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Irgang L, Barth H, Holmén M. Data-Driven Technologies as Enablers for Value Creation in the Prevention of Surgical Site Infections: a Systematic Review. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2023; 7:1-41. [PMID: 36910913 PMCID: PMC9995622 DOI: 10.1007/s41666-023-00129-2] [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: 04/05/2022] [Revised: 01/16/2023] [Accepted: 02/03/2023] [Indexed: 03/02/2023]
Abstract
Despite the advances in modern medicine, the use of data-driven technologies (DDTs) to prevent surgical site infections (SSIs) remains a major challenge. Scholars recognise that data management is the next frontier in infection prevention, but many aspects related to the benefits and advantages of using DDTs to mitigate SSI risk factors remain unclear and underexplored in the literature. This study explores how DDTs enable value creation in the prevention of SSIs. This study follows a systematic literature review approach and the PRISMA statement to analyse peer-reviewed articles from seven databases. Fifty-nine articles were included in the review and were analysed through a descriptive and a thematic analysis. The findings suggest a growing interest in DDTs in SSI prevention in the last 5 years, and that machine learning and smartphone applications are widely used in SSI prevention. DDTs are mainly applied to prevent SSIs in clean and clean-contaminated surgeries and often used to manage patient-related data in the postoperative stage. DDTs enable the creation of nine categories of value that are classified in four dimensions: cost/sacrifice, functional/instrumental, experiential/hedonic, and symbolic/expressive. This study offers a unique and systematic overview of the value creation aspects enabled by DDT applications in SSI prevention and suggests that additional research is needed in four areas: value co-creation and product-service systems, DDTs in contaminated and dirty surgeries, data legitimation and explainability, and data-driven interventions. Supplementary Information The online version contains supplementary material available at 10.1007/s41666-023-00129-2.
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Affiliation(s)
- Luís Irgang
- School of Business, Innovation and Sustainability - Department of Engineering and Innovation, Halmstad University, Halmstad, Sweden
| | - Henrik Barth
- School of Business, Innovation and Sustainability - Department of Engineering and Innovation, Halmstad University, Halmstad, Sweden
| | - Magnus Holmén
- School of Business, Innovation and Sustainability - Department of Engineering and Innovation, Halmstad University, Halmstad, Sweden
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Performance of machine learning algorithms for surgical site infection case detection and prediction: A systematic review and meta-analysis. Ann Med Surg (Lond) 2022; 84:104956. [PMID: 36582918 PMCID: PMC9793260 DOI: 10.1016/j.amsu.2022.104956] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 11/08/2022] [Accepted: 11/13/2022] [Indexed: 11/24/2022] Open
Abstract
Background Medical researchers and clinicians have shown much interest in developing machine learning (ML) algorithms to detect/predict surgical site infections (SSIs). However, little is known about the overall performance of ML algorithms in predicting SSIs and how to improve the algorithm's robustness. We conducted a systematic review and meta-analysis to summarize the performance of ML algorithms in SSIs case detection and prediction and to describe the impact of using unstructured and textual data in the development of ML algorithms. Methods MEDLINE, EMBASE, CINAHL, CENTRAL and Web of Science were searched from inception to March 25, 2021. Study characteristics and algorithm development information were extracted. Performance statistics (e.g., sensitivity, area under the receiver operating characteristic curve [AUC]) were pooled using a random effect model. Stratified analysis was applied to different study characteristic levels. Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Diagnostic Test Accuracy Studies (PRISMA-DTA) was followed. Results Of 945 articles identified, 108 algorithms from 32 articles were included in this review. The overall pooled estimate of the SSI incidence rate was 3.67%, 95% CI: 3.58-3.76. Mixed-use of structured and textual data-based algorithms (pooled estimates of sensitivity 0.83, 95% CI: 0.78-0.87, specificity 0.92, 95% CI: 0.86-0.95, AUC 0.92, 95% CI: 0.89-0.94) outperformed algorithms solely based on structured data (sensitivity 0.56, 95% CI:0.43-0.69, specificity 0.95, 95% CI:0.91-0.97, AUC = 0.90, 95% CI: 0.87-0.92). Conclusions ML algorithms developed with structured and textual data provided optimal performance. External validation of ML algorithms is needed to translate current knowledge into clinical practice.
