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Top 3 Ways to Improve Patient Care Through Healthcare Data Governance

Authors Photo Precisely Editor | September 12, 2022

Healthcare data governance also known in the industry as “information governance,” refers to a framework that manages data assets to ensure confidentiality, appropriate access, and accurate maintenance and storage of patient information. Traditionally, it’s also supported regulatory compliance efforts at both the state and federal level (HITRUST, HIPAA, and PII protection, for example).

Looking beyond security and regulatory compliance, there are foundational challenges that healthcare data governance can 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. 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 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 quite 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 its 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

How have analytics transformed data governance? In a 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 and create a potentially dangerous domino effect. For example: office staff create a new account and a duplicate record for a patient already in the system, leading to future treatment for that patient being divided between two patient records. This could result in missed warning signs, the wrong type of care, or administration of medication that might cause problems.

Healthcare data govrnance

There are more advanced techniques, such as machine learning, that when applied to datasets 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’re 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.

A data governance solution that incorporates analytics and delivers high-quality data can help revolutionize our approach to healthcare.

Read the Whitepaper

Healthcare Digital Transformation

This whitepaper explores how healthcare payers should look for technologies that prioritize data quality to produce better analytic insights to drive critical initiatives like preventive care, population health and interoperability.

How data governance can revolutionize 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:

1. Value-Based Care:

Also known as value-based reimbursement, this 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 (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’s imperative that healthcare providers implement a data governance program that ensures the integrity and completeness of data as it’s 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.

2. 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.

Healthcare IoT devices is another rapidly expanding area, which includes tech 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’s important that all 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.

3. 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.

For more on how to create a better future for patients through data governance and quality, read our whitepaper “Healthcare Digital Transformation