabling big data analytical tools were found in the literature. Quite the opposite does this mean that these strategies have less potential value, but rather that the source systems in these fields need to be further developed to be suitable for big data analytics. Conclusions: Big data analytics can support people-centred and integrated health services e.g. by patient similarity stratifications or predictions of individual risk factors. But BDA fails to unfold its full potential until data source systems are still disconnected and actions towards a comprehensive and people-centred health-related data platform are politically insufficiently incentivized. This work highlighted the potential of big data analysis in the context of the model of people-centred and integrated health services, whereby the role model of the person-centered health platform can be used as a blueprint to support strategies to improve person-centered health care. Likely because health data is extremely sensitive and complex, there are only few practical examples of platforms to some extent already capable of merging and processing people-centred big data, but the integration of health data can be expected to further proceed so that analytical opportunities might also become reality in the near future.
Collapse
|
39
|
Tang PP, Tam IL, Jia Y, Leung SW. Big Data Reality Check (BDRC) for public health: to what extent the environmental health and health services research did meet the 'V' criteria for big data? A study protocol. BMJ Open 2022; 12:e053447. [PMID: 35318232 PMCID: PMC8943752 DOI: 10.1136/bmjopen-2021-053447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
INTRODUCTION Big data technologies have been talked up in the fields of science and medicine. The V-criteria (volume, variety, velocity and veracity, etc) for defining big data have been well-known and even quoted in most research articles; however, big data research into public health is often misrepresented due to certain common misconceptions. Such misrepresentations and misconceptions would mislead study designs, research findings and healthcare decision-making. This study aims to identify the V-eligibility of big data studies and their technologies applied to environmental health and health services research that explicitly claim to be big data studies. METHODS AND ANALYSIS Our protocol follows Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P). Scoping review and/or systematic review will be conducted. The results will be reported using PRISMA for Scoping Reviews (PRISMA-ScR), or PRISMA 2020 and Synthesis Without Meta-analysis guideline. Web of Science, PubMed, Medline and ProQuest Central will be searched for the articles from the database inception to 2021. Two reviewers will independently select eligible studies and extract specified data. The numeric data will be analysed with R statistical software. The text data will be analysed with NVivo wherever applicable. ETHICS AND DISSEMINATION This study will review the literature of big data research related to both environmental health and health services. Ethics approval is not required as all data are publicly available and involves confidential personal data. We will disseminate our findings in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER CRD42021202306.
Collapse
Affiliation(s)
- Pui Pui Tang
- State Key Laboratory of Quality Research in Chinese Medicine, University of Macau Institute of Chinese Medical Science, Macau, China
| | - I Lam Tam
- State Key Laboratory of Quality Research in Chinese Medicine, University of Macau Institute of Chinese Medical Science, Macau, China
| | - Yongliang Jia
- BGI College & Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China
- Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Siu-Wai Leung
- Edinburgh Bayes Centre for AI Research in Shenzhen, College of Science and Engineering, University of Edinburgh, Scotland, UK
- Center for Machine Learning and Intelligent Applications, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, People's Republic of China
| |
Collapse
|
40
|
A Cluster-based Stratified Hybrid Decision Support Model under Uncertainty: Sustainable Healthcare Landfill Location Selection. APPL INTELL 2022; 52:13614-13633. [PMID: 35280110 PMCID: PMC8898660 DOI: 10.1007/s10489-022-03335-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/02/2022] [Indexed: 12/23/2022]
Abstract
Nowadays, healthcare waste management has become one of the significant environmental, health, and social problems. Due to population and urbanization growth and an increase in healthcare waste disposals according to the growing number of diseases and pandemics like COVID-19, disposal of healthcare waste has become a critical issue. Authorities in big cities require reliable decision support systems to empower them to make strategic decisions to provide safe disposal methods with a prospective vision. Since inappropriate healthcare waste management systems would definitely bring up dangerous environmental, social, health, and economic issues for every city. Therefore, this paper attempts to address the landfill location selection problem for healthcare waste using a novel decision support system. Novel decision support model integrates K-means algorithms with Stratified Best-Worst Method (SBWM) and a novel hybrid MARCOS-CoCoSo under grey interval numbers. The proposed decision support system considers waste generate rate in medical centers, future unforeseen but potential events, and uncertainty in experts’ opinion to optimally locate required landfills for safe and economical disposal of dangerous healthcare waste. To investigate the feasibility and applicability of the proposed methodology, a real case study is performed for Mazandaran province in Iran. Our proposed methodology could efficiently deal with 79 medical centers within 4 clusters addressing 9 criteria to prioritize candidate locations. Moreover, the sensitivity analysis of weight coefficients is carried out to evaluate the results. Finally, the efficiency of the methodology is compared with several well-known methods and its high efficiency is demonstrated. Results recommend adherence to local rules and regulations, and future expansion potential as the top two criteria with importance values of 0.173 and 0.164, respectively. Later, best location alternatives are determined for each cluster of medical centers.
