scholarly journals Validation of an Electronic Health Record–Based Suicide Risk Prediction Modeling Approach Across Multiple Health Care Systems

2020 ◽  
Vol 3 (3) ◽  
pp. e201262 ◽  
Author(s):  
Yuval Barak-Corren ◽  
Victor M. Castro ◽  
Matthew K. Nock ◽  
Kenneth D. Mandl ◽  
Emily M. Madsen ◽  
...  
Keyword(s):  
Health Care ◽  
Electronic Health Record ◽  
Risk Prediction ◽  
Suicide Risk ◽  
Health Care Systems ◽  
Health Record ◽  
Prediction Modeling ◽  
Modeling Approach ◽  
Care Systems ◽  
Multiple Health

scholarly journals An efficient approach for surveillance of childhood diabetes by type derived from electronic health record data: the SEARCH for Diabetes in Youth Study

2016 ◽  
Vol 23 (6) ◽  
pp. 1060-1067 ◽  
Author(s):  
Victor W Zhong ◽  
Jihad S Obeid ◽  
Jean B Craig ◽  
Emily R Pfaff ◽  
Joan Thomas ◽  
...  
Keyword(s):  
Health Care ◽  
Electronic Health Record ◽  
Health Care Systems ◽  
Diabetes Type ◽  
Health Record ◽  
Childhood Diabetes ◽  
Electronic Health Record Data ◽  
Efficient Approach ◽  
Care Systems ◽  
Electronic Health

Abstract Objective To develop an efficient surveillance approach for childhood diabetes by type across 2 large US health care systems, using phenotyping algorithms derived from electronic health record (EHR) data. Materials and Methods Presumptive diabetes cases <20 years of age from 2 large independent health care systems were identified as those having ≥1 of the 5 indicators in the past 3.5 years, including elevated HbA1c, elevated blood glucose, diabetes-related billing codes, patient problem list, and outpatient anti-diabetic medications. EHRs of all the presumptive cases were manually reviewed, and true diabetes status and diabetes type were determined. Algorithms for identifying diabetes cases overall and classifying diabetes type were either prespecified or derived from classification and regression tree analysis. Surveillance approach was developed based on the best algorithms identified. Results We developed a stepwise surveillance approach using billing code–based prespecified algorithms and targeted manual EHR review, which efficiently and accurately ascertained and classified diabetes cases by type, in both health care systems. The sensitivity and positive predictive values in both systems were approximately ≥90% for ascertaining diabetes cases overall and classifying cases with type 1 or type 2 diabetes. About 80% of the cases with “other” type were also correctly classified. This stepwise surveillance approach resulted in a >70% reduction in the number of cases requiring manual validation compared to traditional surveillance methods. Conclusion EHR data may be used to establish an efficient approach for large-scale surveillance for childhood diabetes by type, although some manual effort is still needed.

scholarly journals eSyM: An Electronic Health Record–Integrated Patient-Reported Outcomes–Based Cancer Symptom Management Program Used by Six Diverse Health Systems

2022 ◽  
Author(s):  
Michael J. Hassett ◽  
Christine Cronin ◽  
Terrence C. Tsou ◽  
Jason Wedge ◽  
Jessica Bian ◽  
...  
Keyword(s):  
Health Care ◽  
Electronic Health Record ◽  
Symptom Management ◽  
Patient Reported Outcomes ◽  
Health Care Systems ◽  
Management Program ◽  
Health Record ◽  
Patient Reported ◽  
Care Systems ◽  
Electronic Health

PURPOSE Collecting patient-reported outcomes (PROs) can improve symptom control and quality of life, enhance doctor-patient communication, and reduce acute care needs for patients with cancer. Digital solutions facilitate PRO collection, but without robust electronic health record (EHR) integration, effective deployment can be hampered by low patient and clinician engagement and high development and deployment costs. The important components of digital PRO platforms have been defined, but procedures for implementing integrated solutions are not readily available. METHODS As part of the NCI's IMPACT consortium, six health care systems partnered with Epic to develop an EHR-integrated, PRO-based electronic symptom management program (eSyM) to optimize postoperative recovery and well-being during chemotherapy. The agile development process incorporated user-centered design principles that required engagement from patients, clinicians, and health care systems. Whenever possible, the system used validated content from the public domain and took advantage of existing EHR capabilities to automate processes. RESULTS eSyM includes symptom surveys on the basis of the PRO-Common Terminology Criteria for Adverse Events (PRO-CTCAE) plus two global wellness questions; reminders and symptom self-management tip sheets for patients; alerts and symptom reports for clinicians; and population management dashboards. EHR dependencies include a secure Health Insurance Portability and Accountability Act-compliant patient portal; diagnosis, procedure and chemotherapy treatment plan data; registries that identify and track target populations; and the ability to create reminders, alerts, reports, dashboards, and charting shortcuts. CONCLUSION eSyM incorporates validated content and leverages existing EHR capabilities. Build challenges include the innate technical limitations of the EHR, the constrained availability of site technical resources, and sites' heterogenous EHR configurations and policies. Integration of PRO-based symptom management programs into the EHR could help overcome adoption barriers, consolidate clinical workflows, and foster scalability and sustainability. We intend to make eSyM available to all Epic users.

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.