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Pandey NN, Sharma S. Conducting and critically appraising a high-quality systematic review and Meta-analysis pertaining to COVID-19. Curr Med Res Opin 2022; 38:317-325. [PMID: 34870545 DOI: 10.1080/03007995.2021.2015160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
With constantly emerging new information regarding the epidemiology, pathogenesis, diagnosis and management of Coronavirus Disease 2019 (COVID-19), reviewing literature related to it has become increasingly complicated and resource-intensive. In the setting of this global pandemic, clinical decisions are being guided by the results of multiple pertinent studies; however, it has been observed that these studies are often heterogenous in design and population characteristics and results of initial trials may not be replicated in subsequent studies. The resulting clinical conundrum can be resolved by high-quality systematic review and meta-analysis with a robust and reliable methodology, encapsulating and critically appraising all the available literature relevant to the clinical scenario under scrutiny. It can condense the large volume of scientific information available and can also identify the cause of differences in the degree of effect under consideration across different studies. It can identify optimal diagnostic algorithms, assess efficacy of treatment strategies, and analyze inherent factors influencing the efficacy of treatment for COVID-19. The current review aims to provide a basic guide to plan and conduct a high-quality systematic review and meta-analysis pertaining to COVID-19, describing the main steps and addressing the pitfalls commonly encountered at each step. Knowledge of the basic steps would also allow the reader to critically appraise published systematic review and meta-analysis and the quality of evidence provided therein.
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Affiliation(s)
- Niraj Nirmal Pandey
- Department of Cardiovascular Radiology and Endovascular Interventions, All India Institute of Medical Sciences, New Delhi, India
| | - Sanjiv Sharma
- Department of Cardiovascular Radiology and Endovascular Interventions, All India Institute of Medical Sciences, New Delhi, India
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Ciofi Degli Atti ML, Pecoraro F, Piga S, Luzi D, Raponi M. Developing a Surgical Site Infection Surveillance System Based on Hospital Unstructured Clinical Notes and Text Mining. Surg Infect (Larchmt) 2020; 21:716-721. [DOI: 10.1089/sur.2019.238] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
| | - Fabrizio Pecoraro
- National Research Council, Institute for Research on Population and Social Policies, Rome, Italy
| | - Simone Piga
- Clinical Epidemiology Unit, Bambino Gesù Children's Hospital, Rome, Italy
| | - Daniela Luzi
- National Research Council, Institute for Research on Population and Social Policies, Rome, Italy
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Streefkerk HRA, Verkooijen RP, Bramer WM, Verbrugh HA. Electronically assisted surveillance systems of healthcare-associated infections: a systematic review. ACTA ACUST UNITED AC 2020; 25. [PMID: 31964462 PMCID: PMC6976884 DOI: 10.2807/1560-7917.es.2020.25.2.1900321] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Background Surveillance of healthcare-associated infections (HAI) is the basis of each infection control programme and, in case of acute care hospitals, should ideally include all hospital wards, medical specialties as well as all types of HAI. Traditional surveillance is labour intensive and electronically assisted surveillance systems (EASS) hold the promise to increase efficiency. Objectives To give insight in the performance characteristics of different approaches to EASS and the quality of the studies designed to evaluate them. Methods In this systematic review, online databases were searched and studies that compared an EASS with a traditional surveillance method were included. Two different indicators were extracted from each study, one regarding the quality of design (including reporting efficiency) and one based on the performance (e.g. specificity and sensitivity) of the EASS presented. Results A total of 78 studies were included. The majority of EASS (n = 72) consisted of an algorithm-based selection step followed by confirmatory assessment. The algorithms used different sets of variables. Only a minority (n = 7) of EASS were hospital-wide and designed to detect all types of HAI. Sensitivity of EASS was generally high (> 0.8), but specificity varied (0.37–1). Less than 20% (n = 14) of the studies presented data on the efficiency gains achieved. Conclusions Electronically assisted surveillance of HAI has yet to reach a mature stage and to be used routinely in healthcare settings. We recommend that future studies on the development and implementation of EASS of HAI focus on thorough validation, reproducibility, standardised datasets and detailed information on efficiency.