Collapse
|
41
|
Ittoop SM, Jaccard N, Lanouette G, Kahook MY. The Role of Artificial Intelligence in the Diagnosis and Management of Glaucoma. J Glaucoma 2022; 31:137-146. [PMID: 34930873 DOI: 10.1097/ijg.0000000000001972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 12/10/2021] [Indexed: 11/26/2022]
Abstract
Glaucomatous optic neuropathy is the leading cause of irreversible blindness worldwide. Diagnosis and monitoring of disease involves integrating information from the clinical examination with subjective data from visual field testing and objective biometric data that includes pachymetry, corneal hysteresis, and optic nerve and retinal imaging. This intricate process is further complicated by the lack of clear definitions for the presence and progression of glaucomatous optic neuropathy, which makes it vulnerable to clinician interpretation error. Artificial intelligence (AI) and AI-enabled workflows have been proposed as a plausible solution. Applications derived from this field of computer science can improve the quality and robustness of insights obtained from clinical data that can enhance the clinician's approach to patient care. This review clarifies key terms and concepts used in AI literature, discusses the current advances of AI in glaucoma, elucidates the clinical advantages and challenges to implementing this technology, and highlights potential future applications.
Collapse
Affiliation(s)
- Sabita M Ittoop
- The George Washington University Medical Faculty Associates, Washington, DC
| | | | | | - Malik Y Kahook
- Sue Anschutz-Rodgers Eye Center, The University of Colorado School of Medicine, Aurora, CO
| |
Collapse
|
42
|
Zheng MY, Lui H, Patino G, Mmonu N, Cohen AJ, Breyer BN. Morbidity and Mortality Caused by Noncompliance With California Hospital Licensure: Immediate Jeopardies in California Hospitals, 2007-2017. J Patient Saf 2022; 18:e401-e406. [PMID: 35188929 DOI: 10.1097/pts.0000000000000822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE The California Department of Public Health investigates compliance with hospital licensure and issues an administrative penalty when there is an immediate jeopardy. Immediate jeopardies are situations in which a hospital's noncompliance of licensure requirements causes serious injury or death to patient. In this study, we critically examine immediate jeopardies between 2007 and 2017 in California. METHODS All immediate jeopardies reported between 2007 and 2017 were abstracted for hospital, location, date, details of noncompliance, and patient's health outcome. RESULTS Of 385 unique immediate jeopardies, 141 (36.6%) caused mortality, 120 (31.2%) caused morbidity, 96 (24.9%) led to a second surgery, 9 (2.3%) caused emotional trauma without physical trauma, and 19 (4.9%) were caught before patients were harmed. Immediate jeopardy categories included the following: surgical (34.2%), medication (18.9%), monitoring (14.2%), falls (7.8%), equipment (5.4%), procedural (5.4%), resuscitation (4.4%), suicide (3.9%), MD/RN miscommunication (3.4%), and abuse (2.3%). CONCLUSIONS Noncompliance to hospital licensure causes significant morbidity and mortality. Statewide hospital licensure policies should focus on enacting standardized reporting requirements of immediate jeopardies into an Internet-based form that public health officials can regularly analyze to improve hospital safety.
