Health Information Technology: Do’s and Don’ts for Today and Tomorrow

Author: Jaan Sidorov, MD and Akash Randhar

While advocates of health information technology (HIT) emphasize its role in achieving the Triple Aim of lower cost, improved health, and better care (Berwick 2008, Sheikh 2015), insurers and healthcare providers are increasingly asking just how HIT translates into greater business efficiencies, better clinical outcomes, and enhanced customer loyalty.

Thanks to the rapid pace of change in HIT, the moving targets of health reform, and the particularities of local healthcare delivery, “it depends” is really the best answer. However, that does not mean there aren’t some lessons from HIT implementation so far that can be applied across many settings.

In our roles as founders of a health tech company, we have observed several trends that may inform the collaboration between HIT service providers and buyers. Below, we offer some insights and practical suggestions about HIT and its relationship to evidence-based healthcare, clinical workflows, cost savings, and big data. We also look at the future, sounding a note of caution about whether artificial intelligence will be ready for widespread adoption any time soon, while expressing optimism about the potential for gamification to improve patient engagement.
Today: Evidence-based vs. Innovative
While businesses in every economic sector conduct research, healthcare’s large investment in evidence-based practice is noteworthy. History, training, and culture have led generations of physicians, applied health professionals, academics, and regulators to routinely apply considerable scientific scrutiny as well as skepticism to reports of new diagnostics or treatments. It’s no surprise, then, as providers, insurers, vendors, pharma, and other stakeholders compete for market share, claims of new HIT-based innovations continue to prompt old questions on whether the underlying data are tainted by bias, poor design, limited generalizability, questionable effectiveness, and unknown or unintended long-term effects.

To the frustration of investors and management teams, the pace and proprietary nature of much innovation in HIT is ill-suited to the pace of traditional peer review. Fortunately, the number of biomedical publishers has expanded, and many have expedited their review process and offer online publication. Two telling examples of the risk of failing to take advantage of this are lab provider Theranos and some direct-to-consumer skin care smartphone apps, which rushed to market with little peer-reviewed evidence to back up their claims and abysmally failed (Ioannidis 2016, Resneck 2016).

As a result, HIT service providers and their customers should routinely contemplate investing in gathering, interpreting, and reporting their impact on Triple Aim-based outcomes with every customer in a peer-reviewed forum. Innovative healthcare entities that neglect healthcare’s reliance on evidence-based medicine and go to market without the benefit of any peer review, do so at their peril. While time-consuming and expensive, promotion without accompanying proof can ultimately be far more costly.

That is not to say that a fast pace of innovation is bad for the healthcare industry. However, a careful consideration of the scope of rollouts and design of supporting studies can achieve getting the latest in the market quickly while verifying that it is, indeed, the greatest based on well-designed studies. Moreover, as healthcare evolves so individuals can make treatment choices based on quality, price, and convenience, researchers can adopt some of the practices used in the retail industry, such as quick parallel studies, to develop evidence that has traditionally come from randomized trials, observational studies, and case series.
What is Health Information Technology?
Health information technology (HIT) involves the exchange of health information in an electronic environment. Widespread use of HIT within the healthcare industry will improve the quality of healthcare, prevent medical errors, reduce healthcare costs, increase administrative efficiencies, decrease paperwork, and expand access to affordable healthcare. Smartphones and smartphone enabled are part of the HIT ecosystem.
The expedient adoption of health technology can be additive, not substitutive. Absent modification of existing job descriptions or workflows, frontline employees who are asked to adopt new technology will have to grapple with additional roles, new policies, unfamiliar procedures, extra oversight, and unforeseen problems. For them, the addition of more HIT—no matter how innovative it might be—inevitably leads to more work. Absent the concurrent assignment of incumbent low-value duties to machines or the dustbin, layering more HIT on top of a group of busy employees turns them into even busier employees. The introduction of HIT often means copying and pasting across multiple applications scattered across two or more devices rather than added efficiency.

Outside the travails of the electronic health record (Mandl 2012), this additive downside to HIT has gone largely unexamined in the peer-reviewed medical literature. Other reports describing this problem have noted the importance of obtaining frontline worker input and fostering collaboration across multiple vendors (Perna 2014). In our experience, importing a HIT solution also should serve as an opportunity to review which legacy tasks can be modified or discarded. Buyers also should be wary of “middleware” and quick fixes, which tend to add complexity over the long term. Instead, they should consider implementing staged rollouts of HIT-based innovations using a plan-execute-evaluate-adjust (“PLEXEVAD”) strategy.

Additive work often happens when new technology is not accompanied by adoption of the new ways of doing things by the end users of the technology. Without those changes, the promise of technology will often go unrealized. For a healthcare organization, that can mean wasted time, money, and employee goodwill.
Grappling with insurance risk-transfer
One value proposition for HIT includes the reduction of avoidable healthcare utilization or costs. However, successfully decreasing claims expense often translates to a combination of 1) a very real loss of provider revenue, and 2) an abstract calculation avoiding healthcare utilization. Both are reduced further by the direct and indirect costs of HIT’s associated personnel and capital. As the art and science of shared-risk arrangements continue, it should be recognized that as the parallel role of HIT expands, gauging just how it “bends the cost curve” involves multiple care settings and has wide confidence intervals (Asch 2016).

