“It’s taken a long time for the health care industry to embrace Big Data,” writes Jason Millman (@jasonmillman), “but those days are over. What that ultimately means for the industry — care providers, insurers and, most importantly, patients — is still anybody’s guess.” [“What big data could do for health care,” The Washington Post, 9 July 2014] The hope, of course, is that big data can help bring down the cost of healthcare while simultaneously improving care. Millman notes that the healthcare sector “accounts for one-sixth of the American economy.” He continues:
“The policy challenges are many, but so are the potential benefits to be realized from using data to make better health-care decisions. A few things are driving health care’s shift toward data. The proliferation of electronic health records in the past decade has made it easier for doctors to make clinical decisions and for health-care researchers to work in a much larger scale, writes Health Affairs editor-in-chief Alan Weil. Secondly, the industry is finally demanding it. Rising health-care costs and policy changes are forcing health care to transform into a system that’s more and more rewarding providers for quality of care, as opposed to just volume. That change relies on data.”
Stefan Biesdorf (@SBiesdorf) and Florian Niedermann, analysts with McKinsey & Company, assert that the healthcare industry has ridden two waves of IT upgrades and that the programs implemented during those periods have “helped create an important and powerful infrastructure that certainly will be useful in the future.” [“Healthcare’s digital future,” Insights & Publications, July 2014] The next IT wave, they insist, will involve “full digitization of [the] entire enterprise, including digital products, channels, and processes, as well as advanced analytics that enable entirely new operating models.” Obviously, advanced analytics require data to be analyzed — that’s where big data enters the picture. Biesdorf and Niedermann report that healthcare organizations have moved haltingly into the digitalized world because they have “struggled to successfully manage the myriad stakeholders, regulations, and privacy concerns required to build a fully integrated healthcare IT system.” They believe, however, that this is about to change. They explain:
“Now that patients around the world have grown more comfortable using digital networks and services, even for complex and sensitive issues such as healthcare (successful websites DrEd, PatientsLikeMe, and ZocDoc are just three examples of this trend), we believe the time has come for healthcare systems, payors, and providers to go ‘all in’ on their digital strategies. … Many digital healthcare strategies are still driven by myths or information that is no longer true. We interviewed thousands of patients from different age groups, countries, genders, and incomes; respondents had varying levels of digital savvy. Our research revealed surprising and actionable insights about what patients really want, which can in turn inform how healthcare organizations begin their digital patient-enablement journey.”
Biesdorf and Niedermann identified five “myths” that are impeding healthcare organizations from implementing digitalization strategies. They are:
Myth 1: People don’t want to use digital services for healthcare
Myth 2: Only young people want to use digital services
Myth 3: Mobile health is the game changer
Myth 4: Patients want innovative features and apps
Myth 5: A comprehensive platform of service offerings is a prerequisite for creating value
They claim that “understanding the myths and realities about what patients want from digital healthcare is vital to capturing its value.” From their research, Biesdorf and Niedermann came up with three steps they recommend healthcare organizations follow to get started along the path to full digitalization. They write:
“The first step is to understand what it is that patients really want and the best way to give it to them. Surveys and focus groups can help here, as can an assessment of what competitors are offering. Healthcare organizations can combine this information by taking stock of what kinds of services they already have in place or could easily offer — many organizations are surprised to see how much they can do with their existing technological capabilities. Next, organizations should segment their services according to basic criteria such as the amount of investment required, estimated patient demand, and value created through the service. Companies should also consider the ‘change need’ — does the service fundamentally improve some aspect of healthcare delivery? … Once an organization has analyzed the basic criteria — as well as the more complex question of change need — it can implement one or two ‘quick wins’ that, ideally, generate patient momentum and build a significant user base. And finally, just like organizations in other industries, healthcare companies should continually add new services to keep patient attention and build value. Once patients are familiar with the general idea of digital-service provision, organizations can begin offering more complex, high-value services, such as integrated-care companion apps or mobile health records.”
One of the high-value services that will inevitably emerge involves predictive analytics. “The same way that shopping Web sites can predict what you want to buy,” writes Millman, “health-care organizations could use big data to take better care of you. This could draw on traditional data already used in the health care sector, like clinical and genetic information, but also some of our gadgets.” The so-called “gadgets” are used to monitor patients (both healthy and sick patients). For more on that subject, read my post entitled “Patient Monitoring, Big Data, and the Future of Healthcare.” As I noted in that article, privacy concerns are one of the big hurdles that patient monitoring will face. Millman agrees. He writes, “Naturally, something like that — and any use of health care data, really — is going to raise some privacy concerns. And there don’t appear to be accepted standards yet for how patients agree to have their information used with predictive analytics.” He continues:
“This change won’t happen overnight, and the payoffs are potentially big. … Having a better understanding of who’s in the health-care system and what actually works could help better target care for the most expensive patients, reduce unnecessary re-admissions, avoid adverse events and more. And that ultimately means a higher-quality and less expensive health-care system.”
Analysts as Tibco Spotfire cite Millman’s article and agree that predictive analytics in healthcare could have a significant and positive impact. [“How Big Data and Analytics Can Reshape Healthcare,” Trends and Outliers Blog, 222 July 2014] They write:
“The expanding use of electronic health records by physicians, clinics, hospitals, and other practitioners is bringing together patient data that previously resided either just on paper or in siloed medical systems. This can allow healthcare providers to analyze large volumes of information quickly so they can identify and take action on their patients’ medical conditions faster and more accurately. For instance, a hospital can use a mix of patient data and research data to identify patients who may be at risk for certain conditions such as hypertension or diabetes. Hospital physicians can then use these insights to communicate these risks to patients as well as educate them on preventive steps they can take.”
Most people don’t object to the use of predictive analytics to improve health and increase life spans. What they fear is that predictive analytics will be used by insurance companies to identify high risk individuals so that they can raise their insurance rates or deny them coverage altogether. The fact of the matter is that the public doesn’t trust health insurance providers. Last year the August Kaiser Health Tracking Poll found that 44% of the public trusted doctors and nurses “a lot” with their private information but only 15% percent felt that way about health insurance companies. [“Kaiser Health Tracking Poll: August 2013,” The Henry J. Kaiser Family Foundation, 28 August 2013] The ethical use of big data analytics in the healthcare field is even more important than in other economic sectors. Analytics hold great promise for improving care and decreasing costs; but, the public needs to be convinced that those benefits are real.