Collapse
Affiliation(s)
- Micha Y Zheng
- From the Department of Urology, University of California, San Francisco, San Francisco
| | - Hansen Lui
- Department of Urology, University of California, Davis, Sacramento, California
| | - German Patino
- Department of Urology, Hospital Universitario San Ignacio, Bogota, Colombia
| | | | - Andrew J Cohen
- The Brady Urological Institute at JHBMC, Baltimore, Maryland
| | - Benjamin N Breyer
- Department of Urology, Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California
| |
Collapse
|
43
|
Affiliation(s)
- Jeannie LeDoux
- Jeannie LeDoux, MBA, BSN, RN, CCM, CPHQ, CTT+ , is a commissioner and former chair of the Commission for Case Manager Certification, the first and largest nationally accredited organization that certifies more than 50,000 professional case managers and disability management specialists with its CCM and CDMS credentials. She is also a Senior Clinical Educator at MCG, part of Hearst Health
- Anne Mercer, CIA, CFE, CFSA , is public member of the Commission for Case Manager Certification. She is Director of Global Standards & Guidance at the Institute of Internal Auditors. She is a National Association of Corporate Directors Governance Fellow and previously served on the Global Board of the Institute of Internal Auditors
| | - Anne Mercer
- Jeannie LeDoux, MBA, BSN, RN, CCM, CPHQ, CTT+ , is a commissioner and former chair of the Commission for Case Manager Certification, the first and largest nationally accredited organization that certifies more than 50,000 professional case managers and disability management specialists with its CCM and CDMS credentials. She is also a Senior Clinical Educator at MCG, part of Hearst Health
- Anne Mercer, CIA, CFE, CFSA , is public member of the Commission for Case Manager Certification. She is Director of Global Standards & Guidance at the Institute of Internal Auditors. She is a National Association of Corporate Directors Governance Fellow and previously served on the Global Board of the Institute of Internal Auditors
| |
Collapse
|
44
|
Kumar P, Chakraborty S. Green service production and environmental performance in healthcare emergencies: role of big-data management and green HRM practices. INTERNATIONAL JOURNAL OF LOGISTICS MANAGEMENT 2022. [DOI: 10.1108/ijlm-02-2021-0075] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Purpose
This study aims to examine the impact of big data management on green service production (GSP) and environmental performance (ENPr) while considering green HRM practices (GHRM) in healthcare emergencies.
Design/methodology/approach
The authors collected primary data from major healthcare organizations in India by surveying healthcare professionals. The data analysis through structural equation modelling (PLS-SEM) reveals several significant relationships to extricate the underlying dynamics.
Findings
Grounded in the theories of service production and natural resource-based view (NRBV), this study conceptualizes GSP with its three dimensions of green procurement (GP), green service design (GSD) and green service practices (GSPr). The study conducted in India's healthcare sector with a sample size limited to healthcare professionals serving in COVID-19 identifies the positive and significant impact of big data management on GSP and ENPr that organizations seek to deploy in such emergencies. The findings of the study explain the moderating effects of GHRM on GSP-ENPr relationships.
Research limitations/implications
The study was conducted in the healthcare sector in India, and its sample size was limited to healthcare professionals serving in COVID-19. The practical ramifications for healthcare administrators and policymakers are suggested, and future avenues of research are discussed.
Originality/value
This paper develops a holistic model of big data analytics, GP, GSD, GSPr, GHRM and ENPr. This study is a first step in investigating how big data management contributes to ENPr in an emergency and establishing the facets of GSP as a missing link in this relationship, which is currently void in the literature. This study contributes to the theory and fills the knowledge gap in this area.
Collapse
|
45
|
Rabbi F, Dabbagh SR, Angin P, Yetisen AK, Tasoglu S. Deep Learning-Enabled Technologies for Bioimage Analysis. MICROMACHINES 2022; 13:mi13020260. [PMID: 35208385 PMCID: PMC8880650 DOI: 10.3390/mi13020260] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 01/31/2022] [Accepted: 02/03/2022] [Indexed: 02/05/2023]
Abstract
Deep learning (DL) is a subfield of machine learning (ML), which has recently demonstrated its potency to significantly improve the quantification and classification workflows in biomedical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of embryology, point-of-care ovulation testing, as a predictive tool for fetal heart pregnancy, cancer diagnostics via classification of cancer histology images, autosomal polycystic kidney disease, and chronic kidney diseases.
