By Shannon Fuller | July 25, 2019
Original Source
For all the conventional wisdom on how value-based care reduces inefficiencies, getting from “here to there” represents an enormous information technology burden to healthcare providers. With it comes the promise that health systems can save money if they spend more on technology – an irony that can be tough to swallow. In fact, it can seem like precious resources are moving away from care, and going toward IT operations that appear far removed from working with patients.
But it’s at this critical moment that healthcare providers should consider an entire ecosystem of care that incorporates technology to ultimately facilitate better care at a reduced cost. But in order to take the leap of faith and invest in technology that actually provides value, organizations need to look at their IT foundations first – it’s a lot like building a house – without a sturdy foundation with the right reinforcements, you are going to have a myriad of structural issues later.
Digital disruption is here and the challenge for hospitals is figuring out how to prepare. A good place to start is for the leaders of healthcare organizations to ask themselves about the practical first steps in the transition to digital healthcare. Interoperability is a main priority, and hospitals need to integrate data from disparate sources. Both payers and providers can integrate data both within and across their organizations, but such integration will likely require the adoption of innovative technologies.
Healthcare organizations today have siloed applications, and it will take the right technology and partner to bring it all together. Health systems have electronic medical records; they have lab systems, human resources systems, claims systems…the list goes on. It’s common for none of these systems to speak to each other – so they need to figure out a data integration strategy to ensure that they can come to the table with more valuable intelligence drawn from their data. To move in this direction, providers need to try to implement some of the newer cloud-based solutions that will enable them to aggregate and integrate data across the organization.
Another key consideration is that patients’ data is often represented by codes that correspond to diagnoses, measurements and lab tests. These codes vary enormously between geographies, insurers, hospital systems and providers. That makes analytical insights challenging, because data doesn’t exist across different systems in a one-to-one format. In addition to the plague of siloed data sets, a lack of standardized data governance processes is a major challenge.
We can easily become overwhelmed by simply collecting or contributing data – either by never finding the time or resources for the linking and analysis that deliver insights, or by wasting valuable employee talent on manually trying to piece it all together.
Rebuilding Trust in Data
Before any data standardization can take place, analysts can spend more than 40 hours per project validating information. And still, mistrust in the data can persist because it isn’t defined consistently. For data analytics to work, the data must be reliable.
By implementing data governance methods, processes and standards, time is spent on generating insights, not data gathering or cleansing.
The key to delivering value is understanding your data, where it is managed and how it is used. For example, by standardizing demographics data (e.g. ethnicity, language and sexual orientation), one can identify specific populations for health interventions (patient segmentation), analyzing the factors of social determinates of health and understanding disparities of care.
From a technology standpoint, cloud-based tools can reduce barriers and the time to realize value. If we focus on understanding our data, we can start the process of building a common vocabulary, assign decision rights, reduce duplication and develop a holistic analytics platform that supports the entire enterprise. The demands for advanced data and analytics capabilities are relatively new to healthcare, and EHR (electronic health records) applications were not designed to support these evolving demands.
Emerging Technologies to Support Governance
New open-source cloud platforms can help solve the twin issues of data sharing and standardization. A subscription-based cloud platform allows easy data aggregation, automates the monitoring of the data quality, provides easier data sharing, better cybersecurity and has the ability to scale cost effectively. The aim is to bring the pieces of the complex puzzle together, by providing a single point of access and looking at the patient holistically.
With data governance in place, health systems can renew a sense of trust in data and start leaning on analytics for fact-based decisions instead of instincts. To gain adoption across other stakeholders in healthcare organizations, leaders must demonstrate how data governance programs and their underlying initiatives directly support corporate objectives.
Healthcare practitioners need to be proactive in addressing issues of data quality, availability, risk and privacy. Information governance is a key component in identifying and mitigating legal, operational and compliance risks and above all ensuring the right people have the right information at the right time.
Shannon Fuller is director of data governance advisory services at Gray Matter Analytics.
