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Rafiq M, Mazzocato P, Guttmann C, Spaak J, Savage C. Predictive analytics support for complex chronic medical conditions: An experience-based co-design study of physician managers' needs and preferences. Int J Med Inform 2024; 187:105447. [PMID: 38598905 DOI: 10.1016/j.ijmedinf.2024.105447] [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: 03/03/2022] [Revised: 05/05/2023] [Accepted: 04/05/2024] [Indexed: 04/12/2024]
Abstract
PURPOSE The literature suggests predictive technology applications in health care would benefit from physician and manager input during design and development. The aim was to explore the needs and preferences of physician managers regarding the role of predictive analytics in decision support for patients with the highly complex yet common combination of multiple chronic conditions of cardiovascular (Heart) and kidney (Nephrology) diseases and diabetes (HND). METHODS This qualitative study employed an experience-based co-design model comprised of three data gathering phases: 1. Patient mapping through non-participant observations informed by process mining of electronic health records data, 2. Semi-structured experience-based interviews, and 3. A co-design workshop. Data collection was conducted with physician managers working at or collaborating with the HND center, Danderyd University Hospital (DSAB), in Stockholm, Sweden. HND center is an integrated practice unit offering comprehensive person-centered multidisciplinary care to stabilize disease progression, reduce visits, and develop treatment strategies that enables a transition to primary care. RESULTS Interview and workshop data described a complex challenge due to the interaction of underlying pathophysiologies and the subsequent need for multiple care givers that hindered care continuity. The HND center partly met this challenge by coordinating care through multiple interprofessional and interdisciplinary shared decision-making interfaces. The large patient datasets were difficult to operationalize in daily practice due to data entry and retrieval issues. Predictive analytics was seen as a potentially effective approach to support decision-making, calculate risks, and improve resource utilization, especially in the context of complex chronic care, and the HND center a good place for pilot testing and development. Simplicity of visual interfaces, a better understanding of the algorithms by the health care professionals, and the need to address professional concerns, were identified as key factors to increase adoption and facilitate implementation. CONCLUSIONS The HND center serves as a comprehensive integrated practice unit that integrates different medical disciplinary perspectives in a person-centered care process to address the needs of patients with multiple complex comorbidities. Therefore, piloting predictive technologies at the same time with a high potential for improving care represents an extreme, demanding, and complex case. The study findings show that health care professionals' involvement in the design of predictive technologies right from the outset can facilitate the implementation and adoption of such technologies, as well as enhance their predictive effectiveness and performance. Simplicity in the design of predictive technologies and better understanding of the concept and interpretation of the algorithms may result in implementation of predictive technologies in health care. Institutional efforts are needed to enhance collaboration among the health care professionals and IT professionals for effective development, implementation, and adoption of predictive analytics in health care.
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Affiliation(s)
- Muhammad Rafiq
- Department of Learning, Informatics, Management and Ethics (LIME), Medical Management Center, Karolinska Institutet, 171 65 Stockholm, Sweden.
| | - Pamela Mazzocato
- Department of Learning, Informatics, Management and Ethics (LIME), Medical Management Center, Karolinska Institutet, 171 65 Stockholm, Sweden; Södertälje Hospital, Research, Development, Innovation and Education unit, Rosenborgsgatan 6-10, 152 40 Södertälje, Sweden.
| | - Christian Guttmann
- Department of Learning, Informatics, Management and Ethics (LIME), Medical Management Center, Karolinska Institutet, 171 65 Stockholm, Sweden; Nordic Artificial Intelligence Institute, Garvis Carlssons Gata 4, 16941 Stockholm, Sweden.
| | - Jonas Spaak
- Department of Learning, Informatics, Management and Ethics (LIME), Medical Management Center, Karolinska Institutet, 171 65 Stockholm, Sweden; Department of Clinical Sciences, Danderyd University Hospital, Karolinska Institutet, 182 88 Stockholm, Sweden.
| | - Carl Savage
- Department of Learning, Informatics, Management and Ethics (LIME), Medical Management Center, Karolinska Institutet, 171 65 Stockholm, Sweden; School of Health and Welfare, Halmstad University, Halmstad, Sweden.
