What’s Next in the Digital Health Transformation?

Five questions I'll be asking at HIMSS20

The annual HIMSS (Healthcare Information and Management Systems Society) conference is always an exciting opportunity to get updates on the most recent advances and trends in global health information and technology. This year’s event in Orlando, Florida (March 9-13), won’t be an exception.

While the coronavirus will be top of mind for many, and we’ll all be elbow tapping, I’ll also be looking for examples of Human Data Science in practice. Human Data Science is an emerging discipline that blends human health with human science, data science and technology. It offers an integrated, multidimensional approach to help with the acceleration of the digital health transformation by fostering true convergence between healthcare and technology. To learn more about this discipline, see the recently published report from the IQVIA Institute: Advancing Human Data Science – A New Approach to Improve Human Health Outcomes.

Specifically, I’ll be asking the five questions listed here and looking for insights that that will help advance the broader field of digital health transformation.

1. Is there compelling new evidence of impact on patients and costs?

Scientific evidence remains the bedrock of innovation in healthcare accelerators. Successful digital health transformation will require three fundamental stages: innovation, evidence and adoption. Innovative and disruptive new approaches using enabling technologies to improve patient care, cost management and public health are fundamental prerequisites. However, in order to gain wider traction these innovations must be validated based on rigorous, published evidence and be able to demonstrate replicable models that have been tested across different health systems, patient segments and markets. Otherwise, mainstream adoption by payers and providers will be very slow or not happen at all.

According to the HIMSS20 program, we will see some evidence of impact, but it will be interesting to ascertain the quality of the data and the evidence. Despite its potential promise of digital health in leading the healthcare transformation, the two worlds of technology and healthcare are very different and not naturally convergent, and they still have a lot to learn from each other. 

I’ll be looking to see how much has changed since our tracking of the maturity of digital health efficacy studies. I’m particularly interested in joining discussions about changes in clinical deadlines to reflect the role that digital health tools can play – that’s the real mark of maturity.

2. Are there new approaches to protecting patient privacy while gaining the benefits of data availability?

Patient privacy and data protection are vitally important to ensure public and patient trust in the digital health transformation. In principle, all stakeholders agree that patient privacy must be protected in the exchange of health information. However, vulnerabilities around security and protection of patient privacy have been detected in the practical application of patient apps and platforms, which tend to undermine the trust in health information technology. The development of industry guidelines backed by broad stakeholder interests will be key to secure broader adoption. I will be interested to hear about new approaches to ensuring patient privacy around the sharing of larger data-sets, and the level of attention this is getting from stakeholders.

3. How are HIMSS members adapting in light of next generation biotherapeutics and biomarkers?

About 100 next-generation biotherapeutics – defined as cell, gene and nucleotide therapies – are under clinical investigation for oncology in the U.S. with the potential promise to deliver important breakthrough therapies over the next several years. Biomarkers that stratify patients likely to respond to therapy are now included in 39 percent of oncology trials, up from 25 percent in 2010, reflecting that precision medicine approaches are becoming more commonplace. These scientific advances will transform the practice of medicine in many respects.

I will be looking for how leaders in technology and information management are viewing this transformation and the role they will play. This might include new approaches to capturing and using information to deepen the understanding of the natural history of disease, in particular rare diseases, integration of diagnostics with point of care solutions, and delivery of patient care based on cell and gene therapies. I’ll be looking for a clear and conscious distinction from the tools used in and around more traditional medicines.

4. How widely are social determinants being incorporated into information management tools and analytics?

Recent studies have demonstrated that factors outside of the traditional healthcare field – and beyond the typical data captured in EMRs or insurance claims when an individual engages with the health system – have significant impact on life expectancy and health outcomes. A population epidemiology study in JAMA (Journal of the American Medical Association) demonstrated the decline or stagnation in life expectancy in the U.S. due to “deaths of despair” – drug overdoses, suicide and alcohol-related diseases – as well as an increase in mortality rates from chronic conditions, such as obesity, hypertension and renal failure. The authors also highlighted that factors outside of the traditional healthcare environment, such as poverty, income inequality, unstable employment, psychological distress and divergent state policy choices, may play a role.

While this is not an entirely new area of focus, I’ll be looking for the progress being made by the HIMSS community specifically on how the digital health transformation can address social determinants and help overcome the burden of social and environmental factors on health outcomes. I look forward to seeing how the community is embracing the important role social determinants play and how they incorporate those into their tools and approaches.

5. What’s next for sharing and linking datasets?

Digital technology provides a compelling opportunity for sharing electronic health record information across health systems and with patients and providers, researchers and innovators. However, as the volume of data and data sources proliferate, sharing, linking and curating diverse datasets becomes more challenging. With the rise of artificial intelligence (AI) and machine learning (ML), natural language processing (NPL), and other predictive analytics, digital data sharing is also becoming more complex. There is much hype about AI and ML, but the transition toward adoption into mainstream medical practice will take time, as there is still a lack of understanding of these technologies and we are only in the beginning of true convergence between data science and human science.

While the research community would like more open sharing of and access to data sources, there are still many barriers that prevent this – despite a decade of focus on this issue. The current controversy around the CMS Proposed Rule on interoperability is a case in point. The tensions around interoperability are based on real concerns about whether health systems can curate and consolidate quality information not only from EHRs, but also the myriad of patient apps and platforms, and how clinically meaningful information can be generated for clinicians to improve their patient treatment decisions. Interoperability and seamless access to health information are the way of the future. It will take some time to develop robust, evidence-based industry standards and generate consensus among key stakeholders, recognizing that they all have different needs. But we have been at this for a while, and I’m hoping to hear more consensus and commitment from stakeholders and the HIMSS community.

See you at HIMSS20.