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Marani H, Halperin IJ, Jamieson T, Mukerji G. Quality Gaps of Electronic Health Records in Diabetes Care. Can J Diabetes 2020; 44:350-355. [DOI: 10.1016/j.jcjd.2019.10.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 10/28/2019] [Accepted: 10/29/2019] [Indexed: 11/24/2022]
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Abstract
Introduction: We describe the formulation, development, and initial expert review of 3x3 Data Quality Assessment (DQA), a dynamic, evidence-based guideline to enable electronic health record (EHR) data quality assessment and reporting for clinical research. Methods: 3x3 DQA was developed through the triangulation results from three studies: a review of the literature on EHR data quality assessment, a quantitative study of EHR data completeness, and a set of interviews with clinical researchers. Following initial development, the guideline was reviewed by a panel of EHR data quality experts. Results: The guideline embraces the task-dependent nature of data quality and data quality assessment. The core framework includes three constructs of data quality: complete, correct, and current data. These constructs are operationalized according to the three primary dimensions of EHR data: patients, variables, and time. Each of the nine operationalized constructs maps to a methodological recommendation for EHR data quality assessment. The initial expert response to the framework was positive, but improvements are required. Discussion: The initial version of 3x3 DQA promises to enable explicit guideline-based best practices for EHR data quality assessment and reporting. Future work will focus on increasing clarity on how and when 3x3 DQA should be used during the research process, improving the feasibility and ease-of-use of recommendation execution, and clarifying the process for users to determine which operationalized constructs and recommendations are relevant for a given dataset and study.
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Luna FG, Contreras IH, Guerrero AC, Guitarte FB. Integrating Electronic Systems for Requesting Clinical Laboratory Test into Digital Clinical Records: Design and Implementation. Health (London) 2017. [DOI: 10.4236/health.2017.94044] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Abstract
OBJECTIVES Describe the state of Electronic Health Records (EHRs) in 1992 and their evolution by 2015 and where EHRs are expected to be in 25 years. Further to discuss the expectations for EHRs in 1992 and explore which of them were realized and what events accelerated or disrupted/derailed how EHRs evolved. METHODS Literature search based on "Electronic Health Record", "Medical Record", and "Medical Chart" using Medline, Google, Wikipedia Medical, and Cochrane Libraries resulted in an initial review of 2,356 abstracts and other information in papers and books. Additional papers and books were identified through the review of references cited in the initial review. RESULTS By 1992, hardware had become more affordable, powerful, and compact and the use of personal computers, local area networks, and the Internet provided faster and easier access to medical information. EHRs were initially developed and used at academic medical facilities but since most have been replaced by large vendor EHRs. While EHR use has increased and clinicians are being prepared to practice in an EHR-mediated world, technical issues have been overshadowed by procedural, professional, social, political, and especially ethical issues as well as the need for compliance with standards and information security. There have been enormous advancements that have taken place, but many of the early expectations for EHRs have not been realized and current EHRs still do not meet the needs of today's rapidly changing healthcare environment. CONCLUSION The current use of EHRs initiated by new technology would have been hard to foresee. Current and new EHR technology will help to provide international standards for interoperable applications that use health, social, economic, behavioral, and environmental data to communicate, interpret, and act intelligently upon complex healthcare information to foster precision medicine and a learning health system.
