The following are key points to remember about this JACC Heart Failure State-of-the-Art Review on the electronic health record (EHR) and heart failure (HF) care:

1. Throughout the review, authors follow a hypothetical patient and compare care and outcomes utilizing minimal EHR tools with those using optimal EHR tools.
2. Various features of the EHR can promote adherence with evidence-based guidelines, optimize patient engagement, and improve efficiency in coordination of care. EHR tools can also facilitate population health management, clinical research, and continuous system improvement.
3. Patient engagement using EHR tools can improve outcomes by empowering self-management and facilitating remote access to clinicians. E-mail communications (including images), documentation of phone calls, and patient portals offering educational materials facilitate engagement. Video visits can facilitate interaction with homebound patients. Telemonitoring can capture patient-entered or biometric data.
4. Documentation using structured EHR notes might pre-populate data on patient weights, left ventricular ejection fraction, medications, and risk scores. Speech recognition tools can accelerate entry of information. Disease-specific templates can improve efficiency of documentation, reduce errors, and standardize formats for easier review by other clinicians.
5. Data analytics and reports generated by EHRs are more accurate and can be obtained with much greater speed than reports compiled by manual efforts. Reports can aid in predictive modeling.
6. Retrospective analytics compile historical data summaries and can show trends in a patient or population. Adherence to guideline-directed medical therapy of HF improves when summary reports of patient panels—whether one clinician or an entire practice—are included as part of a multidisciplinary quality-improvement program.
7. Predictive analytics can identify patients with new onset HF, activate EHR alerts, or trigger consultations. They can predict patients at highest risk who would derive greatest benefit from limited resources. Complex predictive models are being developed by EHR vendors, but institutions may develop their own customized models.
8. Clinical decision-support tools aid in patient management decisions. Those that promote guideline adherence can improve survival in severe HF, partly by prompting referrals for advanced therapy.
9. Prespecified order sets can improve clinician efficiency. Triggered alerts can recommend interventions such as left ventricular ejection fraction assessment or referral for implantable cardioverter defibrillator.
10. Complex applications might include more advanced clinical decision-support tools, care pathways, patient registries, institutional quality improvement measures, clinical trials screening, or tracking of research participants.
11. In summary, EHRs provide a wide variety of tools that can result in improved patient outcomes. Best evidence supports use of analytics for identifying cases, stratifying risk, and creating HF-specific dashboards.

Clinical Topics: Heart Failure and Cardiomyopathies, Acute Heart Failure

Keywords: Electronic Health Records, Heart Failure, Patient Participation, Decision Support Systems, Clinical, Documentation, Guideline Adherence, Quality Improvement

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