Mastering Healthcare Data Governance

Learn Foundational Challenges that Healthcare Data Governance Can Help Address

Jodi JohnsonJanuary 26, 2018

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Data governance, or as it’s commonly referred to in healthcare, information governance, is becoming increasingly important in this industry. Traditionally, data governance has supported regulatory compliance efforts at both the state and federal level, addressing issues related to HITRUST, HIPAA and the protection of personally identifiable information (PII). Much of the focus has been on managing data assets to ensure confidentiality, appropriate access, and accurate maintenance and storage of patient information. But beyond the realm of security and regulatory compliance, there are foundational challenges that healthcare data governance could help to address.

Across the healthcare industry, both payers and providers alike are increasingly turning to analytics to drive quality, improve outcomes, and increase revenue, and big data has enormous potential to transform the healthcare ecosystem. From glucose readings and immunization records, to claims processing and billing, not to mention electronic health records (EHRs), healthcare organizations are literally swimming in data. But to make accurate, informed decisions based on analysis of all that data, one foundational issue has to be addressed – data quality.

In healthcare, data quality can literally be a life-and-death issue. If data is incomplete, or invalid, it can have devastating effects on a patient’s health or treatment. Likewise, if data is misunderstood or misused, it can also lead to dangerously inaccurate conclusions. That’s why data governance is so critical in healthcare today—because data governance capabilities have evolved to not only provide data understanding and accountability across an organization, but also incorporate analytics to continuously monitor the quality of data assets—a powerful combination for improving the industry.

Incorporating Analytics into Healthcare Data Governance to Improve Data Quality 

The reason analytics have transformed data governance can be summarized in one word: automation. Just consider something as common as patient-matching. Sounds like an easy task, but even matching patient records with the right person inside the clinician’s office can prove difficult. One incorrect letter in a name, or a misplaced digit in a birthday or social security number can throw off a match, resulting in office staff creating a new account and a duplicate record for a patient already in the system. Future treatment for that patient could then be divided between two patient records, which could result in missed warning signs, the wrong type of care or administration of medication that might cause problems.

There are more advanced techniques, such as machine learning, that when applied to data sets allow organizations to automatically detect anomalies based on historical patterns, rather than a person setting a rule to look for them.  With high quality data, healthcare providers can derive additional insights into data that would otherwise go unnoticed.

This is increasingly important as healthcare providers work to modernize their patient care models, while incorporating emerging technologies into treatment. Providers and payers today aren’t simply looking at treating sick patients, they are seeking ways to proactively improve individuals’ overall health and wellness. This means using analytics to predict and prevent the development or deterioration of chronic conditions, and a data governance solution that incorporates analytics and delivers high quality data can help revolutionize our approach to healthcare.

Revolutionizing Patient Care

With high quality data at their fingertips, healthcare providers can capitalize on the massive potential surrounding big data. There are a variety of ways big data can help improve patient care while also maximizing value and minimizing costs. Here are just a few.

Value-Based Care: Value-based care, or value-based reimbursement, is a catch-all descriptor for payment arrangements where a single price is negotiated for all treatment associated with an episode of care. There may be risk-reward incentives based on outcomes, or providers (such as hospitals, physicians, physical therapists, etc.) share in a lump sum payment for all treatment related to a condition or surgical episode. Under this model, unlike traditional fee-for-service reimbursement, providers are incentivized to improve outcomes and reduce unnecessary care (i.e., increase value) rather than rewarded simply for number of services they provide (i.e., volume). To reach these quality goals and reap the rewards associated with these models, it is imperative that providers implement a data governance program that ensures the integrity and completeness of data as it is shared between healthcare providers. Quality care and optimized outcomes arise when every provider has a full picture of the patient’s health, treatment, and progress.

Telemedicine and IoT: Telemedicine is gaining rapid acceptance, and enables patients and providers to use technology to remotely communicate and collaborate to improve a patient’s health. Telemedicine is particularly useful for patients in underserved areas or rural communities, but its convenience is universal. Another rapidly expanding technology is healthcare IoT devices, including wearables for blood pressure, oxygen, and heartrate monitoring, as well as at home devices for sleep apnea and diabetes testing. All of these devices can collect and send vital data to healthcare providers, and enable medical professionals to monitor patients recovering from acute episodes or suffering from chronic conditions. However, it is important that all of the data is collected, accurate, accrued in real time, and of course matched to the correct patient. Analytics-enabled data governance can automate this data quality monitoring and reduce the risk of incomplete or erroneous data.

Electronic Healthcare Records (EHR): Despite big government incentives during the Obama administration to implement EHR systems, many providers have been slow adopters for a multitude of reasons, ranging from the expense to the complexity of the interfaces. Between providers still reliant on paper records, and a lack of standardization among providers’ EHR systems, it’s difficult for providers to ingest and understand health data from external sources. A comprehensive data governance solution can ensure that all incoming EHR’s are complete and readable.

Healthcare data governance has become more important than ever in the industry. With the proper solution, healthcare providers can gain a clear and complete view of their data landscape, and allow them to combat increasingly complex regulatory and compliance demands while also vastly improving patient outcomes.

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