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A. Ocana

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  • OS01 - Better Care Outcomes through Data and Analytics (ID 8)

    • Event: e-Health 2018 Virtual Meeting
    • Type: Oral Session
    • Track: Technical/Interoperability
    • Presentations: 1
    • OS01.04 - Innovative Interoperability: Standardized Insurance Forms to Improve MD-Generated Data (ID 158)

      A. Ocana, NorthShore Mental Health and Addiction Clinic; North Vancouver/CA

      • Abstract
      • Slides

      Purpose/Objectives: Primary Care Physicians (PCPs) manage of 80% of patients with mental health challenges. They have no systematic way to assess or monitor their patients. Currrently EMRs are not interoperable and there is no standardized digital format for insurance reports. Insurance and benefits providers pay physicians to generate medical reports. The data is handwritten, returned by fax, unsecurely, not digital, not entered in distinct fields, generally incomplete, nonspecific, replete with errors, uncodable and therefore impossible to analyze, according to a recent qualitative study: Tang KL, Lucyk K, Quan H. CMAJO August 21, 2017 vol. 5 no. 3 E617-E622. Without access to quality data, we can't answer the big questions in healthcare, like the Fentanyl crisis or teen suicide. According to the WHO, Mental Health and Addiction are the number one causes of disabiilty world-wide. The annual health cost in North America is staggering. Insurance forms are an easily accessible source of patient-level data. Consent to use patients' anonymized data for research is not implicit, but can be obtained. The objective of our project was to develop and implement a standardized, digital, mental health assessment and reporting tool that could be used interoperably in any EMR.

      Methodology/Approach: We reviewed a broad sample of current insurance reports, discerned the commonly asked questions and reverse-engineered our assessment to be able to answer and score those questions in text, drop down menus and other digital fields. Our assessment included: Symptoms and their severity Functional Impact of symptoms on: cognitive tasks, work, home, relationship, social, driving and physical health. A comprehensive Co-morbidity Screen for Mood Disorders, Chronic Pain, ADHD, Substance Use Disorders and Trauma. Patient-rated measures of Current Function including: cognitive, executive, social and emotional function, as well as measures on productivity, mood, social support, motivation, readiness for change, pain, stress tolerance, quality of life and emotional stability. For simplicity and interoperability, measures were rated on a 10-point scale. Prognosis using the balance of resources (such as social support, motivation) vs. risks (such as unemployment or emotional instability) We trialed our assessment on the 150 consecutive patients.

      Finding/Results: By asking the same question, the same way and putting the answer into the same field, our assessment tool simplified and standardized data capture and analysis compared to having multiple different paper-based forms. It uncovered a broad measure of relevant co-morbid psychopathology. It generated quantitative measures with face and construct validity, where previously there was unstructured, unsubstantiated opinion.

      Conclusion/Implications/Recommendations: If insurance and benefits providers could agree on a standardized digital format for medical reports, using industry-standard questions, then physician-generated forms would be much more valuable as a health analysis tool. In future, machine learning, using highly segmented, anonymized, data that is disease, symptom and treatment specific, would allow us to evaluate and optimize interventions across the full cycle of care, based on patient-rated outcomes. The millions of data points created would facilitate the generation of artificial intelligence-driven algorithms that could improve care, reduce adverse events and decrease health costs. That would allow us to answer the big questions.

      140 Character Summary: Mental Health is the primary cause of disability world-wide. Stakeholder collaboration on data collection would generate health benefits and cost savings.

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