An effort to improve electronic health record medication list accuracy between visits: Patients' and physicians' response

Partners HealthCare System Inc., Information Systems, 93 Worcester St. Suite 201, Wellesley, MA 02481, USA.
International Journal of Medical Informatics (Impact Factor: 2). 04/2008; 77(3):153-60. DOI: 10.1016/j.ijmedinf.2007.03.001
Source: PubMed

ABSTRACT To evaluate the efficacy of a secure web-based patient portal called Patient Gateway (PG) in producing more accurate medication lists in the electronic health record (EHR), and whether sending primary care physicians (PCPs) a clinical message updating them on the information their patients provided caused physicians to update the EHR medication list.
We compared the medication list accuracy of 84 patients using PG with that of 79 who were not. Patient-reported medication discrepancies were noted in the EHR in a clinical note by research staff and a message was sent to the participants' PCPs notifying them of the updated information.
Participants were taking 665 medications according to the EHR, and reported 273 additional medications. A lower percentage of PG users' drug regimens (54% versus 61%, p=0.07) were reported to be correct than those of PG non-users, although PG users took significantly more medications than their non-user counterparts (5.0 versus 3.1 medications, p=0.0001). Providing patient-reported information in a clinical note and sending a clinical message to the primary care doctor did not result in PCPs updating their patients' EHR medication lists.
Medication lists in EHRs were frequently inaccurate and most frequently overlooked over-the-counter (OTC) and non-prescription drugs. Patients using a secure portal had just as many discrepancies between medication lists and self-report as those who did not, and notifying physicians of discrepancies via e-mail had no effect.

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Available from: David W Bates, Sep 28, 2015
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    • "Several papers mainly deal with the examination of small and specialized Consumer Healthcare systems (Cimino et al., 2002; Gustafson et al., 2002; Staroselsky et al., 2006; Reidl et al., 2008; Staroselsky et al., 2008; Britto et al., 2009) and derive factors which can improve acceptance and use by patients, or try to achieve this on purely theoretical grounds (Haux, 2006; Goth, 2008; Keselman et al., 2008; Mulder et al., 2009). The resulting factors are manifold and include, for example, ubiquity, intuitive usability, accessibility, and comprehensibility. "
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    e-Service Journal 01/2014; 9(2):1-23. DOI:10.2979/eservicej.9.2.1
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    • "Many articles assessed EHR data completeness by using another source of data as a gold standard. The gold standards used included concurrently kept paper records,12–18 information supplied by patients,19–21 review of data by patients,22–25 clinical encounters with patients,26–28 information presented by trained standard patients,29 30 information requested from the treating physician,31 and alternative data sources from which EHR elements were abstracted.32 33 A similar approach involved triangulating data from multiple sources within the EHR to create a gold standard.34 35 "
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    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.
    Journal of the American Medical Informatics Association 06/2012; 20(1). DOI:10.1136/amiajnl-2011-000681 · 3.50 Impact Factor
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    ABSTRACT: Background: A systematic review of evaluations of innovative eHealth implementations was funded by the New Zealand Ministry of Health to inform information strategy. A key trend of interest to the Ministry was person-centered healthcare, including systems where health consumers use health information technology (IT) directly. Herein we report, analyze and reflect on the review findings with respect to such systems. Objectives: To review the nature and extent of known successes of health IT with consumers as users. Methods: Queries for evaluations of innovative eHealth implementations were submitted to MEDLINE, EMBASE, PsycINFO, CINAHL and Business Source Premier for articles appearing between 2003 and early 2009 and filtered on inclusion criteria of reporting actual implementations (i.e., use), innovativeness, evaluation (interpreted generously) and scaleability. Substitutions were made where more recent superior studies of the same or closely related projects could be found. Results: 100 of 1413 retrieved articles met the inclusion criteria; 47 of these involved consumers as users of a component of the evaluated system. Systems that provided messaging between the patient and their regular care provider met with satisfaction and good uptake. There were improved chronic disease outcomes in 11 of 15 education / self-management systems and 2 of 3 home telemonitoring systems where measurement of such outcomes was reported; a further 3 systems targeting the family members of individuals with chronic conditions as principal users all showed positive well-being outcomes for the caregivers. Conclusions: There have been a number of demonstrated instances of clear successes in both uptake and outcome for health IT interventions involving consumers as users, particularly for chronic condition management. However, compelling demonstrations (in terms of methods and sample size) remain isolated. More study is needed to assess the transferability of the demonstrated successes to greater scale, diverse contexts of deployment and to other conditions. Better keywords and more systematic reporting, particularly with respect to implementation and evaluation status, would aid similar reviews in the future.
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