scholarly journals Assessing the Impact of Innovative Training of Family Physicians for the Patient-Centered Medical Home

2012 ◽  
Vol 4 (1) ◽  
pp. 16-22 ◽  
Author(s):  
Patricia A. Carney ◽  
M. Patrice Eiff ◽  
John W. Saultz ◽  
Erik Lindbloom ◽  
Elaine Waller ◽  
...  
Keyword(s):  
Medical School ◽  
Electronic Health Record ◽  
Medical Home ◽  
The United States ◽  
Health Centers ◽  
Health Record ◽  
Patient Centered Medical Home ◽  
Patient Centered ◽  
Residency Programs ◽  
Electronic Health

Abstract Background New approaches to enhance access in primary care necessitate change in the model for residency education. Purpose To describe instrument design, development and testing, and data collection strategies for residency programs, continuity clinics, residents, and program graduates participating in the Preparing the Personal Physician for Practice (P4) project. Methods We developed and pilot-tested surveys to assess demographic characteristics of residents, clinical and operational features of the continuity clinics and educational programs, and attitudes about and implementation status of Patient Centered Medical Home (PCMH) characteristics. Surveys were administered annually to P4 residency programs since the project started in 2007. Descriptive statistics were used to profile data from the P4 baseline year. Results Most P4 residents were non-Hispanic white women (60.7%), married or partnered, attended medical school in the United States and were the first physicians in their families to attend medical school. Nearly 85% of residency continuity clinics were family health centers, and about 8% were federally qualified health centers. The most likely PCMH features in continuity clinics were having an electronic health record and having fully secure remote access available; both of which were found in more than 50% of continuity clinics. Approximately one-half of continuity clinics used the electronic health record for safety projects, and nearly 60% used it for quality-improvement projects. Conclusions We created a collaborative evaluation model in all 14 P4 residencies. Successful implementation of new surveys revealed important baseline features of residencies and residents that are pertinent to studying the effects of new training models for the PCMH.

Business: The Electronic Health Record: Front Line Experience

2011 ◽  
Vol 21 (1) ◽  
pp. 18-22
Author(s):  
Rosemary Griffin
Keyword(s):  
Health Care ◽  
Information Technology ◽  
Electronic Health Record ◽  
Financial Incentives ◽  
Best Practice ◽  
Health Care Systems ◽  
The United States ◽  
Health Record ◽  
Health Trends ◽  
Electronic Health

National legislation is in place to facilitate reform of the United States health care industry. The Health Care Information Technology and Clinical Health Act (HITECH) offers financial incentives to hospitals, physicians, and individual providers to establish an electronic health record that ultimately will link with the health information technology of other health care systems and providers. The information collected will facilitate patient safety, promote best practice, and track health trends such as smoking and childhood obesity.

scholarly journals Electronic Health Record Portals and Patient-Centered Outcomes in CKD

2021 ◽  
Vol 3 (2) ◽  
pp. 167-170
Author(s):  
Soraya Arzhan ◽  
Christos Argyropoulos ◽  
Maria-Eleni Roumelioti
Keyword(s):  
Electronic Health Record ◽  
Health Record ◽  
Patient Centered ◽  
Electronic Health

scholarly journals Patient Electronic Health Record Portal Use and Patient-Centered Outcomes in CKD

2021 ◽  
Vol 3 (2) ◽  
pp. 231-240.e1 ◽  
Author(s):  
June Tome ◽  
Shahbaz Ahmed ◽  
Angela Fagerlin ◽  
Corey Powell ◽  
Marcio Mourao ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Health Record ◽  
Patient Centered ◽  
Electronic Health

scholarly journals The Development and Piloting of the Ambulatory Electronic Health Record Evaluation Tool: Lessons Learned

2021 ◽  
Vol 12 (01) ◽  
pp. 153-163
Author(s):  
Zoe Co ◽  
A. Jay Holmgren ◽  
David C. Classen ◽  
Lisa P. Newmark ◽  
Diane L. Seger ◽  
...  
Keyword(s):  
Decision Support ◽  
Electronic Health Record ◽  
Medication Safety ◽  
Medication Reconciliation ◽  
The United States ◽  
Health Record ◽  
Evaluation Tool ◽  
Safety Test ◽  
Electronic Health ◽  
The Impact

