Time to become Virtually perfect

Some might believe that the COVID ’19 pandemic was the harbinger of a heightened digital health wave, while others might believe that the pandemic simply hastened the process of its evolution and adoption. I, for one, stand by the latter. The Digital Health market size was around US$ 195.1 billion in 2021, and is estimated to substantially grow to around US$ 780.05 billion by 2030¹. The spending on digital healthcare solutions is estimated to reach US$ 244 billion by 2025². Digital Health companies have been slowly simmering, brewing, adapting and growing, and have seized the market when the time was ripe.

When the pandemic necessitated the need for mitigation amidst disruption and chaos, Health Technology companies were ready to offer mature plug and play solutions that made adoption seamless and imperative. Furthermore, several countries quickly recognized the need to alter privacy policies and data protection regulations to enable remote consultations and virtual health interventions³. This was propelled by the paucity of physical resources, and coupled with an alarming need for accessible, quality healthcare. But more importantly, there was a stark realization and label for a new type of care delivery that need not be in-person- virtually, virtual.

Objectively, virtual care could be segmented into care that makes you get better, and care that makes you stay better…alternatively, curative and preventive. While the former milked patient care during the need of the hour, the latter emerged a new, unsung hero; An unexploited solution to a global, age-old opportunity. Center for Medicare/Medicaid Services’ (CMS) intent to incentivize increased and improved care management could/can take swift flight upon the wings of software platforms like that of HealthViewX. Solutions like Remote Physiological Monitoring (RPM), Transitional Care Management (TCM), Chronic Care Management (CCM), among others, help care teams monitor, manage, and engage patients right from their homes. This in turn has shown to reduce costs and readmissions, mitigate risks, improve outcomes and increase reimbursement⁴. A win-win-win!?

But, hold on! While all this sounds rosy and convenient, I have wondered whether there has/had been resistance in adoption amongst clinicians and patients…the end-users, ultimately. I stumbled upon an informative adapted strategy matrix in an article by Ande De. In a matrix outlining the degree of change behavior needed from clinicians, versus the degree of patients’ resistance to adopting new technology, TeleHealth, RPM and COVID screening, response and monitoring, emerged the most victorious with the least resistance from both stakeholders⁴. While cloud based web portals and health applications that record patient data were met with some resistance, it was a pleasant surprise to note that there were no digital health ‘failures,’ that were met with high resistance⁴. The data also shows that Artificial Intelligence (AI), Prescriptive and Predictive Analytics are here for the ‘long haul,’ being met with high resistance amongst clinicians and low resistance amongst patients⁴…all predictable, yet surprising at the same time!

While there could be several intuitive, understandable reasons for resistance, I’m compelled to boil it down to,

1. Change Management:

   Willingness to embrace change and make the time to familiarize with change. Technological evolution brings up several unknowns, mostly in terms of whom to involve, when and how. While internally developed digital health infrastructure might make these unknowns less gloomy, it is unlikely that health systems have the time, resources and bandwidth to constantly troubleshoot and upgrade. While this drawback is moot with third-party digital health vendors, challenges arise with seamless interoperability, integration and complete customization to the needs of the organization.
   Encouragingly, a growing number of companies like HealthViewX are attempting to address these issues at the grassroots level. The platform entails seamless integration with a home grown interoperability engine, and the ability to completely customize the platform.

2. Liabilities:

   Fear of and risks associated with the unknown. Several clinicians may not be sufficiently trained in using digital tools, alongside issues with seamless integrations… resulting in potential medical malpractices and associated legal claims. There are several open-ended concerns- are these malpractice claims attributed to the clinician, to the technology, or to those responsible for training⁵? Is there a clear, established, legal norm/protocol for how care via digital tools needs to be rendered and documented⁵? Most importantly, is confidential patient data safe and secure?
   In a survey conducted amongst 242 clinicians in Pakistan, 69% ‘agreed’ or ‘strongly agreed’ with the sentiment that there is a lack of regulation to avoid medical malpractice. Only 29% believed that their medical indemnity would cover telehealth consultations⁵. Another study discovered that clinicians were less confident about prescribing controlled medications via TeleHealth.
   On the other side of the coin, studies have shown that several malpractices, misdiagnosis or errors could have been avoided with the intervention of AI and digital health. This is with the help of real-time alerts, diagnostic decision support, tracking, reporting, etc. Increasingly, laws have been restructured to exonerate AI/digital health in the face of mishaps, under several circumstances.

