The report, "U.S. Hospital Health Data Analytics Market," makes the assertion that investments in analytics software will closely follow the implementation of electronic health records (EHRs). As of 2011, approximately 35% of U.S. hospitals had implemented either a basic or comprehensive EHR. By 2016, Frost & Sullivan forecasts that 95% of U.S. hospitals will have EHR systems in place, representing a 22.1% CAGR.
Legislative mandates, specifically, the Health Information Technology for Economic and Clinical Health Act (HITECH) of 2009 and the Patient Protection and Affordable Care Act (PPACA) of 2010, are driving the shift toward investments in analytics tools soon after the implementation of EHRs. These laws call for U.S. hospitals to implement and use technology, including data analytics, to improve quality measures and patient outcomes.
Health data analytics, which the report describes as "advanced analytics techniques applied to clinical, financial, and administrative data that is used to improve the quality and efficiency of patient care," will become more ubiquitous over the next three to five years.
[ Is it time to re-engineer your clinical decision support system? See 10 Innovative Clinical Decision Support Programs. ]
As the nation modernizes its health information infrastructure, the report notes that historically, healthcare delivery organizations have implemented business analytics that focused on financial and administrative systems. During the last five years, however, implementing clinical IT systems such as EHRs has raced to the top of the priority charts.
Still, the report asserted that the majority of providers have not yet applied advanced data analytics tools that can access information from EHRs to gain actionable insights from this information. The report also noted that providers have yet to integrate clinical information with financial and administrative data--a process that must occur if hospitals want to implement a comprehensive data analytics strategy.
"To transform healthcare, all data have to come together in order to get a clear picture of what is happening with individual patients and patient populations in terms of clinical treatment and outcomes, costs and reimbursement, and resource utilization," said Nancy Fabozzi, principal analyst covering Healthcare at Frost & Sullivan, the author of the report. "Hospital executives will increasingly view these data [elements] as a core asset that must be leveraged to support every organizational goal, including financing, reimbursement, recruiting, and--most importantly--patient care."
The good news is that hospital CEOs, CFOs, and CIOs are fully aware that they need to elevate their analytics capabilities, which means channeling additional investments toward a new technology infrastructure to support that function, as well as establishing new processes and workflows around data governance and oversight of this critical asset, Fabozzi said in an interview with InformationWeek Healthcare.
So what will it take to build and leverage a hospital's data assets?
"Integration is the first hurdle," Fabozzi declared. "Some hospitals, mostly larger integrated delivery network (IDNs), and/or academic medical centers, are building data warehouses to integrate data from numerous disparate systems within the enterprise so that the data is amenable to robust analytics."
The second big task is getting hospitals to agree on basic key performance indicators (KPIs). As the health industry enters an era of more government regulations, government will determine many KPIs.
"The need to report on a growing number of metrics around healthcare quality and safety as well as to report to growing numbers of constituents (e.g., local, federal, and state regulators, government and commercial payers, patients, employers, etc.) will drive the need to present the most complete data on KPIs," Fabozzi observed. "Providers may want to develop special programs and metrics around quality that go beyond government regulation in order to gain competitive advantage."
Other challenges that will confront healthcare IT executives as they implement health data analytics tools, according to the report, include:
-- High implementation and licensing fees. Implementation and subscription/licensing fees for advanced analytics are just one more financial burden and can add to the budgetary woes faced by many hospitals today. Advanced health data analytics systems can cost over $100,000 for implementation and about the same for annual subscription/licensing fees, making it difficult for hospitals to justify spending, at least in the short term when they are so focused on implementing EHRs and upgrading revenue cycle management (RCM) systems.
-- Communication barriers in hospitals that slow information sharing. Many hospitals have siloed departments and service lines that operate as independent units. They have not previously had to share data across multiple departments within the same hospital, much less across an entire IDN.
-- Lack of standardized health data that affects analytics use. Inconsistency in capturing and defining data limits the use of clinical data for patient care metrics. Hospitals are increasingly working to integrate and standardize data across various operational units. However the lack of robust and consistent data standards will likely limit the uptake of advanced health data analytics to some extent.
-- Continued use of existing basic analytics tools that restrict the implementation of newer solutions. Many hospitals, particularly small and rural hospitals, continue to rely on their existing, rather basic, legacy business intelligence systems--think Excel and Access. These tools are often only used at the department level and likely use only retrospective data to develop reports.
According to Fabozzi, many hospitals intend to continue using business intelligence tools that focus mainly on financial and administrative data, but change is inevitable.
"Realistically, a lot of hospitals will continue to use these processes for some time because of all the other aforementioned barriers to changing over to newer systems," Fabozzi said. "Legacy business intelligence tools will be seen as "good enough for now" until it just becomes too inefficient to continue."