Managing the who, where and when of data — 3 keys to success in value-based care

As healthcare organizations begin to apply analytics to care delivery, data has become a hospital's most valuable asset — and one of the most challenging to manage.

 This content is sponsored by RelayHealth

Today, technology is driving new avenues of communication, which support the transformation of fee-for-service healthcare into collaborative medicine. Sharing patient data across multiple independent care settings can help physicians avoid duplicative 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 presents a host of complex security, privacy and operational challenges 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," especially when dealing with data on a large scale, says Arien Malec, head of research and development at RelayHealth. Connecting 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 sustain.

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 inefficiencies 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 monitoring performance internally, it isn't adequate for making healthcare decisions for an entire population. Rather, gaining actionable 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 outside 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 coordinated 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 because the data are isolated in disparate or incompatible formats or because their existing infrastructure and IT tools lack the sophistication to scale between multiple sources.

For example, traditional point-to-point sharing models between 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 technology. Every time new data sources are required due 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 acquire 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 history. 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 patient's entire medical history, let alone the complete medical histories for an entire community. This is especially true for patients with complex or chronic disease 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-effectiveness, 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 outcomes-based medicine moving forward, Mr. Malec says. Repeatable data acquisition methods can accelerate what used to be a laborious and costly process of point-to-point integration 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 users a high degree of control over their data sources. Specifically, data policy enables users to manipulate who can access what data for what purpose and when. Controlling data access is essential for healthcare organizations, as some data are appropriate for one organization to see at one time, but may be inappropriate for another partner organization to see at a different time.  

Consider EHR data shared between a hospital and a physician group participating in an ACO. It's helpful and appropriate to share patient information for that specific population when making healthcare decisions, such as planning interventions or offering new services. However, it is inappropriate for a hospital physician to access all of the patient records in the physician group's EHR for the purpose of poaching prospective patients.

"[The hospital and physician group] are working together for one purpose, but there may be other situations in which they're competing, and it's not appropriate from a HIPAA or business relationship perspective for all of that data to be accessible to either organization all the time," Mr. Malec says.

As organizations establish new data sharing agreements with more providers for value-based care, the data needed to support these relationships grows increasingly complex. A solution that doesn't incorporate data use policies requires health systems to acquire the same data multiple times and store it in multiple ways to ensure that only the right data is seen by the right person for the right purposes of use.

"Selecting vendors who understand the complexity of configuring and enforcing this type of policy is critical to a health system's strategy," Mr. Malec says.  

A call to action: Choosing IT to support value-based data needs

Lowering utilization rates and improving health outcomes depends upon data that are complete, consistent, accurate and accessible. Getting data on that scale is an ambitious undertaking, but an achievable one with a data platform that accounts for an organization's specific needs and capabilities. Health systems should consider the following three criteria when choosing a data platform to best position themselves under value-based care, Mr. Malec says.

  1. Build a data roadmap. Many healthcare organizations are focused on automating data capture, acquiring data and getting it together in one spot, rather than strategic planning or accounting for how data are used meaningfully on the backend. When this happens, organizations may encounter a number of setbacks, including incompatible data formats or inefficient data flow. Instead, health systems need to establish an acquisition strategy that starts with the end in mind.

    "Organizations need to start by being thoughtful about where they're going in the next five years," Mr. Malec says. "Consider what kind of value-based payment programs you will be participating in, what data you'll be sharing, what organizations you'll be sharing that data with. And then make sure you're talking to you IT partner about your upfront needs and future data capabilities."

  1. Formal data reuse methods. Repeatable data acquisition can accelerate what used to be a laborious and costly process of point-to-point integration by enabling hospitals to reuse standard data mapping model, Mr. Malec says.

    For example, with a robust data platform, EHRs can be premapped to a canonical format, which means new sources don't have to be manually mapped to one another to share information. Rather, sources are mapped to a predetermined standard, which means that new interfaces can come online within days rather than months and at a much lower cost.

    Data acquisition is a special competency that many organizations have not yet developed. Without the right tools, training and support, acquiring, aggregating and consuming crucial data goes beyond the skillset traditionally associated with data analysts. For this reason, many organizations see value in partnering with advisors to develop appropriate acquisition and data-use strategies.

  1. Built-in, flexible data policy framework. The more partnerships a health system participates in, the more complex data-sharing and data-use becomes. Organizations that don't carefully think about what data is shared with what people at what time increase their risk of giving an inappropriate party access to valuable data.

    "Health systems need a flexible IT infrastructure which allows them to get down to that level of keeping track of what exact data from which organizations can be used for which purposes for which patient — the key data rights you need to satisfy to make sure you're on the right side of HIPAA," Mr. Malec says. Choosing a data platform with a built in policy framework can help organizations manage their data sources from the start.

Conclusion

To survive in the changing healthcare landscape, organizations need to define a plan for how to tap into the value of data across their enterprise. This includes building the skills and processes and employing the right enabling technology to transform raw data into information that drives strategic value. With proper support from executive and IT leadership, a robust data platform helps organizations better position themselves for success under value-based medicine.

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