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Loftus TJ, Balch JA, Marquard JL, Ray JM, Alper BS, Ojha N, Bihorac A, Melton-Meaux G, Khanna G, Tignanelli CJ. Longitudinal clinical decision support for assessing decisions over time: State-of-the-art and future directions. Digit Health 2024; 10:20552076241249925. [PMID: 38708184 PMCID: PMC11067677 DOI: 10.1177/20552076241249925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 04/10/2024] [Indexed: 05/07/2024] Open
Abstract
Objective Patients and clinicians rarely experience healthcare decisions as snapshots in time, but clinical decision support (CDS) systems often represent decisions as snapshots. This scoping review systematically maps challenges and facilitators to longitudinal CDS that are applied at two or more timepoints for the same decision made by the same patient or clinician. Methods We searched Embase, PubMed, and Medline databases for articles describing development, validation, or implementation of patient- or clinician-facing longitudinal CDS. Validated quality assessment tools were used for article selection. Challenges and facilitators to longitudinal CDS are reported according to PRISMA-ScR guidelines. Results Eight articles met inclusion criteria; each article described a unique CDS. None used entirely automated data entry, none used living guidelines for updating the evidence base or knowledge engine as new evidence emerged during the longitudinal study, and one included formal readiness for change assessments. Seven of eight CDS were implemented and evaluated prospectively. Challenges were primarily related to suboptimal study design (with unique challenges for each study) or user interface. Facilitators included use of randomized trial designs for prospective enrollment, increased CDS uptake during longitudinal exposure, and machine-learning applications that are tailored to the CDS use case. Conclusions Despite the intuitive advantages of representing healthcare decisions longitudinally, peer-reviewed literature on longitudinal CDS is sparse. Existing reports suggest opportunities to incorporate longitudinal CDS frameworks, automated data entry, living guidelines, and user readiness assessments. Generating best practice guidelines for longitudinal CDS would require a greater depth and breadth of published work and expert opinion.
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Affiliation(s)
- Tyler J Loftus
- Department of Surgery, University of Florida Health, Gainesville, FL, USA
- Intelligent Critical Care Center (IC3), University of Florida Health, Gainesville, FL, USA
| | - Jeremy A Balch
- Department of Surgery, University of Florida Health, Gainesville, FL, USA
- Intelligent Critical Care Center (IC3), University of Florida Health, Gainesville, FL, USA
| | - Jenna L Marquard
- School of Nursing, University of Minnesota, Minneapolis, MN, USA
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA
| | - Jessica M Ray
- Department of Health Outcomes and Biomedical Informatics, University of Florida Health, Gainesville, FL, USA
| | - Brian S Alper
- Computable Publishing LLC, Ipswich, MA, USA
- Scientific Knowledge Accelerator Foundation, Ipswich, MA, USA
| | | | - Azra Bihorac
- Intelligent Critical Care Center (IC3), University of Florida Health, Gainesville, FL, USA
- Department of Medicine, University of Florida Health, Gainesville, FL, USA
| | - Genevieve Melton-Meaux
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA
- Department of Surgery, University of Minnesota, Minneapolis, MN, USA
- Center for Learning Health Systems Science, University of Minnesota, Minneapolis, MN, USA
| | - Gopal Khanna
- Medical Industry Leadership Institute, Carlson School of Management, University of Minnesota, Minneapolis, MN, USA
| | - Christopher J Tignanelli
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA
- Department of Surgery, University of Minnesota, Minneapolis, MN, USA
- Program for Clinical Artificial Intelligence, Center for Learning Health Systems Science, University of Minnesota, Minneapolis, MN, USA
