From Challenge to Opportunity: Exploring Clinical Data Acquisition and Integration in the Healthcare Ecosystem
6.06.2023 By Lucy Parente, Director of Strategy at Availity
Clinical data sourced from electronic health records (EHRs), labs, health information exchanges (HIEs), and more contain rich details on an individual’s health journey, including vital signs, test results, diagnoses, immunizations, etc. This unique data asset contains vital information pertinent to patient care that is simply not present in other data sources.
As a result, clinical data has the potential to serve as the foundation for accurate risk-adjustment coding, improved clinical decision-making, quality reporting, predicting and mitigating disease progression, and patient care management. However, many health plans continue to be challenged by effectively deploying clinical data at scale.
The Challenge: Multi-Source & Multi-Format Data
The future of healthcare ultimately depends on frictionless data exchange and utilization between critical stakeholders in the healthcare ecosystem, such as providers, health plans, and patients. However, doing so is easier said than done. It can be a substantial challenge and investment to not only acquire and store clinical data but to transform and standardize it at scale to effectively tap into its potential.
Clinical data in its raw form is often unusable and a variety of factors contribute to its complexity:
- Competing standards: Local vs proprietary vs national vs international coding terminologies; standard and non-standard formats, including FHIR, C-CDA, and flat files.
- Coding inconsistencies: There are hundreds of different ways in which a single clinical data element, (such as the test for hemoglobin A1c), is documented.
- Data fragmentation: A patient may visit many providers for a single episode of care associated with an illness or accident. As such, documentation related to their treatment is scattered across different systems.
- Incomplete data: Key information may be omitted, leading to incomplete data. For example, lab results might be missing units.
- Disorganized data: Information may be documented in the wrong location in the health record. For example, an immunization may be documented with a CPT code in the EHR’s procedures section but not included in immunizations.
The cost and effort required to acquire and ingest multi-source and multi-format data is significant, then combine that with the operational and cost burden to solve the above data content and format challenges and many organizations do not know where to start to pursue a clinical data strategy. Availity’s unique position between payers and providers allows us to support industry stakeholders in understanding the sources of data and best practices for integrating and deploying it to generate ROI. Here we will share a few of those perspectives.
Common Sources of Clinical Data
There are several ways in which clinical data is collected, ranging from EHRs, HIEs, and other institutions, often making them time consuming and complex to access. Common clinical data sources include:
Electronic Health Records (EHRs): An EHR is a digital version of a patient’s medical history maintained by a provider. It includes clinical data relevant to a person’s care, such as reported problems, progress notes, medications, vital signs, past medical history, immunizations, laboratory, radiology reports, and demographics.
Health Information Exchange (HIE): HIEs collect and consolidate clinical data from multiple providers within a region. They are also referred to as health information networks (HINs). An HIE can share clinical information such as allergies, current medications, test results, and other clinical information vital to a patient’s care. Demographic data to identify a patient might also be shared, such as name, address, and date of birth. Information is private and viewable only by authorized healthcare providers.
Labs: Laboratory systems that capture diagnostic information to support clinical decisions by payers, providers, pharmaceutical organizations, researchers, and patients. These can help provide vital information for managing patient conditions, reporting accurate quality measure scores, and trending population health and patient outcomes.
Within each source of data, clinical data can be provided in various formats, which defines how the clinical elements are structurally represented. Standard formats include Continuity of Care Documents, (CCD), and Fast Healthcare Interoperability Resources (FHIR®). Non-standard formats can range from source to source but include flat files and other custom extracts. For a health plan looking to acquire and then use data from these sources, they must manage acquisition, consent and member matching, standardization, and routing. Owning all of these activities on top of the cost to acquire quickly becomes expensive and operationally burdensome. This is where technology partners come in to accelerate time to value while reducing overall cost.
How Health Plans Can More Easily and Effectively Utilize Clinical Data
Investing in solutions to transform raw clinical data into a usable asset that is standard, organized, and actionable is critical for health plans to maximize ROI. Without this type of solution in place, health plans can easily find the path toward realizing the true value of clinical data to be undefined and expensive. Traditional approaches to normalizing and integrating data can be resource-intensive and do not scale. As a result, health plans must evaluate automated, real-time mechanisms to ingest, standardize, and deliver data that is fit for use.
Availity Fusion™ leverages a differentiated process for Upcycling Data™ in real-time and at scale to ensure multi-format, multi-source data can be used downstream for risk adjustment, quality, care management, and other use cases. Upcycled data is normalized to national standards, interoperable, deduplicated, consolidated into a longitudinal record, and available in fit-for-purpose data packages for flexible deployment at scale. It leverages flexible integration mechanisms to deploy into existing tech stacks and fit into transactional workflows via standard, real-time APIs.
This benefits health plans in several ways. First, accurate and high-quality clinical data is the key to successful compliance with government mandates surrounding interoperability, including the Patient Access API, as well as supporting the shift to value-based care models. Further, health plans can leverage data as a strategic asset for risk adjustment, to increase the accuracy of HCC risk scores, HEDIS® and quality measure compliance rates, proactive gap closure, targeted care management, predictive analytics, population health reporting, and cohort stratification, among others—ultimately enabling improved decision-making to drive better health outcomes. Lastly, from a financial perspective, this has a direct effect on reimbursement and revenue as well as cost savings and long-term cost avoidance.
If you are interested in learning more about how upcycled data can help your organization realize the greatest value from clinical data, download our Clinical Data Integration Buyer’s Guide