The American journal of managed care
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Electronic consultation, or e-consult, systems improve specialty care access by conveying specialist expertise to primary care clinicians (PCCs) without requiring specialist visits. Our study evaluates organizational factors for e-consult implementation across 5 publicly financed, county-based health systems in California. Each system serves 40,000 to 180,000 culturally and linguistically diverse patients across 4 to 19 primary care locations. ⋯ Successful e-consult implementations in public delivery systems leveraged (1) prior primary care and specialty care clinician relationships and (2) integrated EHR and e-consult platforms. This contrasts with common expectations that new technology will overcome care delivery gaps. Findings add to existing e-consult implementation literature that emphasizes reimbursement and leadership champions.
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To (1) assess whether hospitals in states requiring explicit patient consent ("opt-in") for health information exchange (HIE) are more likely to report regulatory barriers to HIE and (2) analyze whether these policies correlate with hospital volume of HIE. ⋯ Our findings suggest that opt-in consent laws may carry greater administrative burdens compared with opt-out policies. However, less technologically advanced hospitals may bear more of this burden. Furthermore, opt-in policies may not affect HIE volume for hospitals that have already achieved a degree of technological sophistication. Policy makers should carefully consider the incidence of administrative burdens when crafting laws pertaining to HIE.
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The adoption and use of health information technology (IT) by health systems in ambulatory care can be an important driver of care quality. We examine recent trends in health IT adoption by health system-affiliated ambulatory clinics in the context of the federal government's Meaningful Use and Promoting Interoperability programs. ⋯ The relatively low uptake of health IT functionalities important to care improvement suggests substantial opportunities for further improving adoption of ambulatory health IT even among the current EHR users.
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Observational Study
Predicting hospitalizations from electronic health record data.
Electronic health record (EHR) data have become increasingly available and may help inform clinical prediction. However, predicting hospitalizations among a diverse group of patients remains difficult. We sought to use EHR data to create and internally validate a predictive model for clinical use in predicting hospitalizations. ⋯ Prediction models using EHR-only, claims-only, and combined data had similar predictive value and demonstrated strong discrimination for which patients will be hospitalized in the ensuing 6 months.
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To determine if it is possible to risk-stratify avoidable utilization without clinical data and with limited patient-level data. ⋯ This study indicates that it is possible to risk-stratify patients' risk of utilization without interacting with the patient or collecting information beyond the patient's age, gender, race, and address. The implications of this application are wide and have the potential to positively affect health systems by facilitating targeted patient outreach with specific, individualized interventions to tackle detrimental SDH at not only the individual level but also the neighborhood level.