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Affiliation(s)
- H Roel A Streefkerk
- Albert Schweitzer Hospital/Rivas group Beatrix hospital/Regionaal Laboratorium medische Microbiologie, Dordrecht/Gorinchem, the Netherlands.,Erasmus University Medical Center (Erasmus MC), Rotterdam, the Netherlands
| | - Roel Paj Verkooijen
- Department of Medical Microbiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Wichor M Bramer
- Medical Library, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Henri A Verbrugh
- Erasmus University Medical Center (Erasmus MC), Rotterdam, the Netherlands
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da Silva DA, ten Caten CS, dos Santos RP, Fogliatto FS, Hsuan J. Predicting the occurrence of surgical site infections using text mining and machine learning. PLoS One 2019; 14:e0226272. [PMID: 31834905 PMCID: PMC6910696 DOI: 10.1371/journal.pone.0226272] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 11/22/2019] [Indexed: 12/11/2022] Open
Abstract
In this study we propose the use of text mining and machine learning methods to predict and detect Surgical Site Infections (SSIs) using textual descriptions of surgeries and post-operative patients’ records, mined from the database of a high complexity University hospital. SSIs are among the most common adverse events experienced by hospitalized patients; preventing such events is fundamental to ensure patients’ safety. Knowledge on SSI occurrence rates may also be useful in preventing future episodes. We analyzed 15,479 surgery descriptions and post-operative records testing different preprocessing strategies and the following machine learning algorithms: Linear SVC, Logistic Regression, Multinomial Naive Bayes, Nearest Centroid, Random Forest, Stochastic Gradient Descent, and Support Vector Classification (SVC). For prediction purposes, the best result was obtained using the Stochastic Gradient Descent method (79.7% ROC-AUC); for detection, Logistic Regression yielded the best performance (80.6% ROC-AUC).
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Affiliation(s)
- Daniel A. da Silva
- Industrial Engineering Department, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Carla S. ten Caten
- Industrial Engineering Department, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | | | - Flavio S. Fogliatto
- Industrial Engineering Department, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- * E-mail:
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Letica-Kriegel AS, Salmasian H, Vawdrey DK, Youngerman BE, Green RA, Furuya EY, Calfee DP, Perotte R. Identifying the risk factors for catheter-associated urinary tract infections: a large cross-sectional study of six hospitals. BMJ Open 2019; 9:e022137. [PMID: 30796114 PMCID: PMC6398917 DOI: 10.1136/bmjopen-2018-022137] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
MOTIVATION Catheter-associated urinary tract infections (CAUTI) are a common and serious healthcare-associated infection. Despite many efforts to reduce the occurrence of CAUTI, there remains a gap in the literature about CAUTI risk factors, especially pertaining to the effect of catheter dwell-time on CAUTI development and patient comorbidities. OBJECTIVE To examine how the risk for CAUTI changes over time. Additionally, to assess whether time from catheter insertion to CAUTI event varied according to risk factors such as age, sex, patient type (surgical vs medical) and comorbidities. DESIGN Retrospective cohort study of all patients who were catheterised from 2012 to 2016, including those who did and did not develop CAUTIs. Both paediatric and adult patients were included. Indwelling urinary catheterisation is the exposure variable. The variable is interval, as all participants were exposed but for different lengths of time. SETTING Urban academic health system of over 2500 beds. The system encompasses two large academic medical centres, two community hospitals and a paediatric hospital. RESULTS The study population was 47 926 patients who had 61 047 catheterisations, of which 861 (1.41%) resulted in a CAUTI. CAUTI rates were found to increase non-linearly for each additional day of catheterisation; CAUTI-free survival was 97.3% (CI: 97.1 to 97.6) at 10 days, 88.2% (CI: 86.9 to 89.5) at 30 days and 71.8% (CI: 66.3 to 77.8) at 60 days. This translated to an instantaneous HR of. 49%-1.65% in the 10-60 day time range. Paraplegia, cerebrovascular disease and female sex were found to statistically increase the chances of a CAUTI. CONCLUSIONS Using a very large data set, we demonstrated the incremental risk of CAUTI associated with each additional day of catheterisation, as well as the risk factors that increase the hazard for CAUTI. Special attention should be given to patients carrying these risk factors, for example, females or those with mobility issues.