Collapse
Affiliation(s)
- Fazle Rabbi
- Department of Mechanical Engineering, Koç University, Sariyer, Istanbul 34450, Turkey; (F.R.); (S.R.D.)
| | - Sajjad Rahmani Dabbagh
- Department of Mechanical Engineering, Koç University, Sariyer, Istanbul 34450, Turkey; (F.R.); (S.R.D.)
- Koç University Arçelik Research Center for Creative Industries (KUAR), Koç University, Sariyer, Istanbul 34450, Turkey
- Koc University Is Bank Artificial Intelligence Lab (KUIS AILab), Koç University, Sariyer, Istanbul 34450, Turkey
| | - Pelin Angin
- Department of Computer Engineering, Middle East Technical University, Ankara 06800, Turkey;
| | - Ali Kemal Yetisen
- Department of Chemical Engineering, Imperial College London, London SW7 2AZ, UK;
| | - Savas Tasoglu
- Department of Mechanical Engineering, Koç University, Sariyer, Istanbul 34450, Turkey; (F.R.); (S.R.D.)
- Koç University Arçelik Research Center for Creative Industries (KUAR), Koç University, Sariyer, Istanbul 34450, Turkey
- Koc University Is Bank Artificial Intelligence Lab (KUIS AILab), Koç University, Sariyer, Istanbul 34450, Turkey
- Institute of Biomedical Engineering, Boğaziçi University, Çengelköy, Istanbul 34684, Turkey
- Physical Intelligence Department, Max Planck Institute for Intelligent Systems, 70569 Stuttgart, Germany
- Correspondence:
| |
Collapse
|
46
|
John Cremin C, Dash S, Huang X. Big Data: Historic Advances and Emerging Trends in Biomedical Research. CURRENT RESEARCH IN BIOTECHNOLOGY 2022. [DOI: 10.1016/j.crbiot.2022.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
|
47
|
Martínez-García M, Hernández-Lemus E. Data Integration Challenges for Machine Learning in Precision Medicine. Front Med (Lausanne) 2022; 8:784455. [PMID: 35145977 PMCID: PMC8821900 DOI: 10.3389/fmed.2021.784455] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 12/28/2021] [Indexed: 12/19/2022] Open
Abstract
A main goal of Precision Medicine is that of incorporating and integrating the vast corpora on different databases about the molecular and environmental origins of disease, into analytic frameworks, allowing the development of individualized, context-dependent diagnostics, and therapeutic approaches. In this regard, artificial intelligence and machine learning approaches can be used to build analytical models of complex disease aimed at prediction of personalized health conditions and outcomes. Such models must handle the wide heterogeneity of individuals in both their genetic predisposition and their social and environmental determinants. Computational approaches to medicine need to be able to efficiently manage, visualize and integrate, large datasets combining structure, and unstructured formats. This needs to be done while constrained by different levels of confidentiality, ideally doing so within a unified analytical architecture. Efficient data integration and management is key to the successful application of computational intelligence approaches to medicine. A number of challenges arise in the design of successful designs to medical data analytics under currently demanding conditions of performance in personalized medicine, while also subject to time, computational power, and bioethical constraints. Here, we will review some of these constraints and discuss possible avenues to overcome current challenges.
