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32 Executive Briefing Sponsored by: Going Without a Data Asset Strategy Will Cost Your Health System — Here's How I. Introduction As healthcare organizations begin to apply analytics to care de- livery, data has become a hospital's most valuable asset — and one of the most challenging to manage. Due to advances in digital and cloud technology, more patient health information exists today than ever before. Digital health data has the potential to transform care delivery by helping physicians make evidence-based decisions. With access to health information from different data sources, clinicians gain better insight into patients' conditions, such as heart disease or diabetes, which are complex and costly to manage. Although the process of capturing, acquiring, validating and stor- ing data is crucial to gaining these insights, many healthcare orga- nizations don't have standard procedures or processes in place to ensure data quality or security. If an organization tries to aggre- gate and analyze poor-quality data, it may derive useless or even wrong conclusions, which can have dangerous consequences. To better support strategic decision making in value-based care, hospitals and health systems are establishing data asset strate- gies to manage the collection and transformation of raw data into actionable insights that support quality improvement efforts. A data asset strategy outlines the governance framework and sets standard procedures for how a healthcare organization will manage data to ensure it's secure, available, reliable and ac- tionable for an array of hospital staff, including clinicians and administrators. The strategy should span the entire data value continuum — from acquisition to delivering data to end users — and establish best practices at each step that align with stra- tegic goals across the organization. Without a well-developed data science strategy, healthcare or- ganizations are more likely to struggle to leverage and protect increasing volumes of data and medical knowledge in an orga- nized and efficient manner. II. Defining the data challenge Capturing a diverse range of health data has been a strategic priority across the healthcare industry in the last decade. Pro- vider organizations especially have committed a substantial amount of time and money to building sophisticated health in- formation systems and digital warehouses. They amass exhaus- tive amounts of patient health information every day through a complex array of internal and external sources, such as inpa- tient and ambulatory EHRs, electronic prescription databases, laboratory systems and payer claims databases. Capturing data is the first step toward analytics-driven medicine. But data is only as helpful as the insights it yields, and often it re- quires a significant amount of rework before it can be combined and used to make clinical, operational or business decisions. Each individual data source, from fitness trackers to EHRs, pro- vides valuable information of patient health and behaviors, es- pecially when these sources are combined to create a compos- ite medical record. Giving the appropriate caregivers access to this information can positively affect care delivery by reducing the likelihood of duplicative care, discovering gaps in care and avoiding unnecessary testing. "The more visibility you have [into a situation], the more intelli- gent decisions you can make," says Tina Foster, vice president of business advisor services for health information technology provider RelayHealth. This is true for both individual patient care as well as high-level business or marketing decisions. "Executive leaders are asking themselves, should we add more neonatal intensive care unit beds? Does it make sense open an outpatient department? They're looking at information all the time to make strategic decisions," Ms. Foster says. Integrating data from numerous sources is integral to supporting the business of care delivery in outcomes-based reimbursement. However, it also presents substantial challenges — interoperabil- ity, data integrity, usability — as well as privacy and cybersecurity risks that make an organization vulnerable to legal recourse. "The focus in healthcare has been on automating data capture, acquiring data and getting it together in one spot, not on strate- gic planning and how we're going to use that data meaningfully on the backend," Ms. Foster says. The lack of strategic clarity does not go unnoticed among health data scientists. A Stoltenberg Consulting survey found 51 per- cent of healthcare IT leaders believe the most significant barrier to hospital data analytics is not knowing what data to collect or how much of it, followed by a lack of organizational clarity on what to do with data and what to look for when analyzing it. Three common data-related issues plague hospitals without data asset strategies. i. Nonstandard data integration can comprise data quality Data from disparate health information systems come in different formats with different vocabularies. Common health data formats include HL7, X12, CCR, CCD and CCDA, among others. Combin- ing raw data from various sources results in a hodge-podge of digital information that isn't usable or valuable until it is translat- ed into a standard set of definitions. Before a software algorithm can explore the data for insights, the data must be cleaned up and converted into a unified form the algorithm can understand. Healthcare organizations have traditionally addressed the chal- lenges of big data by relying on data scientists and IT staff to manually acquire, "clean up" and integrate data from disparate sources. The tedious nature of the job — what data scientists call "data wrangling" or "data janitor work" — requires sub- stantial labor and cost. Data scientists reported spending up to 80 percent of their workday collecting and preparing unruly