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Affiliation(s)
| | - Hojjat Salmasian
- Department of Quality and Safety, Brigham and Women’s Hospital, Boston, Massachusetts, USA
- Division of General Internal Medicine, Harvard Medical School, New York, USA
| | - David K Vawdrey
- Value Institute, NewYork-Presbyterian Hospital, New York, USA
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, USA
| | - Brett E Youngerman
- Department of Neurological Surgery, Columbia University Irving Medical Center, New York, USA
| | - Robert A Green
- Department of Quality and Patient Safety, NewYork-Presbyterian Hospital, New York, USA
- Department of Emergency Medicine, Columbia University Irving Medical Center, New York, USA
| | - E Yoko Furuya
- Department of Medicine, Columbia University Irving Medical Center, New York, USA
- Department of Infection Prevention and Control, NewYork-Presbyterian Hospital, New York, USA
| | - David P Calfee
- Department of Infection Prevention and Control, NewYork-Presbyterian Hospital, New York, USA
- Department of Medicine, Weill Cornell Medicine, New York, USA
| | - Rimma Perotte
- Value Institute, NewYork-Presbyterian Hospital, New York, USA
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, USA
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Beller E, Clark J, Tsafnat G, Adams C, Diehl H, Lund H, Ouzzani M, Thayer K, Thomas J, Turner T, Xia J, Robinson K, Glasziou P. Making progress with the automation of systematic reviews: principles of the International Collaboration for the Automation of Systematic Reviews (ICASR). Syst Rev 2018; 7:77. [PMID: 29778096 PMCID: PMC5960503 DOI: 10.1186/s13643-018-0740-7] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2017] [Accepted: 05/02/2018] [Indexed: 02/08/2023] Open
Abstract
Systematic reviews (SR) are vital to health care, but have become complicated and time-consuming, due to the rapid expansion of evidence to be synthesised. Fortunately, many tasks of systematic reviews have the potential to be automated or may be assisted by automation. Recent advances in natural language processing, text mining and machine learning have produced new algorithms that can accurately mimic human endeavour in systematic review activity, faster and more cheaply. Automation tools need to be able to work together, to exchange data and results. Therefore, we initiated the International Collaboration for the Automation of Systematic Reviews (ICASR), to successfully put all the parts of automation of systematic review production together. The first meeting was held in Vienna in October 2015. We established a set of principles to enable tools to be developed and integrated into toolkits.This paper sets out the principles devised at that meeting, which cover the need for improvement in efficiency of SR tasks, automation across the spectrum of SR tasks, continuous improvement, adherence to high quality standards, flexibility of use and combining components, the need for a collaboration and varied skills, the desire for open source, shared code and evaluation, and a requirement for replicability through rigorous and open evaluation.Automation has a great potential to improve the speed of systematic reviews. Considerable work is already being done on many of the steps involved in a review. The 'Vienna Principles' set out in this paper aim to guide a more coordinated effort which will allow the integration of work by separate teams and build on the experience, code and evaluations done by the many teams working across the globe.