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Han E, Kharrazi H, Shi L. Identifying Predictors of Nursing Home Admission by Using Electronic Health Records and Administrative Data: Scoping Review. JMIR Aging 2023; 6:e42437. [PMID: 37990815 PMCID: PMC10686617 DOI: 10.2196/42437] [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: 09/05/2022] [Revised: 08/01/2023] [Accepted: 08/07/2023] [Indexed: 11/23/2023] Open
Abstract
Background Among older adults, nursing home admissions (NHAs) are considered a significant adverse outcome and have been extensively studied. Although the volume and significance of electronic data sources are expanding, it is unclear what predictors of NHA have been systematically identified in the literature via electronic health records (EHRs) and administrative data. Objective This study synthesizes findings of recent literature on identifying predictors of NHA that are collected from administrative data or EHRs. Methods The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines were used for study selection. The PubMed and CINAHL databases were used to retrieve the studies. Articles published between January 1, 2012, and March 31, 2023, were included. Results A total of 34 papers were selected for final inclusion in this review. In addition to NHA, all-cause mortality, hospitalization, and rehospitalization were frequently used as outcome measures. The most frequently used models for predicting NHAs were Cox proportional hazards models (studies: n=12, 35%), logistic regression models (studies: n=9, 26%), and a combination of both (studies: n=6, 18%). Several predictors were used in the NHA prediction models, which were further categorized into sociodemographic, caregiver support, health status, health use, and social service use factors. Only 5 (15%) studies used a validated frailty measure in their NHA prediction models. Conclusions NHA prediction tools based on EHRs or administrative data may assist clinicians, patients, and policy makers in making informed decisions and allocating public health resources. More research is needed to assess the value of various predictors and data sources in predicting NHAs and validating NHA prediction models externally.
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Affiliation(s)
- Eunkyung Han
- Ho-Young Institute of Community Health, Paju, Republic of Korea
- Asia Pacific Center For Hospital Management and Leadership Research, Johns Hopkins Bloomberg School of Public Health, BaltimoreMD, United States
| | - Hadi Kharrazi
- Department of Health Policy and Management, Johns Hopkins School of Public Health, BaltimoreMD, United States
- Division of Biomedical Informatics and Data Science, Johns Hopkins School of Medicine, BaltimoreMD, United States
| | - Leiyu Shi
- Department of Health Policy and Management, Johns Hopkins School of Public Health, BaltimoreMD, United States
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Yuan Q, Zhao WL, Qin B. Big data and variceal rebleeding prediction in cirrhosis patients. Artif Intell Gastroenterol 2023; 4:1-9. [DOI: 10.35712/aig.v4.i1.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 02/03/2023] [Accepted: 03/10/2023] [Indexed: 06/08/2023] Open
Abstract
Big data has convincing merits in developing risk stratification strategies for diseases. The 6 “V”s of big data, namely, volume, velocity, variety, veracity, value, and variability, have shown promise for real-world scenarios. Big data can be applied to analyze health data and advance research in preclinical biology, medicine, and especially disease initiation, development, and control. A study design comprises data selection, inclusion and exclusion criteria, standard confirmation and cohort establishment, follow-up strategy, and events of interest. The development and efficiency verification of a prognosis model consists of deciding the data source, taking previous models as references while selecting candidate predictors, assessing model performance, choosing appropriate statistical methods, and model optimization. The model should be able to inform disease development and outcomes, such as predicting variceal rebleeding in patients with cirrhosis. Our work has merits beyond those of other colleagues with respect to cirrhosis patient screening and data source regarding variceal bleeding.