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Huang S, Liang Y, Li J, Li X. Applications of Clinical Decision Support Systems in Diabetes Care: Scoping Review. J Med Internet Res 2023; 25:e51024. [PMID: 38064249 PMCID: PMC10746969 DOI: 10.2196/51024] [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: 07/21/2023] [Revised: 11/10/2023] [Accepted: 11/12/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Providing comprehensive and individualized diabetes care remains a significant challenge in the face of the increasing complexity of diabetes management and a lack of specialized endocrinologists to support diabetes care. Clinical decision support systems (CDSSs) are progressively being used to improve diabetes care, while many health care providers lack awareness and knowledge about CDSSs in diabetes care. A comprehensive analysis of the applications of CDSSs in diabetes care is still lacking. OBJECTIVE This review aimed to summarize the research landscape, clinical applications, and impact on both patients and physicians of CDSSs in diabetes care. METHODS We conducted a scoping review following the Arksey and O'Malley framework. A search was conducted in 7 electronic databases to identify the clinical applications of CDSSs in diabetes care up to June 30, 2022. Additional searches were conducted for conference abstracts from the period of 2021-2022. Two researchers independently performed the screening and data charting processes. RESULTS Of 11,569 retrieved studies, 85 (0.7%) were included for analysis. Research interest is growing in this field, with 45 (53%) of the 85 studies published in the past 5 years. Among the 58 (68%) out of 85 studies disclosing the underlying decision-making mechanism, most CDSSs (44/58, 76%) were knowledge based, while the number of non-knowledge-based systems has been increasing in recent years. Among the 81 (95%) out of 85 studies disclosing application scenarios, the majority of CDSSs were used for treatment recommendation (63/81, 78%). Among the 39 (46%) out of 85 studies disclosing physician user types, primary care physicians (20/39, 51%) were the most common, followed by endocrinologists (15/39, 39%) and nonendocrinology specialists (8/39, 21%). CDSSs significantly improved patients' blood glucose, blood pressure, and lipid profiles in 71% (45/63), 67% (12/18), and 38% (8/21) of the studies, respectively, with no increase in the risk of hypoglycemia. CONCLUSIONS CDSSs are both effective and safe in improving diabetes care, implying that they could be a potentially reliable assistant in diabetes care, especially for physicians with limited experience and patients with limited access to medical resources. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.37766/inplasy2022.9.0061.
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Affiliation(s)
- Shan Huang
- Endocrinology Department, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuzhen Liang
- Department of Endocrinology, The Second Affiliated Hospital, Guangxi Medical University, Nanning, China
| | - Jiarui Li
- Department of Endocrinology, Cangzhou Central Hospital, Cangzhou, China
| | - Xuejun Li
- Department of Endocrinology and Diabetes, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- Xiamen Diabetes Institute, Xiamen, China
- Fujian Provincial Key Laboratory of Translational Medicine for Diabetes, Xiamen, China
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Chen Z, Liang N, Zhang H, Li H, Yang Y, Zong X, Chen Y, Wang Y, Shi N. Harnessing the power of clinical decision support systems: challenges and opportunities. Open Heart 2023; 10:e002432. [PMID: 38016787 PMCID: PMC10685930 DOI: 10.1136/openhrt-2023-002432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 10/31/2023] [Indexed: 11/30/2023] Open
Abstract
Clinical decision support systems (CDSSs) are increasingly integrated into healthcare settings to improve patient outcomes, reduce medical errors and enhance clinical efficiency by providing clinicians with evidence-based recommendations at the point of care. However, the adoption and optimisation of these systems remain a challenge. This review aims to provide an overview of the current state of CDSS, discussing their development, implementation, benefits, limitations and future directions. We also explore the potential for enhancing their effectiveness and provide an outlook for future developments in this field. There are several challenges in CDSS implementation, including data privacy concerns, system integration and clinician acceptance. While CDSS have demonstrated significant potential, their adoption and optimisation remain a challenge.
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Affiliation(s)
- Zhao Chen
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Ning Liang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Haili Zhang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Huizhen Li
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yijiu Yang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Xingyu Zong
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yaxin Chen
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yanping Wang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Nannan Shi
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
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