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Affiliation(s)
- Elaine Beller
- Centre for Research in Evidence-Based Practice, Bond University, Robina, Australia.
| | - Justin Clark
- Centre for Research in Evidence-Based Practice, Bond University, Robina, Australia
| | - Guy Tsafnat
- Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
| | - Clive Adams
- Faculty of Medicine and Health Sciences, University of Nottingham, Nottingham, UK
| | - Heinz Diehl
- Centre for Evidence-Based Practice, Bergen University College, Bergen, Norway
| | - Hans Lund
- Western Norway University of Applied Sciences, Bergen, Norway
| | - Mourad Ouzzani
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Kristina Thayer
- National Institute of Environmental Health Sciences, PennState University, Pennsylvania, USA
| | | | | | - Jun Xia
- Faculty of Medicine and Health Sciences, University of Nottingham, Nottingham, UK
| | - Karen Robinson
- JHU Evidence-based Practice Center, Johns Hopkins University, Baltimore, USA
| | - Paul Glasziou
- Centre for Research in Evidence-Based Practice, Bond University, Robina, Australia
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Erickson J, Abbott K, Susienka L. Automatic address validation and health record review to identify homeless Social Security disability applicants. J Biomed Inform 2018; 82:41-46. [PMID: 29705196 DOI: 10.1016/j.jbi.2018.04.012] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2017] [Revised: 01/30/2018] [Accepted: 04/24/2018] [Indexed: 01/01/2023]
Abstract
OBJECTIVE Homeless patients face a variety of obstacles in pursuit of basic social services. Acknowledging this, the Social Security Administration directs employees to prioritize homeless patients and handle their disability claims with special care. However, under existing manual processes for identification of homelessness, many homeless patients never receive the special service to which they are entitled. In this paper, we explore address validation and automatic annotation of electronic health records to improve identification of homeless patients. MATERIALS AND METHODS We developed a sample of claims containing medical records at the moment of arrival in a single office. Using address validation software, we reconciled patient addresses with public directories of homeless shelters, veterans' hospitals and clinics, and correctional facilities. Other tools annotated electronic health records. We trained random forests to identify homeless patients and validated each model with 10-fold cross validation. RESULTS For our finished model, the area under the receiver operating characteristic curve was 0.942. The random forest improved sensitivity from 0.067 to 0.879 but decreased positive predictive value to 0.382. DISCUSSION Presumed false positive classifications bore many characteristics of homelessness. Organizations could use these methods to prompt early collection of information necessary to avoid labor-intensive attempts to reestablish contact with homeless individuals. Annually, such methods could benefit tens of thousands of patients who are homeless, destitute, and in urgent need of assistance. CONCLUSION We were able to identify many more homeless patients through a combination of automatic address validation and natural language processing of unstructured electronic health records.
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Affiliation(s)
- Jennifer Erickson
- Minnesota Disability Determination Services, 121 7th Place E, Saint Paul, MN 55101, United States.
| | - Kenneth Abbott
- Minnesota Disability Determination Services, 121 7th Place E, Saint Paul, MN 55101, United States
| | - Lucinda Susienka
- Minnesota Disability Determination Services, 121 7th Place E, Saint Paul, MN 55101, United States
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10
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Abbott K, Ho YY, Erickson J. Automatic health record review to help prioritize gravely ill Social Security disability applicants. J Am Med Inform Assoc 2018; 24:709-716. [PMID: 28108546 DOI: 10.1093/jamia/ocw159] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2016] [Accepted: 11/01/2016] [Indexed: 12/27/2022] Open
Abstract
Objective Every year, thousands of patients die waiting for disability benefits from the Social Security Administration. Some qualify for expedited service under the Compassionate Allowance (CAL) initiative, but CAL software focuses exclusively on information from a single form field. This paper describes the development of a supplemental process for identifying some overlooked but gravely ill applicants, through automatic annotation of health records accompanying new claims. We explore improved prioritization instead of fully autonomous claims approval. Materials and Methods We developed a sample of claims containing medical records at the moment of arrival in a single office. A series of tools annotated both patient records and public Web page descriptions of CAL medical conditions. We trained random forests to identify CAL patients and validated each model with 10-fold cross validation. Results Our main model, a general CAL classifier, had an area under the receiver operating characteristic curve of 0.915. Combining this classifier with existing software improved sensitivity from 0.960 to 0.994, detecting every deceased patient, but reducing positive predictive value to 0.216. Discussion True positive CAL identification is a priority, given CAL patient mortality. Mere prioritization of the false positives would not create a meaningful burden in terms of manual review. Death certificate data suggest the presence of truly ill patients among putative false positives. Conclusion To a limited extent, it is possible to identify gravely ill Social Security disability applicants by analyzing annotations of unstructured electronic health records, and the level of identification is sufficient to be useful in prioritizing case reviews.