Transitioning from one Electronic Health Record (EHR) to Another: A Narrative Literature Review

2018 ◽  
Vol 62 (1) ◽  
pp. 489-493 ◽  
Author(s):  
Jason J. Saleem ◽  
Jennifer Herout
Keyword(s):  
Health Care ◽  
Literature Review ◽  
Electronic Health Record ◽  
Health Care Systems ◽  
Health Care Organization ◽  
Data Migration ◽  
Health Record ◽  
Care Organization ◽  
Healthcare Organizations ◽  
Electronic Health

This paper reports the results of a literature review of health care organizations that have transitioned from one electronic health record (EHR) to another. Ten different EHR to EHR transitions are documented in the academic literature. In eight of the 10 transitions, the health care organization transitioned to Epic, a commercial EHR which is dominating the market for large and medium hospitals and health care systems. The focus of the articles reviewed falls into two main categories: (1) data migration from the old to new EHR and (2) implementation of the new EHR as it relates to patient safety, provider satisfaction, and other measures pre-and post-transition. Several conclusions and recommendations are derived from this review of the literature, which may be informative for healthcare organizations preparing to replace an existing EHR. These recommendations are likely broadly relevant to EHR to EHR transitions, regardless of the new EHR vendor.

scholarly journals From screening to ascertainment of the primary outcome using electronic health records: Challenges in the STRIDE trial

2020 ◽  
Vol 17 (4) ◽  
pp. 346-350
Author(s):  
Denise Esserman
Keyword(s):  
Electronic Health Records ◽  
Electronic Health Record ◽  
Health Care Systems ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Health Records ◽  
Record Data ◽  
Care Systems ◽  
Electronic Health ◽  
Data Elements

Electronic health record data are a rich resource and can be utilized to answer a wealth of research questions. It is important when using electronic health record data in clinical trials that systems be put in place and vetted prior to enrollment to ensure data elements can be collected consistently across all health care systems. It is often overlooked how something conceptualized on paper (e.g. use of the electronic health record in a study) can be difficult to implement in practice. This article discusses some of the challenges in using electronic health records in the conduct of the STRIDE (Strategies to Reduce Injuries and Develop Confidence in Elders) trial, how we handled those challenges, and the lessons we learned for the conduct of future trials looking to employ the electronic health record.

scholarly journals Differences, Opportunities, and Strategies in Drug Alert Optimization—Experiences of Two Different Integrated Health Care Systems

2019 ◽  
Vol 10 (05) ◽  
pp. 777-782 ◽  
Author(s):  
Salim M. Saiyed ◽  
Katherine R. Davis ◽  
David C. Kaelber
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Systematic Approach ◽  
Drug Information ◽  
Health Care Systems ◽  
Clinical Decision ◽  
Health Record ◽  
Alarm Fatigue ◽  
Care Systems ◽  
Guidelines And Recommendations

Abstract Background Concerns about the number of automated medication alerts issued within the electronic health record (EHR), and the subsequent potential for alarm fatigue, led us to examine strategies and methods to optimize the configuration of our drug alerts. Objectives This article reports on comprehensive drug alerting rates and develops strategies across two different health care systems to reduce the number of drug alerts. Methods Standardized reports compared drug alert rates between the two systems, among 13 categories of drug alerts. Both health care systems made modifications to the out-of-box alerts available from their EHR and drug information vendors, focusing on system-wide approaches, when relevant, while performing more drug-specific changes when necessary. Results Drug alerting rates even after initial optimization were 38 alerts and 51 alerts per 100 drug orders, respectively. Eight principles were identified and developed to reflect the themes in the implementation and optimization of drug alerting. Conclusion A team-based, systematic approach to optimizing drug-alerting strategies can reduce the number of drug alerts, but alert rates still remain high. In addition to strategic principles, additional tactical guidelines and recommendations need to be developed to enhance out-of-the-box clinical decision support for drug alerts.

scholarly journals Identifying Opioid Use Disorder in the Emergency Department: Multi-System Electronic Health Record–Based Computable Phenotype Derivation and Validation Study

10.2196/15794 ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. e15794 ◽  
Author(s):  
David Chartash ◽  
Hyung Paek ◽  
James D Dziura ◽  
Bill K Ross ◽  
Daniel P Nogee ◽  
...  
Keyword(s):  
Health Care ◽  
Emergency Department ◽  
Health Care Systems ◽  
External Validation ◽  
Opioid Use Disorder ◽  
Health Record ◽  
Opioid Use ◽  
Internal Validation ◽  
Care Systems ◽  
Emergency Department Patients

Background Deploying accurate computable phenotypes in pragmatic trials requires a trade-off between precise and clinically sensical variable selection. In particular, evaluating the medical encounter to assess a pattern leading to clinically significant impairment or distress indicative of disease is a difficult modeling challenge for the emergency department. Objective This study aimed to derive and validate an electronic health record–based computable phenotype to identify emergency department patients with opioid use disorder using physician chart review as a reference standard. Methods A two-algorithm computable phenotype was developed and evaluated using structured clinical data across 13 emergency departments in two large health care systems. Algorithm 1 combined clinician and billing codes. Algorithm 2 used chief complaint structured data suggestive of opioid use disorder. To evaluate the algorithms in both internal and external validation phases, two emergency medicine physicians, with a third acting as adjudicator, reviewed a pragmatic sample of 231 charts: 125 internal validation (75 positive and 50 negative), 106 external validation (56 positive and 50 negative). Results Cohen kappa, measuring agreement between reviewers, for the internal and external validation cohorts was 0.95 and 0.93, respectively. In the internal validation phase, Algorithm 1 had a positive predictive value (PPV) of 0.96 (95% CI 0.863-0.995) and a negative predictive value (NPV) of 0.98 (95% CI 0.893-0.999), and Algorithm 2 had a PPV of 0.8 (95% CI 0.593-0.932) and an NPV of 1.0 (one-sided 97.5% CI 0.863-1). In the external validation phase, the phenotype had a PPV of 0.95 (95% CI 0.851-0.989) and an NPV of 0.92 (95% CI 0.807-0.978). Conclusions This phenotype detected emergency department patients with opioid use disorder with high predictive values and reliability. Its algorithms were transportable across health care systems and have potential value for both clinical and research purposes.

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