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Affiliation(s)
- R S Evans
- R. Scott Evans, MS, PhD, FACMI, Department of Medical Informatics, LDS Hospital, 8th Ave & C Street, Salt Lake City, Utah 84143, USA, Tel: +1 801 408-3029, Fax: +1 801 408-5802, E-mail:
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Jacobs J, Narus SP, Evans RS, Staes CJ. Longitudinal Analysis of Computerized Alerts for Laboratory Monitoring of Post-liver Transplant Immunosuppressive Care. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2015; 2015:1918-1926. [PMID: 26958291 PMCID: PMC4765651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Post-liver transplant patients require lifelong immunosuppressive care and monitoring. Computerized alerts can aid laboratory monitoring, but it is unknown how the distribution of alerts changes over time. We describe the changes over time of the distribution of computerized alerts for laboratory monitoring of post-liver transplant immunosuppressive care. Data were collected for post-liver transplant patients transplanted and managed at Intermountain Healthcare between 2005 and 2012. Alerts were analyzed based on year triggered, time since transplantation, hospitalization status, alert type, action taken (accepted or rejected), reason given for the action taken, and narrative comments. Alerts for overdue laboratory testing became more prevalent as time since transplantation increased. There is an increased need to support monitoring for overdue laboratory testing as the time since transplantation increases. Alerts should support providers as they monitor the evolving needs of post-transplant patients over time. We identify opportunities for improving laboratory monitoring of post-liver transplant patients.
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Affiliation(s)
- Jason Jacobs
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah
| | - Scott P Narus
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah; Intermountain Healthcare, Salt Lake City, Utah
| | - R Scott Evans
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah; Intermountain Healthcare, Salt Lake City, Utah
| | - Catherine J Staes
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah
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Jacobs J, Weir C, Evans RS, Staes C. Assessment of readiness for clinical decision support to aid laboratory monitoring of immunosuppressive care at U.S. liver transplant centers. Appl Clin Inform 2014; 5:988-1004. [PMID: 25589912 DOI: 10.4338/aci-2014-08-ra-0060] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2014] [Accepted: 11/16/2014] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Following liver transplantation, patients require lifelong immunosuppressive care and monitoring. Computerized clinical decision support (CDS) has been shown to improve post-transplant immunosuppressive care processes and outcomes. The readiness of transplant information systems to implement computerized CDS to support post-transplant care is unknown. OBJECTIVES a) Describe the current clinical information system functionality and manual and automated processes for laboratory monitoring of immunosuppressive care, b) describe the use of guidelines that may be used to produce computable logic and the use of computerized alerts to support guideline adherence, and c) explore barriers to implementation of CDS in U.S. liver transplant centers. METHODS We developed a web-based survey using cognitive interviewing techniques. We surveyed 119 U.S. transplant programs that performed at least five liver transplantations per year during 2010-2012. Responses were summarized using descriptive analyses; barriers were identified using qualitative methods. RESULTS Respondents from 80 programs (67% response rate) completed the survey. While 98% of programs reported having an electronic health record (EHR), all programs used paper-based manual processes to receive or track immunosuppressive laboratory results. Most programs (85%) reported that 30% or more of their patients used external laboratories for routine testing. Few programs (19%) received most external laboratory results as discrete data via electronic interfaces while most (80%) manually entered laboratory results into the EHR; less than half (42%) could integrate internal and external laboratory results. Nearly all programs had guidelines regarding pre-specified target ranges (92%) or testing schedules (97%) for managing immunosuppressive care. Few programs used computerized alerting to notify transplant coordinators of out-of-range (27%) or overdue laboratory results (20%). CONCLUSIONS Use of EHRs is common, yet all liver transplant programs were largely dependent on manual paper-based processes to monitor immunosuppression for post-liver transplant patients. Similar immunosuppression guidelines provide opportunities for sharing CDS once integrated laboratory data are available.