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Affiliation(s)
- Quan Yuan
- Department of Gastroenterology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400042, China
| | - Wen-Long Zhao
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China
- Medical Data Science Academy, Chongqing 400016, China
- Chongqing Engineering Research Centre for Clinical Big-data and Drug Evaluation, Chongqing 400016, China
| | - Bo Qin
- Department of Infectious Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400042, China
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Taipalus T, Isomöttönen V, Erkkilä H, Äyrämö S. Data Analytics in Healthcare: A Tertiary Study. SN COMPUTER SCIENCE 2023; 4:87. [PMID: 36532635 PMCID: PMC9734338 DOI: 10.1007/s42979-022-01507-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 11/14/2022] [Indexed: 12/13/2022]
Abstract
The field of healthcare has seen a rapid increase in the applications of data analytics during the last decades. By utilizing different data analytic solutions, healthcare areas such as medical image analysis, disease recognition, outbreak monitoring, and clinical decision support have been automated to various degrees. Consequently, the intersection of healthcare and data analytics has received scientific attention to the point of numerous secondary studies. We analyze studies on healthcare data analytics, and provide a wide overview of the subject. This is a tertiary study, i.e., a systematic review of systematic reviews. We identified 45 systematic secondary studies on data analytics applications in different healthcare sectors, including diagnosis and disease profiling, diabetes, Alzheimer's disease, and sepsis. Machine learning and data mining were the most widely used data analytics techniques in healthcare applications, with a rising trend in popularity. Healthcare data analytics studies often utilize four popular databases in their primary study search, typically select 25-100 primary studies, and the use of research guidelines such as PRISMA is growing. The results may help both data analytics and healthcare researchers towards relevant and timely literature reviews and systematic mappings, and consequently, towards respective empirical studies. In addition, the meta-analysis presents a high-level perspective on prominent data analytics applications in healthcare, indicating the most popular topics in the intersection of data analytics and healthcare, and provides a big picture on a topic that has seen dozens of secondary studies in the last 2 decades.
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Affiliation(s)
- Toni Taipalus
- grid.9681.60000 0001 1013 7965Faculty of Information Technology, University of Jyväskylä, P.O. Box 35, FI-40014 Jyvaskyla, Finland
| | - Ville Isomöttönen
- grid.9681.60000 0001 1013 7965Faculty of Information Technology, University of Jyväskylä, P.O. Box 35, FI-40014 Jyvaskyla, Finland
| | - Hanna Erkkilä
- grid.9681.60000 0001 1013 7965Faculty of Information Technology, University of Jyväskylä, P.O. Box 35, FI-40014 Jyvaskyla, Finland
| | - Sami Äyrämö
- grid.9681.60000 0001 1013 7965Faculty of Information Technology, University of Jyväskylä, P.O. Box 35, FI-40014 Jyvaskyla, Finland
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Held LA, Wewetzer L, Steinhäuser J. Determinants of the implementation of an artificial intelligence-supported device for the screening of diabetic retinopathy in primary care - a qualitative study. Health Informatics J 2022; 28:14604582221112816. [PMID: 35921547 DOI: 10.1177/14604582221112816] [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: 11/15/2022]
Abstract
Diabetic retinopathy is a microvascular complication of diabetes mellitus that is usually asymptomatic in the early stages. Therefore, its timely detection and treatment are essential. First pilot projects exist to establish a smartphone-based and AI-supported screening of DR in primary care. This study explored health professionals' perceptions of potential barriers and enablers of using a screening such as this in primary care to understand the mechanisms that could influence implementation into routine clinical practice. Semi-structured telephone interviews were conducted and analysed with the help of qualitative analysis of Mayring. The following main influencing factors to implementation have been identified: personal attitude, organisation, time, financial factors, education, support, technical requirement, influence on profession and patient welfare. Most determinants could be relocated in the behaviour change wheel, a validated implementation model. Further research on the patients' perspective and a ranking of the determinants found is needed.
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Affiliation(s)
- Linda A Held
- Institute of Family Medicine, 54360University Medical Center Schleswig-Holstein, Campus Lübeck, Germany
| | - Larisa Wewetzer
- Institute of Family Medicine, 54360University Medical Center Schleswig-Holstein, Campus Lübeck, Germany
| | - Jost Steinhäuser
- Institute of Family Medicine, 54360University Medical Center Schleswig-Holstein, Campus Lübeck, Germany
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Atalla S, Amin SA, Manoj Kumar MV, Sastry NKB, Mansoor W, Rao A. Autonomous Tool for Monitoring Multi-Morbidity Health Conditions in UAE and India. Front Artif Intell 2022; 5:865792. [PMID: 35573899 PMCID: PMC9096249 DOI: 10.3389/frai.2022.865792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 03/23/2022] [Indexed: 11/19/2022] Open
Abstract
Multi-morbidity is the presence of two or more long-term health conditions, including defined physical or mental health conditions, such as diabetes or schizophrenia. One of the regular and critical health cases is an elderly person with a multi-morbid health condition and special complications who lives alone. These patients are typically not familiar with advanced Information and Communications Technology (ICT), but they are comfortable using smart devices such as wearable watches and mobile phones. The use of ICT improves medical quality, promotes patient security and data security, lowers operational and administrative costs, and gives the people in charge to make informed decisions. Additionally, the use of ICT in healthcare practices greatly reduces human errors, enhances clinical outcomes, ramps up care coordination, boosts practice efficiencies, and helps in collecting data over time. The proposed research concept provides a natural technique to implement preventive health care innovative solutions since several health sensors are embedded in devices that autonomously monitor the patients' health conditions in real-time. This enhances the elder's limited ability to predict and respond to critical health situations. Autonomous monitoring can alert doctors and patients themselves of unexpected health conditions. Real-time monitoring, modeling, and predicting health conditions can trigger swift responses by doctors and health officials in case of emergencies. This study will use data science to stimulate discoveries and breakthroughs in the United Arab Emirates (UAE) and India, which will then be reproduced in other world areas to create major gains in health for people, communities, and populations.
