IMPORTANCE Accessing specialty care continues to be a persistent problem for patients who use safety-net health systems. To address this access barrier, hospital systems have begun to implement electronic referral systems using eConsults, which allow clinicians to submit referral requests to specialty clinics electronically and enable specialty reviewers to resolve referrals, if appropriate, through electronic dialogue without an in-person visit.OBJECTIVE Measure the effect of implementing an eConsult program on access to specialty care. DESIGN, SETTING, AND PARTICIPANTSUsing an interrupted time series design with data from 2016 to 2020, this study analyzed 50 260 referral requests submitted during the year before and the year after eConsult implementation at 19 New York City Health + Hospitals (NYC H+H) specialty clinics that spanned 7 NYC H+H hospital facilities and 6 unique specialties.EXPOSURES Referral request was submitted to a specialty clinic in the year following eConsult implementation. MAIN OUTCOMES AND MEASURES Main outcomes included the fraction of referral requestsresolved without an in-person visit following eConsult implementation; and, among requests triaged to have an in-person visit, the fraction of referrals with a successfully scheduled appointment, mean wait time to a specialty appointment, and the fraction of referral requests with a completed specialty visit. Changes associated with eConsult implementation were estimated using multivariate linear regression adjusting for patient age, gender, and specialty clinic fixed effects. RESULTSAcross 19 NYC H+H specialty clinics, 26 731 referral requests were submitted in the year before and 23 529 referrals were submitted in the year after eConsult implementation. Following eConsult implementation, 13% of all requests were resolved electronically. Among requests requiring a follow-up visit, the fraction with an appointment successfully scheduled increased by 15.8%, from 66.5% to 82.3% (P < .001). The mean time to an appointment decreased from 61.0 days pre-eConsult to 54.1 days post-eConsult, an adjusted 8.2-day shorter wait time (or 13.3% reduction) following eConsult adoption (P < .001). The percentage of referrals with a completed follow-up visit with a specialist within 90 days of the request did not change (38.4% vs 37.9%, P = .07). Changes in outcomes were mitigated during months when most clinics underwent an electronic health record transition after implementing eConsult. CONCLUSIONS AND RELEVANCEIn this quality improvement study, implementation of eConsults at a large multi-specialty safety-net system was associated with improvements in appointment scheduling rates and wait times. Despite an additional electronic health record transition, eConsults are a promising health care delivery tool for increasing access to specialty care.
Electronic health records (EHRs) provide timely access to millions of patient data records while limiting errors associated with manual data extraction. To demonstrate these advantages of EHRs to public health practice, we examine the ability of a EHR calculated blood-pressure (BP) measure to replicate seasonal variation as reported by prior studies that used manual data extraction.Our sample included 609 primary-care practices in New York City. BP control among hypertensives was defined as systolic blood pressure of 140 or less and diastolic blood pressure of 90 or less (BP < 140/90 mm Hg). An innovative query-distribution system was used to extract monthly BP control values from the EHRs of adult patients diagnosed with hypertension over a 25-month period. Generalized estimating equations were used to compare the association between seasonal temperature variations and BP control rates at the practice level, while adjusting for known demographic factors (age, gender), comorbid diseases (diabetes) associated with blood pressure, and months since EHR implementation.BP control rates increased gradually from the spring months to peak summer months before declining in the fall months. In addition to seasonal variation, the adjusted model showed that a 1% increase in patients with a diabetic comorbidity is associated with an increase of 3% (OR 1.03; CI 1.028–1.032) on the BP measure.Our findings identified cyclic trends in BP control and highlighted greater association with increased proportion of diabetic patients, therefore confirming the ability of the EHR as a tool for measuring population health outcomes.
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