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Affiliation(s)
- J Jacobs
- Department of Biomedical Informatics, University of Utah , Salt Lake City, Utah, USA
| | - C Weir
- Department of Biomedical Informatics, University of Utah , Salt Lake City, Utah, USA
| | - R S Evans
- Department of Biomedical Informatics, University of Utah , Salt Lake City, Utah, USA ; Medical Informatics, Intermountain Healthcare , Salt Lake City, Utah, USA
| | - C Staes
- Department of Biomedical Informatics, University of Utah , Salt Lake City, Utah, USA
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Adane K, Muluye D, Abebe M. Processing medical data: a systematic review. ACTA ACUST UNITED AC 2013; 71:27. [PMID: 24107106 PMCID: PMC3876722 DOI: 10.1186/0778-7367-71-27] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2013] [Accepted: 10/02/2013] [Indexed: 12/02/2022]
Abstract
Background Medical data recording is one of the basic clinical tools. Electronic Health Record (EHR) is important for data processing, communication, efficiency and effectiveness of patients’ information access, confidentiality, ethical and/or legal issues. Clinical record promote and support communication among service providers and hence upscale quality of healthcare. Qualities of records are reflections of the quality of care patients offered. Methods Qualitative analysis was undertaken for this systematic review. We reviewed 40 materials Published from 1999 to 2013. We searched these materials from databases including ovidMEDLINE and ovidEMBASE. Two reviewers independently screened materials on medical data recording, documentation and information processing and communication. Finally, all selected references were summarized, reconciled and compiled as one compatible document. Result Patients were dying and/or getting much suffering as the result of poor quality medical records. Electronic health record minimizes errors, saves unnecessary time, and money wasted on processing medical data. Conclusion Many countries have been complaining for incompleteness, inappropriateness and illegibility of records. Therefore creating awareness on the magnitude of the problem has paramount importance. Hence available correct patient information has lots of potential in reducing errors and support roles.
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Affiliation(s)
- Kasaw Adane
- College of Medicine and Health Sciences, School of Biomedical and Laboratory Sciences, Unit of Laboratory Management and Quality Assurance, University of Gondar, Gondar, Ethiopia.
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Gascón F, Herrera I, Vázquez C, Jiménez P, Jiménez J, Real C, Pérez F. Electronic health record: Design and implementation of a lab test request module. Int J Med Inform 2013; 82:514-21. [DOI: 10.1016/j.ijmedinf.2013.03.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2012] [Revised: 03/16/2013] [Accepted: 03/16/2013] [Indexed: 10/26/2022]
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Longhurst CA, Palma JP, Grisim LM, Widen E, Chan M, Sharek PJ. Using an Evidence-Based Approach to EMR Implementation to Optimize Outcomes and Avoid Unintended Consequences. JOURNAL OF HEALTHCARE INFORMATION MANAGEMENT : JHIM 2013; 27:79-83. [PMID: 24771994 PMCID: PMC3998198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Implementation of an electronic medical record (EMR) with computerized physician order entry (CPOE) can provide an important foundation for preventing harm and improving outcomes. Incentivized by the recent economic stimulus initiative, healthcare systems are implementing vendor-based EMR systems at an unprecedented rate. Accumulating evidence suggests that local implementation decisions, rather than the specific EMR product or technology selected, are the primary drivers of the quality improvement performance of these systems. However, limited attention has been paid to effective approaches to EMR implementation. In this case report, we outline the evidence-based approach we used to make EMR implementation decisions in a pragmatic structure intended for replication at other sites.
