What are the challenges in applying technological and business model innovations to the healthcare sector? How will these innovations impact the quality and cost of healthcare?

Contributors: Prof. Yunjie Xu, School of Management, Fudan University; Dr. Jianwei Xuan, Adjunct Prof., School of Public Health, Fudan University, China; Prof Ana Malik, FGV-EAESP; Mr. Martin Burger, Senior Industry Advisor Healthcare, SAP APJ; Ms. Betsabeh Madani, Corporate Business Strategist, Cerner Corporation; Prof. Martin Pohl, University of Tsukuba; Prof. Scott Wallace, Geisel School of Medicine, Dartmouth College

Written by Prof. Edward Yagi.

iStock_000051791398_Large©Avatar_023Novelty versus Value

Successful innovation requires a focus on the value of innovation, rather than the novelty of innovation. “Value” in this sense means, at a minimum, a practical purpose or application that fulfills an unmet need, as well as clear financial viability: ideally benefits that equal or exceed direct and indirect costs. Value orientation requires an understanding of key stakeholders, and in the healthcare environment this requires tracking of the full cycle of patient outcomes. From a clinical efficacy perspective, the data required includes overall survival, quality of life (QOL), and progression-free survival (the percentage of people in a study or treatment group who are alive for a given period of time after diagnosis, referring to the span of time during/after medication or treatment in which a condition does not get worse) It is important to note that patients value QOL more than progression-free survival.

Future medical checkups: no doctor requiredStrategy versus outcome

Two important trends can be observed in the application of technology in healthcare: using technology to advance strategy, and using technology to improve measurement of outcomes. While there has been effective tracking of inputs in patient interactions, such as medications administered, there has been little measurement or tracking of patient outcomes relative to their healthcare experiences. This lack of measurement will have to be addressed as information technology enters a new phase of maturity.

The Challenges

Among the major challenges ahead is included the question of fragmentation among existing systems. Hospitals, physicians, and occupational services all currently have their own systems, typically with none of them connected well, if at all. The question is how to integrate these systems to create value for the patients. One approach is to use incentive models to bring the different players together. For instance, Germany introduced a model driven by health insurance players that encourages general practitioners and hospitals to work in tandem.

Getting physicians to actually use available technology is also a point high on nearly everyone’s agenda. Generally, healthcare is behind many other industries in degree of automation. There is also deep segregation between those who are relatively advanced and those who are far behind. However, in order to adopt new technologies physicians need to know what technology is available, and in many cases government involvement may be an indispensable factor (generally speaking, any large-scale investment is always at least somewhat influenced by government decisions). After Japan’s 2011 Tohoku earthquake and tsunami disaster Japanese physicians requested that the government install an information system to allow them to access medical data from locations other than a patient’s primary healthcare facility. Even a short distance from where they had lived, many patients needing treatment were disadvantaged because the available physicians had no access to their new patient’s medical histories. A key follow-on from this finding is that in order to reduce investment in required training, technology must be designed for non-technical users.

Predictive healthcareThe future: Predictive healthcare

Finally, the field of personalized medicine provides an important point on which to focus attention. While personalized medicine can potentially bring great benefits in terms of more effective and targeted treatment, there remain challenges to be overcome to enable its widespread adoption, including commercial viability, the point at which it becomes inefficient to test for low-yield pathologies (diseases that are expensive to treat but are relatively rare and/or expensive or difficult to test for accurately), and the large amount of tissue required for separate pathology testing for multiple procedures. There are studies underway using predictive models for the likelihood of patients having, for example, gene mutations. With this predictive data, physicians can test patients with a high probability of susceptibility or resistance to certain forms of testing or treatment, thereby directing technology towards patients who will benefit from the technology while avoiding testing or treatment that has a high probability of being ineffective, inefficient, or counterproductive.

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