A 'green button' for using aggregate patient data at the point of care

Health Aff (Millwood). 2014 Jul;33(7):1229-35. doi: 10.1377/hlthaff.2014.0099.


Randomized controlled trials have traditionally been the gold standard against which all other sources of clinical evidence are measured. However, the cost of conducting these trials can be prohibitive. In addition, evidence from the trials frequently rests on narrow patient-inclusion criteria and thus may not generalize well to real clinical situations. Given the increasing availability of comprehensive clinical data in electronic health records (EHRs), some health system leaders are now advocating for a shift away from traditional trials and toward large-scale retrospective studies, which can use practice-based evidence that is generated as a by-product of clinical processes. Other thought leaders in clinical research suggest that EHRs should be used to lower the cost of trials by integrating point-of-care randomization and data capture into clinical processes. We believe that a successful learning health care system will require both approaches, and we suggest a model that resolves this escalating tension: a "green button" function within EHRs to help clinicians leverage aggregate patient data for decision making at the point of care. Giving clinicians such a tool would support patient care decisions in the absence of gold-standard evidence and would help prioritize clinical questions for which EHR-enabled randomization should be carried out. The privacy rule in the Health Insurance Portability and Accountability Act (HIPAA) of 1996 may require revision to support this novel use of patient data.

Keywords: Evidence-Based Medicine; Information Technology; Medicine/Clinical Issues; Quality Of Care; Research And Technology.

MeSH terms

  • Biomedical Research / methods
  • Datasets as Topic
  • Delivery of Health Care
  • Electronic Health Records*
  • Evidence-Based Medicine / methods*
  • Humans
  • Medical Informatics
  • Point-of-Care Systems*
  • Quality of Health Care
  • Retrospective Studies