Becker's Hospital Review

66 Managing the Who, Where and When of Data — 3 Keys to Success in Value-Based Care A s healthcare organizations begin to apply analytics to care delivery, data has become a hospital's most valuable as- set — and one of the most challenging to manage. Today, technology is driving new avenues of communication, which support the transformation of fee-for-service healthcare into collaborative medicine. Sharing patient data across mul- tiple independent care settings can help physicians avoid du- plicative or unnecessary procedures and close gaps in care, thereby lowering costs and improving outcomes. Although exchanging medical information is key to facilitating more seamless and cost-effective care delivery, it also pres- ents a host of complex security, privacy and operational chal- lenges traditional data-sharing models struggle to adequately or efficiently solve. "The complexities of today's healthcare data go beyond the capacities of the traditional point-to-point data model," es- pecially when dealing with data on a large scale, says Arien Malec, Senior Vice President at Change Healthcare. Connect- ing data by manually matching source to source, one interface at a time, is neither cost-effective nor efficient, and can result in expensive, unruly ownership models that are difficult to sus- tain. Managing this data complexity in outcomes-based medicine requires forethought and consideration, as well as two specific IT criteria — scalable data acquisition and adaptive data policy. This article defines and examines the issues of data acquisition and data policy in value-based payment, as well as inefficien- cies in traditional data models. It identifies three criteria health systems should consider when choosing a data platform for managing value-based payment models. Defining the data challenge: Data acquisition and reuse Individual hospitals generate and store vast amounts of patient data every day. While this information is valuable for monitor- ing performance internally, it isn't adequate for making health- care decisions for an entire population. Rather, gaining action- able insights from data requires assembling and analyzing the most complete and accurate data possible, drawing from a diverse array of sources to build a composite medical record. For most organizations, this means connecting to sources out- side of their own four walls. "First and foremost, you need the ability to get the data from all these different siloed entities, and everybody knows right now data acquisition in healthcare is messy — it's poorly coor- dinated and often requires a bunch of ... interfaces," Mr. Malec says. The greatest stumbling block for many hospital systems is their inability to cost-effectively acquire and scale data — either be- cause the data are isolated in disparate or incompatible formats or because their existing infrastructure and IT tools lack the so- phistication to scale between multiple sources. For example, traditional point-to-point sharing models be- tween systems, such as application program interfaces (APIs), are useful in isolated situations but infeasible on a large scale, as they must be built and custom fit to each individual tech- nology. Every time new data sources are required due to changing regulations, treatment protocols or quality metric definitions, data must be remapped and integrated. Manually, this lengthy process can take several months to more than a year. Using this IT model, mappings must be redone again and again as data models shift. "Point-to-point data acquisition works for data-sharing for one particular use case. But if an organization wants to share and access the same data for a different purpose, they have to ac- quire the data all over again," Mr. Malec says. This becomes especially problematic as health systems begin taking on risk for populations. Consider that a single EHR from an independent physician's office represents just one portion of a patient's medical histo- ry. Patients are likely to see dozens of primary care providers throughout their lifetime in addition to multiple providers for behavioral health, care specialists and nurse practitioners. It is highly unlikely a single care setting or EHR possesses a pa- tient's entire medical history, let alone the complete medical histories for an entire community. This is especially true for pa- tients with complex or chronic diseases who regularly receive care in multiple settings to manage their health. And clinical data is just half the story. In a value-based care model, health systems are responsible for more than just health outcomes. They are also evaluated according to cost-ef- fectiveness, patient satisfaction and efficiency, which requires keeping track of financial and operational data across facilities. Finding ways to acquire and scale data is integral to out- comes-based medicine moving forward, Mr. Malec says. Re- peatable data acquisition methods can accelerate what used to be a laborious and costly process of point-to-point integra- tion by enabling hospitals to apply standard data mapping models. A solution that leverages a variety of data acquisition methods can reach through departmental silos and system barriers, to connect hospitals to the data they need and drive insights from the new connections they make. Data policy: Managing the who, where and when of data Managing which healthcare partners can access which patient information for what purpose — known as "data-use policy" — is invaluable when dealing with patient information, especially as health systems engage increasingly complex partnerships. Data policy is a technical term for an IT framework that gives Sponsored by: CONTRIBUTED ARTICLE

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