By Cassandra Balentine
Data management tools target and serve a variety of industries. Healthcare is one vertical to benefit from these solutions. In addition to providing ease of use for accessing and managing data, advantages include the ability to collaborate once siloed databases to provide a complete patient view and allow for analytic functions.
Health data management (HDM) solutions help enable care providers to access, understand, and leverage data in order to generate a holistic patient view. A variety of software vendors offer HDM solutions designed to deliver a data management framework specific to the needs of healthcare. With proper HDM in place, these organizations can optimize processes, reduce costs, and transform disparate data into a useful source for predictability and preparedness.
This article discusses the role and benefits of data management specific to healthcare organizations of all types and sizes.
Smart Data
The role of big data and data management solutions is well known across a variety of industries. For healthcare, the need for good, smart data is clear.
Richie Etwaru, chief digital officer, IMS Health, notes that healthcare is no stranger to too much data. “The industry has a complex set of value exchanges between stakeholders such as manufacturers, healthcare providers, payers, pharmacists, patients, and a host of other stakeholders, resulting in massive amounts of data stored at every stop or start of the exchange of value between stakeholders,” he explains. “Like most other industries, healthcare stakeholders from patient to manufacturer are looking for insight, insight to drive decisions about therapies, behaviors, costs, and dosages.”
Etwaru says IMS Health defines insight as precision plus context. “The first function for HDM is precision. While the industry is awash with big data, we are not sure that it is necessarily good data. Good data can be loosely described as big data that is precise,” he offers.
Data precision stems from the horizontal consolidation of multiple silos of information, mastering of key data attributes to create golden copies of anchor records, and consistent processes to maintain data precision at required levels. “These processes can be examples such as data stewardship services or quality refreshes of sources known to deteriorate in precision over time,” he says.
The second insight function is context. Etwaru says context is a bit more elusive than precision. “Big data that is precise can be good data, but that data may not be smart. We believe that good data within its appropriate context is smart data,” he explains. He says that we can think of big data as stuff that we know. Good data as stuff that we do not know—but can ask. And smart data is the stuff that we didn’t know that we didn’t know. “Smart data requires a set of companion algorithms that are tested for efficacy over time, an analytic capability to drive reporting within context, and some amounts of sematic and language processing to take advantage of vast unstructured data stores,” he says.
Healthcare organizations rely on the ability to manage unstructured content and clinical data to achieve a standard view of all data—regardless of origin or type. Patty Lehan, director of marketing, North America, BridgeHead Software, suggests that these organizations also need to make the data easier to use.
At a minimum, Tom Suk, senior director, product management, LexisNexis Risk Solutions, suggests that data management solutions need to be able to ingest and process data feeds from multiple sources in multiple formats. From there, they must be able to integrate data accurately and provide end users with the ability to view, manipulate, and report on their information. “Healthcare demands that data management solutions be able to create a golden profile for patient or provider from these disparate data sources,” he says.
“Wherever your organization lies on the healthcare continuum—hospital, integrated health system, health insurance provider, physical practice, or medical lab—the greatest technological challenge is interoperability,” states Kathleen Aller, director of HealthShare Business Development, InterSystems.
She notes that the ability to integrate all forms of data, both structured and unstructured, from different systems and organizations across the entire health and social care system in a way that makes it usable is absolutely essential.
This is critical because the average person sees several health professionals in a variety of care settings. “For the most part, each of those care providers maintains a separate record of care—so none of them can effectively see and treat the whole person. The federal government has invested heavily in individual electronic health record systems (EHR) for care providers. Now we need to ensure those systems can interoperate so we have comprehensive electronic patient records that unite data from these multiple care providers while also protecting the privacy of patient data,” says Aller.
She notes that the healthcare industry has the most demanding data requirements for reliable and comprehensive, up-to-the-minute data. That data informs life or death decisions and can lead to better health outcomes for individuals and populations. “This is the promise of truly connected healthcare,” she says, adding that it is no small task.