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Affiliation(s)
- Christopher A Longhurst
- Lucile Packard Children's Hospital and a Clinical Associate Professor of Pediatrics at Stanford University School of Medicine
| | - Jonathan P Palma
- Lucile Packard Children's Hospital and a Clinical Assistant Professor of Neonatology at Stanford University School of Medicine
| | | | - Eric Widen
- Lucile Packard Children's Hospital at the time of this work
| | | | - Paul J Sharek
- Lucile Packard Children's Hospital and an Associate Professor of Pediatrics at Stanford University School of Medicine
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Weiskopf NG, Weng C. Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. J Am Med Inform Assoc 2013; 20:144-51. [PMID: 22733976 PMCID: PMC3555312 DOI: 10.1136/amiajnl-2011-000681] [Citation(s) in RCA: 560] [Impact Index Per Article: 50.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2011] [Accepted: 05/03/2012] [Indexed: 11/12/2022] Open
Abstract
OBJECTIVE To review the methods and dimensions of data quality assessment in the context of electronic health record (EHR) data reuse for research. MATERIALS AND METHODS A review of the clinical research literature discussing data quality assessment methodology for EHR data was performed. Using an iterative process, the aspects of data quality being measured were abstracted and categorized, as well as the methods of assessment used. RESULTS Five dimensions of data quality were identified, which are completeness, correctness, concordance, plausibility, and currency, and seven broad categories of data quality assessment methods: comparison with gold standards, data element agreement, data source agreement, distribution comparison, validity checks, log review, and element presence. DISCUSSION Examination of the methods by which clinical researchers have investigated the quality and suitability of EHR data for research shows that there are fundamental features of data quality, which may be difficult to measure, as well as proxy dimensions. Researchers interested in the reuse of EHR data for clinical research are recommended to consider the adoption of a consistent taxonomy of EHR data quality, to remain aware of the task-dependence of data quality, to integrate work on data quality assessment from other fields, and to adopt systematic, empirically driven, statistically based methods of data quality assessment. CONCLUSION There is currently little consistency or potential generalizability in the methods used to assess EHR data quality. If the reuse of EHR data for clinical research is to become accepted, researchers should adopt validated, systematic methods of EHR data quality assessment.
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Affiliation(s)
- Nicole Gray Weiskopf
- Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA.
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Chan KS, Fowles JB, Weiner JP. Review: electronic health records and the reliability and validity of quality measures: a review of the literature. Med Care Res Rev 2010; 67:503-27. [PMID: 20150441 DOI: 10.1177/1077558709359007] [Citation(s) in RCA: 234] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Previous reviews of research on electronic health record (EHR) data quality have not focused on the needs of quality measurement. The authors reviewed empirical studies of EHR data quality, published from January 2004, with an emphasis on data attributes relevant to quality measurement. Many of the 35 studies reviewed examined multiple aspects of data quality. Sixty-six percent evaluated data accuracy, 57% data completeness, and 23% data comparability. The diversity in data element, study setting, population, health condition, and EHR system studied within this body of literature made drawing specific conclusions regarding EHR data quality challenging. Future research should focus on the quality of data from specific EHR components and important data attributes for quality measurement such as granularity, timeliness, and comparability. Finally, factors associated with poor or variability in data quality need to be better understood and effective interventions developed.
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Affiliation(s)
- Kitty S Chan
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
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Reti SR, Feldman HJ, Safran C. Governance for personal health records. J Am Med Inform Assoc 2009; 16:14-7. [PMID: 18952939 PMCID: PMC2605603 DOI: 10.1197/jamia.m2854] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2008] [Accepted: 09/21/2008] [Indexed: 11/10/2022] Open
Abstract
Personal health records (PHR) are a modern health technology with the ability to engage patients more fully in their healthcare. Despite widespread interest, there has been little discussion around PHR governance at an organizational level. We develop a governance model and compare it to the practices of some of the early PHR adopters, including hospitals and ambulatory care settings, insurers and health plans, government departments, and commercial sectors. Decision-making structures varied between organizations. Business operations were present in all groups, but patients were not represented in any of the governance structures surveyed. To improve patient-centered care, policy making for PHRs needs to include patient representation at a governance level.
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Affiliation(s)
- Shane R Reti
- Division of Clinical Informatics, Department of Medicine, Beth Israel Deaconess Medical Center, 1330 Beacon Street, Suite 400, Brookline, MA 02446, USA.