3. Proofs:

   A natural barrier to adoption in general is a lack of evidence based outcomes. The advent of Digital Health solutions might not be mature enough to present a historic laundry list of troubleshooting and adaptability to the constantly evolving needs of users. However, the more external digital health solutions are adopted by health entities, the more their counterparts have a track record to witness and to pine for.
   A valuable metric rests in the achievement of the Quadruple Aim, ie, focusing on Population Health, enhancing the experiences of end-users, and of care providers/clinical staff, and reducing the per-capita cost of health care⁶. There are several intangible outcomes such as, provider burnout, time saved, patient outcomes, and patient satisfaction⁶. Externally developed tools also often provide case studies or scientific evidence displaying them meaningful outcomes.

4. Access:

   While digital health has redefined care with a click of a button, socio-demographic barriers to access could result in health disparities and a digital divide. This could be segregated into a technological barrier (such as, lack of smart devices and internet connection, the prevalence of digital health in their region/community) and, a digital literacy barrier involving the ease of use of technology depending on age, literacy, income and tech-savvyness, etc.
   While the digital divide can be narrowed by subsidizing the inherent cost of access, and perhaps by installing public access kiosks, ultimately, the utopian vision should be to extend beyond digital literacy to digital mastery and autonomy⁷.

My presumptuous, yet sagacious retort to these four points is, time.

Time to be moved. Time to take the plunge. Time to embrace. Time to get and assess outcomes. Time to advance. Time to revolutionize.

Time to become Virtually perfect.

References:

1. “Digital Health Market Size Will Attain USD 780.05 Billion by 2030 Growing at 16.1% CAGR – Exclusive Report by Facts & Factors,” February 2023, Facts and Factors, https://www.globenewswire.com/en/news-release/2023/02/01/2599148/0/en/Digital-Health-Market-Size-Will-Attain-USD-780-05-Billion-by- 2030-Growing-at-16-1-CAGR-Exclusive-Report-by-Facts-Factors.html
2. “The Use of Digital Healthcare Platforms During the COVID-19 Pandemic: the Consumer Perspective,” Alharbi. F, March 2021, PMC, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8116074/
3. “Digital health and care in pandemic times: impact of COVID-19,” Peek. N, Susan. M, Scott. P, 2020, BMJ Journals, https://informatics.bmj.com/content/27/1/e100166
4. Degree of adoption diagram, “Five ways Digital Health Innovation will grow + evolve post pandemic,” Ande De, April 2020, Alteryx, https://www.alteryx.com/input/blog/5-ways-digital-health-innovation-will-grow-evolve-post-pandemic
5. “Digital health technology-specific risks for medical malpractice liability” S. Rowland, E. Fitzgerald, et al, October 2022, https://www.nature.com/articles/s41746-022-00698-3
6. “Assessing the impact of digital transformation of health services,” EXPERT PANEL ON EFFECTIVE WAYS OF INVESTING IN HEALTH , Barros, P et al, November 2018, https://health.ec.europa.eu/system/files/2019-11/022_digitaltransformation_en_0.pdf
7. “The Digital Determinants Of Health: How To Narrow The Gap,” K. VIgilante, Feb 2023, https://www.forbes.com/sites/forbestechcouncil/2023/02/02/the-digital-determinants-of-health-how-to-narrow-the-gap/?sh=384def8c59ba