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Affiliation(s)
- Kenneth Abbott
- Minnesota Disability Determination Services, Saint Paul, Minnesota, USA
| | - Yen-Yi Ho
- Department of Statistics, College of Arts and Sciences, University of South Carolina, Columbia, SC, USA
| | - Jennifer Erickson
- Minnesota Disability Determination Services, Saint Paul, Minnesota, USA
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Lucini FR, Fogliatto FS, da Silveira GJC, Neyeloff JL, Anzanello MJ, Kuchenbecker RS, Schaan BD. Text mining approach to predict hospital admissions using early medical records from the emergency department. Int J Med Inform 2017; 100:1-8. [PMID: 28241931 DOI: 10.1016/j.ijmedinf.2017.01.001] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Revised: 10/31/2016] [Accepted: 01/03/2017] [Indexed: 11/25/2022]
Abstract
OBJECTIVE Emergency department (ED) overcrowding is a serious issue for hospitals. Early information on short-term inward bed demand from patients receiving care at the ED may reduce the overcrowding problem, and optimize the use of hospital resources. In this study, we use text mining methods to process data from early ED patient records using the SOAP framework, and predict future hospitalizations and discharges. DESIGN We try different approaches for pre-processing of text records and to predict hospitalization. Sets-of-words are obtained via binary representation, term frequency, and term frequency-inverse document frequency. Unigrams, bigrams and trigrams are tested for feature formation. Feature selection is based on χ2 and F-score metrics. In the prediction module, eight text mining methods are tested: Decision Tree, Random Forest, Extremely Randomized Tree, AdaBoost, Logistic Regression, Multinomial Naïve Bayes, Support Vector Machine (Kernel linear) and Nu-Support Vector Machine (Kernel linear). MEASUREMENTS Prediction performance is evaluated by F1-scores. Precision and Recall values are also informed for all text mining methods tested. RESULTS Nu-Support Vector Machine was the text mining method with the best overall performance. Its average F1-score in predicting hospitalization was 77.70%, with a standard deviation (SD) of 0.66%. CONCLUSIONS The method could be used to manage daily routines in EDs such as capacity planning and resource allocation. Text mining could provide valuable information and facilitate decision-making by inward bed management teams.
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Affiliation(s)
- Filipe R Lucini
- Industrial Engineering Department, Federal University of Rio Grande do Sul. Av. Osvaldo Aranha, 99, 5° Andar, 90035-190 Porto Alegre, RS, Brazil.
