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Health Information Technology (HIT) Interoperability

–  Presales Team

Introduction

Healthcare payers have long operated using only retrospective claims data for making business and care decisions. But with the ongoing transition from fee-for-service to value-based care, claims data is no longer sufficient. Forward-looking payers need a near real-time view into the health and care of their members, and into their performance.

Clinical data provides substantial details on member encounters, but it is often difficult to assemble and integrate data from more than one healthcare provider. Claims data is better at following a member across multiple care providers but lacks information on member health status and outcomes. Individually, both sets of data tell helpful stories, from chronicling the cost of care to reflecting how medicine is practiced. Together, clinical and claims data provide a deeper picture of a member’s interactions with health care systems, the costs involved, and the results achieved.

Opportunities & Use Cases

Until recently, technology-enabled efforts to improve population health relied heavily on the use of claims data alone. The increased availability of other types of useful data, namely clinical data from electronic health records, and radiology and ECG DICOM images, can help healthcare organizations fine-tune their analytics. As a result, they can better segment populations, improve quality, increase member engagement to successfully address population health needs.

  1. Improving Member Identification and Matching
    With increased levels of information sharing, supported by the interoperability of systems, methods for accurately identifying members and matching their records throughout the health care system can be improved substantially. The need for identification and matching has become more urgent considering the increasingly digitized state of the US health care system and the substantial increase in demands and policies for accelerating electronic information sharing.
    Solution: Member 360 Insights
    Member 360 Insights connects fragmented data from any source or channel—campaign management, member portal, digital health, policy administration, eligibility, claims, billing, care management, CRM as well as contact center, member surveys captured by both payers and providers, health insurance marketplaces, and affiliated provider networks (EHR data, DICOM data, member surveys)—and synthesizes it into a consolidated 360 member view that is enriched with deep intelligence to produce insights and is consumable in real-time. Member 360 discovers and manages complex relationships between individuals, households, groups, provider networks, and service providers.
  2. Population Health Improvement
    Healthcare has shifted its focus to population health. The field’s emergence as a top priority represents an even greater trend: the transition to value-based care. In this new world, payers and providers alike are responsible for what happens to members inside the clinic and what fails to happen outside. Unifying provider and payer data can improve care, reduce costs, and enhance health.
    Solution: Gaps in Care Analytics for Risk Management
    Claims data, when combined with clinical data provides very specific value when comparing recommended care against evidence-based practices. Advanced analytics can be used to identify members with possible care gaps. Using computer vision, machine learning and natural language processing, medical terms related to diagnosis, procedures, drugs and/or related medical codes can be extracted for HCC (Hierarchical Condition Category) coding. This can help determine whether an individual in a specific stage of heart failure is on the most appropriate medication. The combination of data can also help identify food, drug or substance allergies, and members with early risk factors before these issues lead to acute events and hospitalizations. Alert notifications can be sent to all stakeholders.
    Solution: Predict Members at High Risk for Chronic Disease like Type 2 Diabetes
    Advanced analytics using Machine Learning and Deep Learning can help payers identify member segments with distinct known features and preferences who are at risk of developing chronic conditions (e.g., turning diabetic). Combining claims data with clinical data significantly increases prediction accuracy. Analytics can assist in developing personalized messages and interventions and prescribe appropriate medical pathways as treatment. For example, diabetic plan participants who need assistance to better manage their disease.
    Solution: Hospital Readmission Prediction
    Readmission rates track the percentage of members who are admitted into the same or another hospital within 30 days of being discharged for the same condition or a complication from the original episode of care. AI-based solutions can be used to predict the risk of hospital readmissions by analyzing member data (admission discharge transfer data, claims data, and utilization authorization data) to plan appropriate interventions to avoid the same.
    Solution: Targeted Engagement Strategies
    A combination of claims and clinical data can be used to drive better member engagement, which is key to a successful population health program. For example, member data married with claims/clinical data can identify the most appropriate timing and vehicle for outreach. It could determine whether an individual will respond more positively to one-on-one health coaching or self-service engagement tools. This approach will also help drive operational efficiencies by allowing organizations to allocate resources where they have the most impact.
  3. Advanced Analytics for Claims and Pre-Authorization Requests
    Request processing follows a complex workflow that includes several checkpoints such as the patient’s insurance plan, the payer’s guidelines for request submission, and the provider’s contract with the payer. A customized analytics solution can provide better accuracy, detect fraud, improve cost-efficiency, and help healthcare providers process requests faster.
    Solution: Enhancing Claims Fraud Detection
    Through the deployment of real-time information sharing across physicians, clinics, members, and payer organizations, pattern identification is undertaken to detect potential frauds towards claims. AI-based solutions can analyze claims data of providers and identify patterns of prescriptions or medications and investigations leading to malpractice. Missing or incorrect information is highlighted, gaps are identified, unrequired services are avoided, separate billing procedures are reduced and miscoding is realized. This form of payer analytics uses interoperability data analysis to track and mitigate potential frauds before realization.
    Solution: Prior-Authorization/Claim Request Adjudication Automation
    AI-based solutions can automate the process of adjudicating prior-authorization and claim requests in real-time to replace manual or batch review processes with rules and in-time workflow and eliminate or reduce duplication errors, resulting in decreased turnaround time at lower costs. AI can mine data from laboratories, medication, and claims data to recommend appropriate treatments and evaluate member outcomes. Admission Discharge Transfer data can be used to create alerts for authorization requests.
  4. Interoperability with Consumer Health Technology
    Several factors, from higher out-of-pocket costs to lack of primary care availability, are driving member demand for and acceptance of alternative approaches to their care, including the use of retail clinics and a growing catalog of healthcare products that rely on mobile, “wearable,” and home-based technologies. These demands provide both the opportunity and the necessity of a broader view of interoperability among new consumer health technologies and medical devices, traditional and non-traditional health care providers, and other components of our health care system.
    Solution: Care 360: Community based Healthy Reward Program
    Care 360 is an application that rewards members for taking small steps that help them live a healthy life by measuring member behavior with multichannel eco-system to reward members automatically. It leverages data and analytics to customize member offerings and partnership pairing and helps payers to engage members beyond traditional outreach by leveraging social determinants of health factors. Members can leverage Care 360 coins at community level, in addition to individual benefits.
    When connected to smart home devices such as Google Home and Amazon Alexa members can use voice commands to track activities, check health appointments, and get medication reminders.

Conclusion

By integrating claims and clinical data, payers will be able to dig deeper and more accurately into the health status of their members and into what its providers need to improve. This solves many of the problems inherent in a fragmented healthcare system, and provides an advantage in improving business processes and performance.

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