Within the general software industry, data management refers to the systems and procedures that a company uses to manage data throughout its lifecycle. According to Vince Marinelli, lead architect, data analytics, Medidata, this encompasses a broad area that includes master data management, data storage systems, and data retention and access policies. However, within the sciences, Marinelli says data management can also relate to the systems and processes used to manage data for an experiment. For some areas of healthcare, such as clinical trials, both definitions apply.
Advantages of Data Management
The biggest advantage of HDM is the possibility for healthcare organizations to achieve a true electronic patient record that includes all of a patient’s relevant information. Lehan notes that this allows clinicians to more easily access data for improved outcomes.
“Better information drives better insights, and as a result better decisions and better business outcomes. Every stakeholder in healthcare stands to benefit from the improved quality of information that can result from advanced data management,” says Etwaru. “The consolidation of a patient’s data from multiple providers into a single view that’s simultaneously processed around patient data to increase the precision of each patient record, coupled with analytics to help deliver care insights to healthcare providers, can improve—if not save—lives dramatically. The benefits to other stakeholders pales in comparison to the impact that precision and context can have on patients’ lives,” he adds.
Aller suggests the right data management infrastructure is necessary to improve health outcomes for patients while containing costs. “These are two key mandates laid out in the Affordable Care Act,” she states. “There are a lot of programs in place to address those goals. One is to reduce unnecessary hospital readmissions—a costly problem that arises in part from poor adherence to care plans after the patient is discharged. The state of RI, which implemented shared patient records through a public health information exchange called CurrentCare, was successful at reducing on-month readmissions,” shares Aller. In this example, RI’s rates decreased as much as 18 percent for potential cost savings of $1.4 million when primary care providers received notifications about hospital discharges.
Another benefit of data management is preparedness. Marinelli suggests that as connectively becomes more ubiquitous, new opportunities and models for the conduct of clinical trials become possible. “In the coming years, a flood of data from things such as wearable sensors, smartphones, and mobile applications promise to disrupt the currently understood methods for conducting clinical trials. Planning for the coming big data flood requires mature data management processes and systems to be in place,” he adds.
Compliance is another consideration. The ability of data management platforms to help meet regulatory criteria is another advantage. “The Health Information and Accountability Act was originally enacted in 1996 to ensure the confidentiality and availability of patient records, but has recently evolved with the Omnibus Rule, which adds data privacy regulations and stricter penalties,” says Jim Laux, senior manager, Business Imaging Solutions Group, Canon U.S.A., Inc. “With these changes and more enforcement, those impacted by non-compliance now include business associates of medical offices, as well as civil penalties that can reach up to $250,000 for first-time offenses and up to $1.5 million for repeated offenses,” he adds.
This adds incentive to ensure all EHR and patient information is secure and meets applicable compliance standards.
Laux points out that a main component of the Affordable Care Act requires physicians to increase their interaction with patients while still providing high-quality care. “Advanced image management systems can allow professionals to instantly capture, store, query, and display all patient studies from a single database from nearly any location, whether in the same room or thousands of miles apart,” he explains. With this increased portability of patient data, physicians are better able to collaborate and coordinate care with other specialists.
Primary Challenges
Taking the next step to combine and leverage healthcare data comes with both operational and organizational challenges that must be addressed before full benefits are achieved.
Lehan notes that BridgeHead Software has seen HDM emerge from the back room of a hospital’s “plumbing” to its newer position as a primary, clinical application. “The former infrastructure of hospital IT has been elevated to a more strategic role, thereby, causing the definition of HDM to be revaluated.”
She explains that hand in hand with the growth of HDM is the new role of the electronic patient record. “Healthcare organizations aspire to create a single electronic patient record that allows clinicians to easily access data. However, most hospitals have yet to achieve the singe patient record.”
Lehan says HDM is an increasingly important application because it’s part of the patient record, which is integral to the way healthcare is delivered.
