Electronic health record design and implementation for pharmacogenomics: a local perspective.

PURPOSE: The design of electronic health records to translate genomic medicine into clinical care is crucial to successful introduction of new genomic services, yet there are few published guides to implementation. METHODS: The design, implemented features, and evolution of a locally developed electronic health record that supports a large pharmacogenomics program at a tertiary-care academic medical center was tracked over a 4-year development period. RESULTS: Developers and program staff created electronic health record mechanisms for ordering a pharmacogenomics panel in advance of clinical need (preemptive genotyping) and in response to a specific drug indication. Genetic data from panel-based genotyping were sequestered from the electronic health record until drug-gene interactions met evidentiary standards and deemed clinically actionable. A service to translate genotype to predicted drug-response phenotype populated a summary of drug-gene interactions, triggered inpatient and outpatient clinical decision support, updated laboratory records, and created gene results within online personal health records. CONCLUSION: The design of a locally developed electronic health record supporting pharmacogenomics has generalizable utility. The challenge of representing genomic data in a comprehensible and clinically actionable format is discussed along with reflection on the scalability of the model to larger sets of genomic data.

Extending closed-loop control to the management of chronic disease.

A closed-loop control process assures that a system performs within control limits by direct feedback of the system's output to change the system's inputs. We developed methods for the closed-loop control of system-based practice, using ventilator management as a model or test bed. The control system has three components: 1) an explicit end-to-end plan; 2) a record of what is done as it is done; and 3) an instant display of the status of each patient against the plan for that patient. The status display provides process control by showing the clinical team where corrections are needed while the team still has the time needed to act prospectively. We are extending these methods to the management of chronic disease. Their extension requires engagement of the patient as a member of the team, a coordinated plan across the care continuum, informatics algorithms to stratify individual patients according to co-morbidities and their current level of control, and a means of detecting the presence or absence of a reaction to each action taken by the team.

Identifying unpredicted drug benefit through query of patient experiential knowledge: a proof of concept web-based system.

Information necessary to recognize unexpected drug efficacy is not routinely collected. Once a drug is approved, opportunities for understanding these phenomena are usually lost within clinical care. We propose that patients are willing to provide a wide range of experiential knowledge about the effects of therapies that is seldom solicited. Experience with various drug therapies might be solicited directly from patients in both structured and unstructured formats. Although the signal to noise ratio is expected to be low, these data, if organized in a constructive manner, could provide a useful hypothesis generation resource for areas of further pharmacologic inquiry. A pilot study was conducted for 18 months; 1,065 individuals using the MyHealthAtVanderbilt.com patient portal clicked on a research link to find more information about the study; 375 completed the survey (response rate of 37%). Of those, 218 patients reported that they were currently taking at least one prescription. Statistical methods applied detected known associations between drugs and their intended effects. This validated the type and frequency of effects being reported by patients and provided evidence for the potential for using patient-supplied information to generate hypotheses related to unexpected positive benefits associated with medications. Improved data filtering and mining methods will be needed to expand this concept.

Behind-the-scenes of patient-centered care: content analysis of electronic messaging among primary care clinic providers and staff.

BACKGROUND: Transitions to patient-centered health care, the increasing complexity of care, and growth in self-management have all increased the frequency and intensity of clinical services provided outside office settings and between visits. Understanding how electronic messaging, which is often used to coordinate care, affects care is crucial. A taxonomy for codifying clinical text messages into standardized categories could facilitate content analysis of work performed or enhanced via electronic messaging. OBJECTIVE: To codify electronic messages exchanged among the primary care providers and the staff managing diabetes patients at an academic medical center. RESEARCH DESIGN: Retrospective analysis of 27,061 electronic messages exchanged among 578 providers and staff caring for a cohort of 639 adult primary care patients with diabetes between April 1, 2003 and October 31, 2003. SUBJECTS: Providers and staff using locally developed electronic messaging in an academic medical center's adult primary care clinic. MEASURES: Raw data included clinical text message content, message ID, thread ID, and user ID. Derived measures included user job classification, 35 flags codifying message content, and a taxonomy grouping the flags. RESULTS: Messages contained diverse content: communications with patients, families, and other providers (47.2%), diagnoses (25.4%), documentation (33%), logistics and support functions (29.6%), medications (32.9%), and treatments (28.9%). All messages could be classified; 59.5% of messages addressed 2 or more content areas. CONCLUSIONS: Systematic content analysis of provider and staff electronic messages yields specific insight regarding clinical and administrative work carried out via electronic messaging.

Integrating automated patient letter generation into the electronic medical record.

This poster describes the design and functionality of StarLetter, an electronic patient letter generation tool. StarLetter is integrated into the new results feature of the electronic medical record front end allowing the clinicians to generate electronic letters to patients within their workflow.