- What’s so hard about healthcare big data analytics? Pretty much everything. Technical obstacles like EHR interoperability and health information exchange are just the tip of the iceberg for healthcare organizations struggling to generate deeper insights into their clinical quality and business practices. Few organizations understand how to define their big data or where to locate it, let alone how to use it to improve patient care.
Only ten percent of organizations believe they are using their big data and analytics tools to their fullest potential, says a new survey by KPMG, despite the widespread acknowledgement that business intelligence and clinical analytics are extremely important for strategic decision making and improvements in quality care.
As competition over market share increases, accountable care starts to put the squeeze on overutilization, and patients develop increasingly complex needs that have to be managed with a higher degree of efficiency and effectiveness, healthcare providers can’t afford to be drowning in their data.
They must develop the core competencies to use operational and clinical analytics to take a proactive stance on population health management, care quality, patient safety, and revenue cycle management.
What are some of the major barriers to meaningful healthcare big data analytics, and how can providers overcome these challenges to see success in an increasingly data-driven world?
Securing the necessary support
Every organization has had its share of clashes between the board room and the boots on the ground, but the executive suite doesn’t appear to be the primary obstacle for most providers. C-suite support is strong and steady, most organizations say, and healthcare leaders have a keen sense of how important it is to engage in big data analytics.
A GE and Accenture poll from 2014 found that 89 percent of executive leaders believe healthcare big data analytics will be the key to financial success for their organizations, while a more recent HIMSS Leadership Survey unveiled at this year’s conference stated that 42 percent of providers think their executives have a “fairly sophisticated understanding” of analytics and population health management technologies.
Yet at the same conference, a survey from Stoltenberg Consulting found that 34 percent of providers blame a lack of organizational buy-in as a major obstacle to their health IT plans. In the HIMSS Leadership poll, just 29 percent of participants believe their clinical staff has a “favorable attitude” towards health IT.
But executives aren’t always the ones trying to drag reluctant clinicians kicking and screaming into the big data era. Plenty of clinicians are well aware of the perks of analytics, and actively promote the adoption of health IT within their organizations.
Physicians and nurses, especially those with some level of training in analytics, do recognize the important role that health IT can play in patient care. The HIMSS Leadership Survey found broad awareness of the fact that clinical analytics and EHR technology can improve care coordination and ease clinical quality reporting burdens. Two-thirds believe health IT can raise efficiency for primary care.
Physicians and nurses with informatics backgrounds are highly sought-after to bridge the gap between health IT theory and practice, and can be extremely effective for streamlining workflows, leading medical device integration programs, improving documentation and data integrity, and educating their peers about the positive impacts of analytics.
In order to overcome buy-in barriers that affect both the suits and the scrubs, clinicians and executives must work together to increase acceptance and awareness about the importance of healthcare big data analytics for meeting mandated requirements like meaningful use as well as organizational initiatives such as population health management or accountable care.
Defining appropriate objectives
Data analytics advocates may have an easier time convincing participants to engage in innovative programs – and wringing funding from the budget for new infrastructure – if they are able to present a cohesive and practical strategic plan for utilizing these new tools for quality improvements. But healthcare providers are having a very tough time understanding the healthcare big data analytics landscape, let alone charting a course to success.
More than half of providers responding to the Stoltenberg Consulting poll said that they have not yet been able to define their big data needs. A third don’t know what data to look for or what to do with the big data they have. Ten percent admitted that they aren’t even sure the answers are out there yet, or that the industry has come up with successful strategies for handling the massive amount of information at its fingertips.
Overwhelmed by competing initiatives and unsure how big data will help them conquer their objectives, just 56 percent of organizations in a cross-industry survey from Dell believe they have made any progress with big data analytics at all. Only 15 percent of hospitals have progressed to the point of using predictive analytics, Jvion found in another poll, with the majority of providers focused on preventable readmissions and individual patient prognostications.
That’s where most successful hospitals start, especially in advance of payment reforms that demand more efficient patient care that produces better outcomes. In the KPMG survey, 27 percent of organizations think improving clinical outcomes is one of the major benefits big data can provide.
How can organizations move from confusion to confidence? By forming strong strategic partnerships through accountable care organizations, affiliations, consultants and outsourcing, or even software vendors with deep experience in the field and a financial motivation for their customers’ success.
Providers should develop a detailed plan to leveraging existing infrastructure, add products and services, train employees on new interfaces, and create meaningful reports that prompt improved decision-making before committing too much time or money to a big data analytics program.
Implementing the right technology
Developing a healthcare big data analytics infrastructure that embraces the most forward-thinking work on EHR interoperability and health data standards is one of the most challenging pieces of the puzzle, and one continues to have providers worried.
Thirty-seven percent of respondents to the KPMG survey said that unstandardized data locked in silos was a major problem for their big data aspirations, while 17 percent added that insufficient infrastructure was holding them back. In the GE and Accenture survey, the number of providers worried about the role of interoperability in the development of a data-driven continuum of care jumped to 60 percent.
A WEDI survey from earlier this month highlighted overwhelming concerns about the difficulty of exchanging and utilizing information from multiple care settings. Only 20 percent of organizations believe integrating EHR data and other sources of information is an “easy” task. Just 30 percent have the capability to routinely integrate data in a seamless, end-to-end manner that doesn’t involve the user tinkering around with data retrieval and queries.
Health IT vendors and independent collaborations are trying to make it easier for providers to choose the right healthcare big data analytics infrastructure by tackling the problem of interoperability from multiple angles. Open architecture and standards-based products are rapidly proliferating as data exchange becomes a critical component of financial success, and the Internet of Things starts to demand deeper integration of a wider array of data sources.
When choosing an infrastructure vendor or partner, providers should be sure to clarify their stance on data interoperability and ensure that there are no hidden fees for sending, receiving, or analyzing data. Vendors who actively block the data exchange necessary for big data analytics are now under increased scrutiny from Congress and the ONC, and providers should steer clear of those with reputations for locking down their data instead of liberating it.
Building a capable big data team
Above all, healthcare organizations must secure the right human resources to make healthcare big data analytics happen. But even that is more complicated than it sounds. A dearth of data scientists is compounding the problems of a growing shortage of qualified clinical talent, leaving providers without the skills and know-how to make big data work.
Fifteen percent of providers from the KPMG poll blame the data analytics skill gap for part of their troubles. In 2013 a HIMSS workforce survey found that close to half of organizations hoping to get ahead of the curve with big data had to put their initiatives on hold when they couldn’t find the right applicants for their growing number of job openings.
Everyone is hiring – 20 percent of hospitals have immediate plans to expand their health IT staff by more than ten percent, providers said at HIMSS15 – yet building the team for big data analytics has never been more difficult.
Providers who wish to flesh out their analytics teams with informaticists, health information management experts, and data scientists may wish to take a tour of their local college campuses as academic institutions start to offer more specific training in big data management, or poach experienced experts from other industries as the healthcare data revolution begins to mirror the trials and tribulations of many other economic sectors.
Developing a data-savvy staff will help organizations secure the answers to some of the biggest questions plaguing providers about healthcare big data analytics, and help to drive more focused data-driven strategies in the future. Big data holds many promises for the healthcare industry as a whole – promises that can only be fulfilled by combining the right people with the right leadership committed to strategic success and a comprehensive understanding of what analytics can do for the quality and delivery of patient care.