To effectively navigate this challenge, both HIT providers and customers need to account for the health burden of the insured population being served, the impact of social determinants, baseline expenses, insurance claims trends, background cost inflation, and local provider network performance. Without this knowledge, calculations of the economic value of a particular HIT-based innovation in a particular setting for a particular population may not be a question that can be precisely answered.

Healthcare correlations derived from the analysis of huge, multisource datasets need to not only render meaningful insights, but be accompanied by actionable opportunities that can be scaled to available resources. In other words, the “big” of “big data” needs to be boiled down to a manageable number of achievable interventions for a manageable number of patients. For example, while the clinical issues and social determinants underlying an increased risk of rehospitalization have been the subject of considerable research, far less is known about prioritizing and modifying these determinants so that the few patients who are most likely to benefit are selected for the right intervention.

As population health and care management spreads to more and more consumers, the value proposition of HIT will include access to insights that give the greatest impact on cost and clinical outcomes. Once that is achieved, the experience can inform additional interventions for additional numbers of patients in a virtuous cycle of continuous improvement.
The ecosystem and gadgets
Early versions of HIT were single source, end-to-end, and complex. This has given way to a networked and decentralized array of smaller and interchangeable billing, claims, health record, laboratory, imaging, data warehousing, and analytics components (Surviving 2016). As a result, healthcare’s chief technology officers increasingly preside over complex “ecosystems” of local and remote software and hardware. As with other systems, the whole becomes greater than the sum of its parts. The growth of HIT means the merits of upgrading, swapping, or supplementing any part is dependent not only on their individual functionality, but also on their interdependent compatibility and synergy.

Given this reality, the incorporation of consumer apps and monitoring devices into the HIT ecosystem is less revolutionary than evolutionary. While the consumer allure is undeniable, the ultimate value proposition of these apps and gadgets will depend more on their ability to enhance the consumer and provider experience by supporting, for example, patient-centeredness and shared decision making (Moore 2006). Their potential in these and other areas of the Triple Aim has only just begun to be documented in the peer-reviewed literature. The healthcare app and device vendors that can prove they create value in this ecosystem will have a key competitive advantage.
Tomorrow: Humans plus health tech
While artificial intelligence promises to completely outsource much complex decision-making to machines, the experience in many nonhealthcare settings with established robotics is that computers plus human insight make for greater efficiency and effectiveness than either alone (Automation 2015). In addition, the art and science and associated cost management of healthcare delivery are still a matter of limited knowledge, insufficient evidence, myriad logic exceptions, and very human irrationality (Eichner 2010). While expedited access to scientific databases and the generation of potential diagnoses and treatments are well within reach, it remains unlikely that diagnostic and treatment guidelines will be translated into accurate computer code in the near future (Semigran 2016). The superiority of having live subject matter experts enter the HIT loop for even “simple” clinical tasks, such as giving advice to patients on how to successfully use their hearing aids, suggests that for now, HIT will remain a decision support tool rather than a decision substitution tool.

The change to a more automated approach to management of hearing loss and other related disorders should not be discouraged; however, a note of caution about implementation is warranted. Adopters of cutting-edge HIT should be skeptical about claims that we are on the cusp of HIT that is independent of any human oversight. If implemented prematurely, the result could be a limited menu of one-size-fits-all care options or a high incidence of exceptions. For now, the artificial intelligence version of “Dr. Watson” that is fully independent remains experimental. It is best to leave it in the labs of researchers or to your competitors.

Until now, mainstream efforts to improve diet, exercise, medication adherence, or provider appointments have had limited success. This is because such efforts are largely based on 1) educational appeals to improving personal health status or 2) the use of economic incentives to change behavior. The former has had a disappointing track record, while the latter have substantial cost and regulatory limitations.

Enter the alternative of healthcare “gamification,” in which consumers pursue healthful behaviors by competing for noneconomic and symbolic awards. This is emerging as a surprisingly effective tool in motivating behavior change, and its science is still evolving. The phenomenon of millions of Pokémon Go users increasing their physical activity levels in the pursuit of virtual avatars is just the latest, if very public, example of the potential low-cost synergies of gamification and HIT (Althoff 2016).

Gamification has been the subject of a considerable amount of applied research (King 2013) and, in contrast to artificial intelligence, may be ready for adoption in many healthcare settings, including audiology. Once this tipping point is achieved, the disruptive technologies that support gamification for health promotion and disease management are likely to transform patient education and engagement. As a result, we predict early adopters will have a competitive advantage.
As HIT service providers rush to provide innovative solutions in the healthcare marketplace, they will need to manage multiple challenges all at once. They and their customers will need to meet the expectations of evidence-based healthcare, deliver on the substitutive promises of innovation, avoid burdening physicians with additional tasks, grapple with risk-transfer calculations, leverage big data in the service of achievable outcomes, and serve as one of many components of an informatics ecosystem that also includes patient apps and other gadgetry. While artificial intelligence holds great promise, the even greater complexity of healthcare decision making means its adoption is likely to be delayed for several years. In the meantime, the limitations of traditional education and incentives and the surprising appeal of handheld games makes “gamification” the next frontier of consumer engagement. HIT vendors and customers that succeed in these key areas will be the most likely to succeed in achieving the Triple Aim.    
Jaan Sidorov, MD, a primary care physician, serves on the editorial advisory board of Managed Care and is the chief medical officer of Medsolis Inc., a HIT company.

Akash Randhar is the CEO of Medsolis.
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