How significant are Apple's medical apps to its future as a technology company?

Wearable devices (WD), mobile apps, other electronic systems, all these are new health monitoring tools, falling under the umbrella term mHealth (mobile health). They create new types of patient data called Patient Generated Health Data (PGHD). According to Shapiro et al (1),

'PGHD are health-related data, created, recorded, gathered, or inferred by or from patients or their designees (i.e., care partners or those who assist them) to help address a health concern'

According to Michael Fardis (2), PGHD include health and Rx history, symptoms, biometric data, lifestyle choices and other information. Main difference from traditional health data is patients record PGHD and are responsible for sharing it with those responsible for their care. According to Chan et al (3), over the coming years, mHealth in general and WD in particular could become indispensable in improving chronic disease monitoring. Systems monitored would be cardiovascular, nervous and respiratory, and chronic diseases covered would include cardiovascular diseases, Chronic obstructive pulmonary disease, diabetes, epilepsy, Parkinson's, autoimmune diseases such as Multiple sclerosis (MS), Rheumatoid arthritis (RA), to mention just a few obvious ones (for more possibilities, see table below from 2).

Thus, mHealth could fundamentally change chronic disease management and indeed biomedical research itself. Multiple sclerosis is a case in point. As Simpson et al state (4),

' Social media such as Twitter, mobile phone-based applications (apps), or even more pedestrian technology such as web based surveys or email, may provide a mechanism to more reliably track relapses. One could imagine an app that quickly queries relapse symptoms each day, and which automatically alerts investigators to follow up when a participant reports any level of symptoms indicative of relapse. Alternatively, a more participant-driven method like tweeting or emailing investigators about potential relapse symptoms could be utilised, avoiding the need to keep a study nurse on hand at particular hours to receive calls, and being sufficiently quick and simple to do that participants might be more likely to make that contact than if they were immediately having to do a phone assessment with a study nurse'

At its core, mHealth offers the following possibilities,

• That more frequent health monitoring could trigger behavior change.
• That it could help in better managing chronic diseases.
• That it could help improve medical research.

However, thus far the way mHealth has evolved creates a binary that mirrors the mutually antagonistic forces that currently drive it, namely, commercial market value of customer-generated health data versus improving chronic disease management and biomedical research by helping develop Precision medicine. Thus, mHealth essentially divides into two non-overlapping features,

1. Everyday health monitoring for the healthy.
2. Precision medicine for the chronically ill and for medical research.

Reason these are non-overlapping goals is because they target different population segments and serve different purposes. This difference in kind is a conundrum because the medical value is unclear for #1 while being obvious for #2. Everyday Health Monitoring ≠ Precision Medicine (see figure below). 

1. Healthy people frequently tracking some easily quantifiable health measurements (heart rate, pulse, blood pressure, sleep patterns, step tracking, posture, even pelvic floor health) raises the obvious issue of relevance. While its immediate medical value is ill-defined, it offers immediate commercial value for mHealth companies. Just sell WDs or apps to customers. Even if a customer doesn't share their data with their doctor or other healthcare advisers, there's always the possibility the device/app maker would sell it. Such user-generated health data is of obvious commercial value to health insurance companies, employers, affiliates, etc. Data may not be shared when the customer signs up for the service but there's no guarantee it will stay that way. For e.g., a 2015 Buzzfeed report by Ann Helen Petersen stated that Moves 'quietly modified its privacy policy to allow for sharing of data with “affiliates” after being acquired by Facebook in April 2014' (5). Issue of privacy is also an ever-present concern. Data may be anonymized when the customer signs up for the service but there's no guarantee it will stay that way.
2. OTOH, applied to chronic diseases and medical research, mHealth could help improve patient segmentation, symptom tracking and assessment, and thereby help develop more precise medicine, i.e., better fulfill the promise of precision medicine in short. Its medical value is thus clear and immediate but commercial value is long-, not short-term. This is because value-added input of mHealth in existing health care management and medical research can only be discerned over time through trial and error.

Apple's 2-pronged approach, Healthkit targeting customers, and Researchkit targeting medical research, epitomizes this essential conundrum in mHealth technologies. Conundrum as in commercial value of Healthkit data generated by healthy users versus Researchkit helping develop precision medicine in chronic disease and biomedical research. Clearly a difference in kind.

How to de-link the profit motive from collecting and selling health data generated from WDs and other health-monitoring apps from its potential to help develop precision medicine? If past is any predictor of the future, this won't happen without a fight followed by government stepping in to regulate this space. As for Apple, its future and future legal wrangles in mHealth will probably be shaped by which of these two mutually exclusive paths, Healthkit versus Researchkit, it decides to prioritize.

Bibliography

1. Patient-Generated Health Data. White Paper. Michael Shapiro, Douglas Johnston, Jonathan Wald, Donald Mon. April 2012. https://www.healthit.gov/sites/d...

2. Investigation of Models for the integration of Patient Generated Health Data within Swedish Multiple Sclerosis Quality Register. Michael Fardis, Master's Thesis. 2015. http://ki.se/sites/default/files...

3. Chan, Marie, et al. "Smart wearable systems: Current status and future challenges." Artificial intelligence in medicine 56.3 (2012): 137-156. https://www.researchgate.net/pro...

4. Simpson, Steve, Bruce V. Taylor, and Ingrid Van der Mei. "The role of epidemiology in MS research: Past successes, current challenges and future potential." Multiple Sclerosis Journal 21.8 (2015): 969-977. Past successes, current challenges and future potential

5. Big Mother Is watching You. Anne Helen Petersen, BuzzFeed News, Jan 1, 2015. Big Mother Is Watching You: The Track-Everything Revolution Is Here Whether You Want It Or Not

https://www.quora.com/How-significant-are-Apples-medical-apps-to-its-future-as-a-technology-company/answer/Tirumalai-Kamala