Etwaru says challenges to HDM are technical and operational in nature, but that many hurdles are organizational. “Healthcare companies are relatively more vertical in organizational structure than companies in other industries,” he observes. “Pharmaceutical manufacturers for example are vertical regarding brand or regional verticals. As a result, data is owned by each vertical and this creates a set of ownership and governance strains that run counter to horizontal consolidation of data.”
Healthcare organizations are also dealing with complex technology integration issues exacerbated by industry consolidation and merger and acquisition activity. “As large healthcare systems grow by acquisition they are faced with enormous technical challenges. How do you cleanse and transfer all of that legacy data? How do you create a single comprehensive patient record without having to rip and replace all of your existing information systems?’” asks Aller.
Healthcare providers need to start with the data infrastructure and ensure they have a strategic informatics platform that enables interoperability and long-term growth. “One large health system in NY, which has invested in such a platform, described it as ‘the most strategic investment we have ever made,’” she says.
When looking at specific sectors—such as the clinical trial space where Medidata plays, the nature of many systems used to support the planning and exaction is a major barrier to good data management practices. “The software systems currently on the market support only certain aspects of clinical trials,” suggests Marinelli. Current systems duplicate master data, representing it in disparate entity models that often lack common identifiers. “All of this data needs to be mapped together for each clinical trial—at great cost to the company executing the trial,” he says.
The cost, time, and resources associated with this process are multi-layered. “At the highest level, the cost of joining together all of these datasets represents the development expense, but there is also a large opportunity cost related to the delays between data collection and assembly into meaningful structures. At a lower level, the fact that time-based metadata regarding common clinical trial business practices is currently spread across systems means that it is practically impossible to leverage it all to obtain an overall view of process efficiency. The end result is that pharmaceutical executives are not able to leverage all of the data they collect, imparting their ability to make decisions as efficiently and effectively as possible,” says Marinelli.
He explains that certain standards within the industry can help in some areas, but the standards themselves were historically built around the same business verticals. “There is work underway now to align these standards under a common umbrella, but our experience has been that the rate of adoption has been slow, absent mandates from regulatory bodies,” he offers.
What to Expect
The healthcare industry is currently in the process of moving to big data. “In the next three to five years, the realization of smart data will create a condition where various healthcare organizations will become information companies. Smart data with precision and context enabling meaningful insights will create a set of operational conditions where healthcare will transition from making best guesses on historical processes and tendencies to evidence-based decision making predicated on best practices and high-quality, robust real-time inputs,” offers Etwaru. “Few industries have as much at stake when it comes to making crucial decisions as healthcare,” he adds.
As medical data becomes more complete and accessible, hospitals realize new ways to optimize data. “These new opportunities might include advanced data analytics, which help unlock new information buried in hospital data,” says Lehan.
One growing trend on the rise is wearable, mobile, and home medical devices, which Allen refers to as the healthcare version of the Internet of Things (IoT). This is creating an explosion of device-generated data. “Providers don’t want to be inundated by personal device data. They need advanced data management tools that will deliver not all the data, but the relevant data—in context.”
Marinelli says that historically, the pharmaceutical industry has been conservative in the adoption of new technologies, but it is at an inflection point. “There is growing awareness of this need for data management on a broader scale within the industry, driven both by market pressure and the growing realization that proper management and leveraging of a company’s data assets is a requirement for 21st century businesses.” He says that big data and the IoT will drive advancements within the industry. “We can expect to see expanded use of external data sources, many of which will deliver high data volumes. The industry will need to rethink, or at least revisit, data management issues such as data retention policies, privacy protection, and regulatory oversight as they apply to big data.”
Suk believes that in the next three to five years, we can expect to see more self-service offerings for data management in healthcare, more firms outsourcing data management to industry experts, and a greater push for real-time interactions.
Savvy Healthcare
HDM strategies allow healthcare organizations to manage data for compliance and daily processes. However, the power of big data provides the opportunity for much more. SW
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Dec2015, Software Magazine