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Affiliation(s)
- Shadi Atalla
- College of Engineering and Information Technology, University of Dubai, Dubai, United Arab Emirates
- *Correspondence: Shadi Atalla
| | - Saad Ali Amin
- College of Engineering and Information Technology, University of Dubai, Dubai, United Arab Emirates
| | - M. V. Manoj Kumar
- Department of Information Science and Engineering, Nitte Meenakshi Institute of Technology, Bangalore, India
| | | | - Wathiq Mansoor
- College of Engineering and Information Technology, University of Dubai, Dubai, United Arab Emirates
| | - Ananth Rao
- College of Engineering and Information Technology, University of Dubai, Dubai, United Arab Emirates
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Olde Rikkert MGM, Melis RJF, Cohen AA, (Geeske) Peeters GMEE. Age and Ageing journal 50th anniversary commentary seriesWhy illness is more important than disease in old age. Age Ageing 2022; 51:6501364. [PMID: 35018409 PMCID: PMC8755909 DOI: 10.1093/ageing/afab267] [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: 10/19/2021] [Indexed: 12/05/2022] Open
Abstract
Clinical reasoning and research in modern geriatrics often prioritises the disease concept. This is understandable as it has brought impressive advances in medicine (e.g. antibiotics, vaccines, successful cancer treatment and many effective surgeries). However, so far the disease framework has not succeeded in getting us to root causes of many age-related chronic diseases (e.g. Alzheimer’s disease, diabetes, osteoarthritis). Moreover, in aging and disease constructs alone fail to explain the variability in illness presentations. Therefore, we propose to apply the underused illness concept in a new way by reconsidering the importance of common symptoms in the form of a dynamic network of symptoms as a complementary framework. We show that concepts and methods of complex system thinking now enable to fruitfully monitor and analyse the multiple interactions between symptoms in such in networks, offering new routes for prognosis and treatment. Moreover, close attention to the symptoms that bother older persons may also improve weighing the therapeutic objectives of well-being and survival and aligning treatment targets with the patients’ priorities.
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Affiliation(s)
- Marcel G M Olde Rikkert
- Department of Geriatric Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
| | - René J F Melis
- Department of Geriatric Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Alan A Cohen
- PRIMUS Research Group, Department of Family Medicine, University of Sherbrooke, Sherbrooke, Quebec J1H 5N4, Canada
- Research Center on Aging, Sherbrooke, Quebec J1H 4C4, Canada
- Research Center of Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, Quebec J1H 5N4, Canada
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Samal L, Fu HN, Camara DS, Wang J, Bierman AS, Dorr DA. Health information technology to improve care for people with multiple chronic conditions. Health Serv Res 2021; 56 Suppl 1:1006-1036. [PMID: 34363220 PMCID: PMC8515226 DOI: 10.1111/1475-6773.13860] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 07/15/2021] [Accepted: 07/19/2021] [Indexed: 11/29/2022] Open
Abstract
OBJECTIVE To review evidence regarding the use of Health Information Technology (health IT) interventions aimed at improving care for people living with multiple chronic conditions (PLWMCC) in order to identify critical knowledge gaps. DATA SOURCES We searched MEDLINE, CINAHL, PsycINFO, EMBASE, Compendex, and IEEE Xplore databases for studies published in English between 2010 and 2020. STUDY DESIGN We identified studies of health IT interventions for PLWMCC across three domains as follows: self-management support, care coordination, and algorithms to support clinical decision making. DATA COLLECTION/EXTRACTION METHODS Structured search queries were created and validated. Abstracts were reviewed iteratively