Collapse
Affiliation(s)
- Mireya Martínez-García
- Clinical Research Division, National Institute of Cardiology ‘Ignacio Chávez’, Mexico City, Mexico
| | - Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine (INMEGEN), Mexico City, Mexico
- Center for Complexity Sciences, Universidad Nacional Autnoma de Mexico, Mexico City, Mexico
| |
Collapse
|
48
|
Batko K, Ślęzak A. The use of Big Data Analytics in healthcare. JOURNAL OF BIG DATA 2022; 9:3. [PMID: 35013701 PMCID: PMC8733917 DOI: 10.1186/s40537-021-00553-4] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Accepted: 12/19/2021] [Indexed: 05/30/2023]
Abstract
The introduction of Big Data Analytics (BDA) in healthcare will allow to use new technologies both in treatment of patients and health management. The paper aims at analyzing the possibilities of using Big Data Analytics in healthcare. The research is based on a critical analysis of the literature, as well as the presentation of selected results of direct research on the use of Big Data Analytics in medical facilities. The direct research was carried out based on research questionnaire and conducted on a sample of 217 medical facilities in Poland. Literature studies have shown that the use of Big Data Analytics can bring many benefits to medical facilities, while direct research has shown that medical facilities in Poland are moving towards data-based healthcare because they use structured and unstructured data, reach for analytics in the administrative, business and clinical area. The research positively confirmed that medical facilities are working on both structural data and unstructured data. The following kinds and sources of data can be distinguished: from databases, transaction data, unstructured content of emails and documents, data from devices and sensors. However, the use of data from social media is lower as in their activity they reach for analytics, not only in the administrative and business but also in the clinical area. It clearly shows that the decisions made in medical facilities are highly data-driven. The results of the study confirm what has been analyzed in the literature that medical facilities are moving towards data-based healthcare, together with its benefits.
Collapse
Affiliation(s)
- Kornelia Batko
- Department of Business Informatics, University of Economics in Katowice, Katowice, Poland
| | - Andrzej Ślęzak
- Department of Biomedical Processes and Systems, Institute of Health and Nutrition Sciences, Częstochowa University of Technology, Częstochowa, Poland
| |
Collapse
|
49
|
Sarni N. Nouvelles influences pour la nosographie psychiatrique. ANNALES MEDICO-PSYCHOLOGIQUES 2022. [DOI: 10.1016/j.amp.2021.11.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
50
|
Catlow J, Bray B, Morris E, Rutter M. Power of big data to improve patient care in gastroenterology. Frontline Gastroenterol 2022; 13:237-244. [PMID: 35493622 PMCID: PMC8996101 DOI: 10.1136/flgastro-2019-101239] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 05/23/2021] [Indexed: 02/04/2023] Open
Abstract
Big data is defined as being large, varied or frequently updated, and usually generated from real-world interaction. With the unprecedented availability of big data, comes an obligation to maximise its potential for healthcare improvements in treatment effectiveness, disease prevention and healthcare delivery. We review the opportunities and challenges that big data brings to gastroenterology. We review its sources for healthcare improvement in gastroenterology, including electronic medical records, patient registries and patient-generated data. Big data can complement traditional research methods in hypothesis generation, supporting studies and disseminating findings; and in some cases holds distinct advantages where traditional trials are unfeasible. There is great potential power in patient-level linkage of datasets to help quantify inequalities, identify best practice and improve patient outcomes. We exemplify this with the UK colorectal cancer repository and the potential of linkage using the National Endoscopy Database, the inflammatory bowel disease registry and the National Health Service bowel cancer screening programme. Artificial intelligence and machine learning are increasingly being used to improve diagnostics in gastroenterology, with image analysis entering clinical practice, and the potential of machine learning to improve outcome prediction and diagnostics in other clinical areas. Big data brings issues with large sample sizes, real-world biases, data curation, keeping clinical context at analysis and General Data Protection Regulation compliance. There is a tension between our obligation to use data for the common good and protecting individual patient's data. We emphasise the importance of engaging with our patients to enable them to understand their data usage as fully as they wish.
Collapse
Affiliation(s)
- Jamie Catlow
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
- Gastroenterology, University Hospital of North Tees, Stockton-on-Tees, UK
| | - Benjamin Bray
- Medical Director & Head of Epidemiology, EMEA Data Science, IQVIA Europe, Reading, UK
- Medicine Clinical Academic Group, King's College London, London, UK
| | - Eva Morris
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, Oxfordshire, UK
| | - Matt Rutter
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
- Gastroenterology, University Hospital of North Tees, Stockton-on-Tees, UK
| |
Collapse
|