EHR implementation without meaningful use can lead to worse outcomes.
(2/29)
Defying expectations, typical electronic health record (EHR) use in practices belonging to a primary care network has been associated with poorer diabetes care quality and outcomes. Current expansion of primary care EHR implementation must focus on use that improves care. (+info)
Leveraging standards to support patient-centric interdisciplinary plans of care.
(3/29)
As health care systems and providers move towards meaningful use of electronic health records, the once distant vision of collaborative patient-centric, interdisciplinary plans of care, generated and updated across organizations and levels of care, may soon become a reality. Effective care planning is included in the proposed Stages 2-3 Meaningful Use quality measures. To facilitate interoperability, standardization of plan of care messaging, content, information and terminology models are needed. This degree of standardization requires local and national coordination. The purpose of this paper is to review some existing standards that may be leveraged to support development of interdisciplinary patient-centric plans of care. Standards are then applied to a use case to demonstrate one method for achieving patient-centric and interoperable interdisciplinary plan of care documentation. Our pilot work suggests that existing standards provide a foundation for adoption and implementation of patient-centric plans of care that are consistent with federal requirements. (+info)
Using a unified usability framework to dramatically improve the usability of an EMR Module.
(4/29)
Electronic Medical Records (EMRs) are increasingly used in modern health care. As a result, systematically applying usability principles becomes increasingly vital in creating systems that provide health care professionals with satisfying, efficient, and effective user experiences, as opposed to frustrating interfaces that are difficult to learn, hard to use, and error prone. This study demonstrates how the TURF framework [1] can be used to evaluate the usability of an EMR module and subsequently redesign its interface with dramatically improved usability in a unified, systematic, and principled way. This study also shows how heuristic evaluations can be utilized to complement the TURF framework. (+info)
Health information exchange, Health Information Technology use, and hospital readmission rates.
(5/29)
The Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 offers significant financial incentives to hospitals that can demonstrate "meaningful use" of EHRs. Reduced hospital readmissions are an expected outcome of improved care coordination. Increased use of HIT, and in particular participation in HIE are touted as ways to improve coordination of care. In a 2007 national sample of US hospitals, we evaluated the association between hospitals' HIE and HIT use and 30-day risk adjusted readmission rates for acute myocardial infarction (AMI), heart failure, and pneumonia. We found that hospital participation in HIE was not associated with lower hospital readmission rates; however, high levels of electronic documentation (an aspect of HIT use) were associated with modest reductions in readmission for heart failure (24.6% vs. 24.1%, P=.02) and pneumonia (18.4% vs. 17.9%, P=.003). More detailed data on participation in HIE are necessary to conduct more robust assessment of the relationship between HIE and hospital readmission rates. (+info)
Automatically detecting problem list omissions of type 2 diabetes cases using electronic medical records.
(6/29)
As part of a large-scale project to use DNA biorepositories linked with electronic medical record (EMR) data for research, we developed and validated an algorithm to identify type 2 diabetes cases in the EMR. Though the algorithm was originally created to support clinical research, we have subsequently re-applied it to determine if it could also be used to identify problem list gaps. We examined the problem lists of the cases that the algorithm identified in order to determine if a structured code for diabetes was present. We found that only just over half of patients identified by the algorithm had a corresponding structured code entered in their problem list. We analyze characteristics of this patient population and identify possible reasons for the problem list omissions. We conclude that application of such algorithms to the EMR can improve the quality of the problem list, thereby supporting satisfaction of Meaningful Use guidelines. (+info)
Improving completeness of electronic problem lists through clinical decision support: a randomized, controlled trial.
(7/29)