to refine inclusion and exclusion criteria. The search was supplemented by manually searching the bibliographic sections of the included studies. The search included a forward citation search of studies nested within a clinical trial to identify the clinical trial protocol and published clinical trial results. Data were extracted independently by two reviewers. PRINCIPAL FINDINGS The search yielded 1907 articles; 44 were included. Nine randomized controlled trials (RCTs) and 35 other studies including quasi-experimental, usability, feasibility, qualitative studies, or development/validation studies of analytic models were included. Five RCTs had positive results, and the remaining four RCTs showed that the interventions had no effect. The studies address individual patient engagement and assess patient-centered outcomes such as quality of life. Few RCTs assess outcomes such as disability and none assess mortality. CONCLUSIONS Despite a growing body of literature on health IT interventions or multicomponent interventions including a health IT component for chronic disease management, current evidence for applying health IT solutions to improve care for PLWMCC is limited. The body of literature included in this review provides critical information on the state of the science as well as the many gaps that need to be filled for digital health to fulfill its promise in supporting care delivery that meets the needs of PLWMCC.
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Affiliation(s)
- Lipika Samal
- Brigham and Women's HospitalBostonMAUSA
- Harvard Medical SchoolBostonMAUSA
| | - Helen N. Fu
- Indiana University Richard M. Fairbanks School of Public HealthIndianapolisINUSA
- Regenstrief InstituteCenter for Biomedical InformaticsIndianapolisINUSA
| | - Djibril S. Camara
- Center for Disease Control and Prevention, Center for Surveillance, Epidemiology, and Laboratory Services (CSELS) Division of Scientific Education and Professional Development, Public Health Informatics Fellowship ProgramAtlantaGeorgiaUSA
- Center for Evidence and Practice Improvement, Agency for Healthcare Research and QualityRockvilleMDUSA
| | - Jing Wang
- Center for Evidence and Practice Improvement, Agency for Healthcare Research and QualityRockvilleMDUSA
- Florida State University College of NursingTallahasseeFloridaUSA
- Health and Aging Policy Fellows Program at Columbia UniversityNew YorkNYUSA
| | - Arlene S. Bierman
- Center for Evidence and Practice Improvement, Agency for Healthcare Research and QualityRockvilleMDUSA
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da Fonseca MH, Kovaleski F, Picinin CT, Pedroso B, Rubbo P. E-Health Practices and Technologies: A Systematic Review from 2014 to 2019. Healthcare (Basel) 2021; 9:healthcare9091192. [PMID: 34574966 PMCID: PMC8470487 DOI: 10.3390/healthcare9091192] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 08/17/2021] [Accepted: 08/26/2021] [Indexed: 12/17/2022] Open
Abstract
E-health can be defined as a set of technologies applied with the help of the internet, in which healthcare services are provided to improve quality of life and facilitate healthcare delivery. As there is a lack of similar studies on the topic, this analysis uses a systematic literature review of articles published from 2014 to 2019 to identify the most common e-health practices used worldwide, as well as the main services provided, diseases treated, and the associated technologies that assist in e-health practices. Some of the key results were the identification of the four most common practices used (mhealth or mobile health; telehealth or telemedicine; technology; and others) and the most widely used technologies associated with e-health (IoT, cloud computing, Big Data, security, and systems).
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Affiliation(s)
- Maria Helena da Fonseca
- Department of Production Engineering, Federal University of Technology—Paraná (UTFPR), Ponta Grossa 84017-220, Brazil; (F.K.); (C.T.P.)