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Staes CJ, Evans RS, Rocha BHSC, Sorensen JB, Huff SM, Arata J, Narus SP. Computerized alerts improve outpatient laboratory monitoring of transplant patients. J Am Med Inform Assoc 2008; 15:324-32. [PMID: 18308982 PMCID: PMC2410008 DOI: 10.1197/jamia.m2608] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2007] [Accepted: 01/22/2008] [Indexed: 11/10/2022] Open
Abstract
Authors evaluated the impact of computerized alerts on the quality of outpatient laboratory monitoring for transplant patients. For 356 outpatient liver transplant patients managed at LDS Hospital, Salt Lake City, this observational study compared traditional laboratory result reporting, using faxes and printouts, to computerized alerts implemented in 2004. Study alerts within the electronic health record notified clinicians of new results and overdue new orders for creatinine tests and immunosuppression drug levels. After implementing alerts, completeness of reporting increased from 66 to >99 %, as did positive predictive value that a report included new information (from 46 to >99 %). Timeliness of reporting and clinicians' responses improved after implementing alerts (p <0.001): median times for clinicians to receive and complete actions decreased to 9 hours from 33 hours using the prior traditional reporting system. Computerized alerts led to more efficient, complete, and timely management of laboratory information.
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Affiliation(s)
- Catherine J Staes
- Department of Biomedical Informatics, University of Utah School of Medicine, (RSE, SMH, SPN, CJS), Salt Lake City, UT,USA.
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Biron P, Thiesse P, Becker F, Perrossier M, Durand T. [How to manage medical imaging performed outside the hospital?]. JOURNAL DE RADIOLOGIE 2008; 89:521-523. [PMID: 18477962 DOI: 10.1016/s0221-0363(08)71459-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Affiliation(s)
- P Biron
- Département de Médecine Oncologique, Chef de Projet Dossier Patient Informatisé, Centre Régional Léon Bérard, France.
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Barrett JS, Mondick JT, Narayan M, Vijayakumar K, Vijayakumar S. Integration of modeling and simulation into hospital-based decision support systems guiding pediatric pharmacotherapy. BMC Med Inform Decis Mak 2008; 8:6. [PMID: 18226244 PMCID: PMC2254609 DOI: 10.1186/1472-6947-8-6] [Citation(s) in RCA: 51] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2007] [Accepted: 01/28/2008] [Indexed: 11/23/2022] Open
Abstract
Background Decision analysis in hospital-based settings is becoming more common place. The application of modeling and simulation approaches has likewise become more prevalent in order to support decision analytics. With respect to clinical decision making at the level of the patient, modeling and simulation approaches have been used to study and forecast treatment options, examine and rate caregiver performance and assign resources (staffing, beds, patient throughput). There us a great need to facilitate pharmacotherapeutic decision making in pediatrics given the often limited data available to guide dosing and manage patient response. We have employed nonlinear mixed effect models and Bayesian forecasting algorithms coupled with data summary and visualization tools to create drug-specific decision support systems that utilize individualized patient data from our electronic medical records systems. Methods Pharmacokinetic and pharmacodynamic nonlinear mixed-effect models of specific drugs are generated based on historical data in relevant pediatric populations or from adults when no pediatric data is available. These models are re-executed with individual patient data allowing for patient-specific guidance via a Bayesian forecasting approach. The models are called and executed in an interactive manner through our web-based dashboard environment which interfaces to the hospital's electronic medical records system. Results The methotrexate dashboard utilizes a two-compartment, population-based, PK mixed-effect model to project patient response to specific dosing events. Projected plasma concentrations are viewable against protocol-specific nomograms to provide dosing guidance for potential rescue therapy with leucovorin. These data are also viewable against common biomarkers used to assess patient safety (e.g., vital signs and plasma creatinine levels). As additional data become available via therapeutic drug monitoring, the model is re-executed and projections are revised. Conclusion The management of pediatric pharmacotherapy can be greatly enhanced via the immediate feedback provided by decision analytics which incorporate the current, best-available knowledge pertaining to dose-exposure and exposure-response relationships, especially for narrow therapeutic agents that are difficult to manage.
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Affiliation(s)
- Jeffrey S Barrett
- Department of Pediatrics, Division of Clinical Pharmacology and Therapeutics, The Children's Hospital of Philadelphia, USA.
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