Abstract Background Substantial research has been performed about the impact of computerized physician order entry on medication safety in the inpatient setting; however, relatively little has been done in ambulatory care, where most medications are prescribed. Objective To outline the development and piloting process of the Ambulatory Electronic Health Record (EHR) Evaluation Tool and to report the quantitative and qualitative results from the pilot. Methods The Ambulatory EHR Evaluation Tool closely mirrors the inpatient version of the tool, which is administered by The Leapfrog Group. The tool was piloted with seven clinics in the United States, each using a different EHR. The tool consists of a medication safety test and a medication reconciliation module. For the medication test, clinics entered test patients and associated test orders into their EHR and recorded any decision support they received. An overall percentage score of unsafe orders detected, and order category scores were provided to clinics. For the medication reconciliation module, clinics demonstrated how their EHR electronically detected discrepancies between two medication lists. Results For the medication safety test, the clinics correctly alerted on 54.6% of unsafe medication orders. Clinics scored highest in the drug allergy (100%) and drug–drug interaction (89.3%) categories. Lower scoring categories included drug age (39.3%) and therapeutic duplication (39.3%). None of the clinics alerted for the drug laboratory or drug monitoring orders. In the medication reconciliation module, three (42.8%) clinics had an EHR-based medication reconciliation function; however, only one of those clinics could demonstrate it during the pilot. Conclusion Clinics struggled in areas of advanced decision support such as drug age, drug laboratory, and drub monitoring. Most clinics did not have an EHR-based medication reconciliation function and this process was dependent on accessing patients' medication lists. Wider use of this tool could improve outpatient medication safety and can inform vendors about areas of improvement.

scholarly journals Provider interaction with the electronic health record: The effects on patient-centered communication in medical encounters

2014 ◽  
Vol 96 (3) ◽  
pp. 315-319 ◽  
Author(s):  
Richard L. Street ◽  
Lin Liu ◽  
Neil J. Farber ◽  
Yunan Chen ◽  
Alan Calvitti ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Health Record ◽  
Patient Centered ◽  
Electronic Health

scholarly journals Clinical decision support alert malfunctions: analysis and empirically derived taxonomy

2017 ◽  
Vol 25 (5) ◽  
pp. 496-506 ◽  
Author(s):  
Adam Wright ◽  
Angela Ai ◽  
Joan Ash ◽  
Jane F Wiesen ◽  
Thu-Trang T Hickman ◽  
...  
Keyword(s):  
Decision Support ◽  
Electronic Health Record ◽  
Medical Informatics ◽  
Clinical Decision Support ◽  
Quantitative Methods ◽  
Clinical Decision ◽  
The United States ◽  
Health Record ◽  
Qualitative And Quantitative Methods ◽  
Electronic Health

Abstract Objective To develop an empirically derived taxonomy of clinical decision support (CDS) alert malfunctions. Materials and Methods We identified CDS alert malfunctions using a mix of qualitative and quantitative methods: (1) site visits with interviews of chief medical informatics officers, CDS developers, clinical leaders, and CDS end users; (2) surveys of chief medical informatics officers; (3) analysis of CDS firing rates; and (4) analysis of CDS overrides. We used a multi-round, manual, iterative card sort to develop a multi-axial, empirically derived taxonomy of CDS malfunctions. Results We analyzed 68 CDS alert malfunction cases from 14 sites across the United States with diverse electronic health record systems. Four primary axes emerged: the cause of the malfunction, its mode of discovery, when it began, and how it affected rule firing. Build errors, conceptualization errors, and the introduction of new concepts or terms were the most frequent causes. User reports were the predominant mode of discovery. Many malfunctions within our database caused rules to fire for patients for whom they should not have (false positives), but the reverse (false negatives) was also common. Discussion Across organizations and electronic health record systems, similar malfunction patterns recurred. Challenges included updates to code sets and values, software issues at the time of system upgrades, difficulties with migration of CDS content between computing environments, and the challenge of correctly conceptualizing and building CDS. Conclusion CDS alert malfunctions are frequent. The empirically derived taxonomy formalizes the common recurring issues that cause these malfunctions, helping CDS developers anticipate and prevent CDS malfunctions before they occur or detect and resolve them expediently.

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