| | - Flavio S Fogliatto
- Industrial Engineering Department, Federal University of Rio Grande do Sul. Av. Osvaldo Aranha, 99, 5° Andar, 90035-190 Porto Alegre, RS, Brazil
| | - Giovani J C da Silveira
- Haskayne School of Business, University of Calgary, 2500 University Dr NW, T2N 1N4 Calgary, AB, Canada
| | - Jeruza L Neyeloff
- Hospital de Clínicas de Porto Alegre, Federal University of Rio Grande do Sul. Rua Ramiro Barcelos, 2350, 90035-903 Porto Alegre, RS, Brazil
| | - Michel J Anzanello
- Industrial Engineering Department, Federal University of Rio Grande do Sul. Av. Osvaldo Aranha, 99, 5° Andar, 90035-190 Porto Alegre, RS, Brazil
| | - Ricardo S Kuchenbecker
- Hospital de Clínicas de Porto Alegre, Federal University of Rio Grande do Sul. Rua Ramiro Barcelos, 2350, 90035-903 Porto Alegre, RS, Brazil
| | - Beatriz D Schaan
- Hospital de Clínicas de Porto Alegre, Federal University of Rio Grande do Sul. Rua Ramiro Barcelos, 2350, 90035-903 Porto Alegre, RS, Brazil
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Samuels JG, McGrath RJ, Fetzer SJ, Mittal P, Bourgoine D. Using the Electronic Health Record in Nursing Research: Challenges and Opportunities. West J Nurs Res 2015; 37:1284-94. [PMID: 25819698 DOI: 10.1177/0193945915576778] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Changes in the patient record from the paper to the electronic health record format present challenges and opportunities for the nurse researcher. Current use of data from the electronic health record is in a state of flux. Novel data analytic techniques and massive data sets provide new opportunities for nursing science. Realization of a strong electronic data output future relies on meeting challenges of system use and operability, data presentation, and privacy. Nurse researchers need to rethink aspects of proposal development. Joining ongoing national efforts aimed at creating usable data output is encouraged as a means to affect system design. Working to address challenges and embrace opportunities will help grow the science in a way that answers important patient care questions.
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Simpao AF, Ahumada LM, Rehman MA. Big data and visual analytics in anaesthesia and health care. Br J Anaesth 2015; 115:350-6. [PMID: 25627395 DOI: 10.1093/bja/aeu552] [Citation(s) in RCA: 56] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Advances in computer technology, patient monitoring systems, and electronic health record systems have enabled rapid accumulation of patient data in electronic form (i.e. big data). Organizations such as the Anesthesia Quality Institute and Multicenter Perioperative Outcomes Group have spearheaded large-scale efforts to collect anaesthesia big data for outcomes research and quality improvement. Analytics--the systematic use of data combined with quantitative and qualitative analysis to make decisions--can be applied to big data for quality and performance improvements, such as predictive risk assessment, clinical decision support, and resource management. Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces, and it can facilitate performance of cognitive activities involving big data. Ongoing integration of big data and analytics within anaesthesia and health care will increase demand for anaesthesia professionals who are well versed in both the medical and the information sciences.
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Affiliation(s)
- A F Simpao
- Department of Anesthesiology and Critical Care, Perelman School of Medicine at the University of Pennsylvania and the Children's Hospital of Philadelphia, 3401 Civic Center Boulevard, Suite 9329, Philadelphia, PA 19104-4399, USA
| | - L M Ahumada
- Enterprise Analytics and Reporting, The Children's Hospital of Philadelphia, 1300 Market Street, Room W-8006, Philadelphia, PA 19107-3323, USA
| | - M A Rehman
- Department of Anesthesiology and Critical Care, Perelman School of Medicine at the University of Pennsylvania and the Children's Hospital of Philadelphia, 3401 Civic Center Boulevard, Suite 9329, Philadelphia, PA 19104-4399, USA
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14
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A review of analytics and clinical informatics in health care. J Med Syst 2014; 38:45. [PMID: 24696396 DOI: 10.1007/s10916-014-0045-x] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2014] [Accepted: 03/20/2014] [Indexed: 01/18/2023]
Abstract
Federal investment in health information technology has incentivized the adoption of electronic health record systems by physicians and health care organizations; the result has been a massive rise in the collection of patient data in electronic form (i.e. "Big Data"). Health care systems have leveraged Big Data for quality and performance improvements using analytics-the systematic use of data combined with quantitative as well as qualitative analysis to make decisions. Analytics have been utilized in various aspects of health care including predictive risk assessment, clinical decision support, home health monitoring, finance, and resource allocation. Visual analytics is one example of an analytics technique with an array of health care and research applications that are well described in the literature. The proliferation of Big Data and analytics in health care has spawned a growing demand for clinical informatics professionals who can bridge the gap between the medical and information sciences.
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