- Correspondence: ; Tel.: +55-42-999388129
| | - Fanny Kovaleski
- Department of Production Engineering, Federal University of Technology—Paraná (UTFPR), Ponta Grossa 84017-220, Brazil; (F.K.); (C.T.P.)
| | - Claudia Tania Picinin
- Department of Production Engineering, Federal University of Technology—Paraná (UTFPR), Ponta Grossa 84017-220, Brazil; (F.K.); (C.T.P.)
| | - Bruno Pedroso
- Division of Physical Education, State University of Ponta Grossa—Paraná (UEPG), Ponta Grossa 84030-900, Brazil;
| | - Priscila Rubbo
- Department of Accounting Sciences, Federal University of Technology—Paraná (UTFPR), Pato Branco 85503-390, Brazil;
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Khan IH, Javaid M. Big Data Applications in Medical Field: A Literature Review. JOURNAL OF INDUSTRIAL INTEGRATION AND MANAGEMENT 2020. [DOI: 10.1142/s242486222030001x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Digital imaging and medical reporting have acquired an essential role in healthcare, but the main challenge is the storage of a high volume of patient data. Although newer technologies are already introduced in the medical sciences to save records size, Big Data provides advancements by storing a large amount of data to improve the efficiency and quality of patient treatment with better care. It provides intelligent automation capabilities to reduce errors than manual inputs. Large numbers of research papers on big data in the medical field are studied and analyzed for their impacts, benefits, and applications. Big data has great potential to support the digitalization of all medical and clinical records and then save the entire data regarding the medical history of an individual or a group. This paper discusses big data usage for various industries and sectors. Finally, 12 significant applications for the medical field by the implementation of big data are identified and studied with a brief description. This technology can be gainfully used to extract useful information from the available data by analyzing and managing them through a combination of hardware and software. With technological advancement, big data provides health-related information for millions of patient-related to life issues such as lab tests reporting, clinical narratives, demographics, prescription, medical diagnosis, and related documentation. Thus, Big Data is essential in developing a better yet efficient analysis and storage healthcare services. The demand for big data applications is increasing due to its capability of handling and analyzing massive data. Not only in the future but even now, Big Data is proving itself as an axiom of storing, developing, analyzing, and providing overall health information to the physicians.
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Affiliation(s)
- Ibrahim Haleem Khan
- School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi, India
| | - Mohd Javaid
- Department of Mechanical Engineering, Jamia Millia Islamia, New Delhi, India
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11
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Wilfling D, Hinz A, Steinhäuser J. Big data analysis techniques to address polypharmacy in patients - a scoping review. BMC FAMILY PRACTICE 2020; 21:180. [PMID: 32883227 PMCID: PMC7472702 DOI: 10.1186/s12875-020-01247-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Accepted: 08/17/2020] [Indexed: 11/10/2022]
Abstract
Background Polypharmacy is a key challenge in healthcare especially in older and multimorbid patients. The use of multiple medications increases the potential for drug interactions and for prescription of potentially inappropriate medications. eHealth solutions are increasingly recommended in healthcare, with big data analysis techniques as a major component. In the following we use the term analysis of big data as referring to the computational analysis of large data sets to find patterns, trends, and associations in large data sets collected from a wide range of sources in contrast to using classical statistics programs. It is hypothesized that big data analysis is able to reveal patterns in patient data that would not be identifiable using conventional methods of data analysis. The aim of this review was to evaluate whether there are existing big data analysis techniques that can help to identify patients consuming multiple drugs and to assist in the reduction of polypharmacy in patients. Methods A computerized search was conducted in February 2019 and updated in May 2020, using the PubMed, Web of Science and Cochrane Library databases. The search strategy was defined by the principles of a systematic search, using the PICO scheme. All studies evaluating big data analytics about patients consuming multiple drugs were considered. Two researchers assessed all search results independently to identify eligible studies. The data was then extracted into standardized tables. Results A total of 327 studies were identified through the database search. After title and abstract screening, 302 items were removed. Only three studies were identified as addressing big data analysis techniques in patients with polypharmacy. One study extracted antipsychotic polypharmacy data, the second introduced a decision support system to evaluate side-effects in patients with polypharmacy and the third evaluated a decision support system to identify polypharmacy-related problems in individuals. Conclusions There are few studies to date which have used big data analysis techniques for identification and management of polypharmacy. There may be a need to further explore interdisciplinary collaboration between computer scientists and healthcare professionals, to develop and evaluate big data analysis techniques that can be implemented to manage polypharmacy.
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Affiliation(s)
- D Wilfling
- Institute of Family Medicine, University Hospital Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23538, Lübeck, Germany.
| | - A Hinz
- Institute of Family Medicine, University Hospital Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23538, Lübeck, Germany
| | - J Steinhäuser
- Institute of Family Medicine, University Hospital Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23538, Lübeck, Germany
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