A functioning healthcare infrastructure is one of the cornerstones of a civilized society, but there are some fundamental administrative problems with current and legacy systems that could be effectively managed through the use of time data.

Individuals

Problem

  • As individuals age or encounter increasing or more complex health issues, they may be faced with notable administrative challenges. These cover a wide range of topics, from scheduling of checkups, adherence to medication timings and access to a dated record of all vaccinations or other medical history. Where oversights are made, the costs to health, finances and convenience can be significant.

Solution

  • Individuals and healthcare providers could be incentivized through ANLOG rewards to opt in to the submission of medical records to the Timegraph. Analog’s mobile API could then be utilized to compile a user-friendly interface from which an individual could access a timeline of upcoming medical obligations. Notifications could also be sent to the user’s phone or linked email address. These factors should significantly reduce the probability of missed appointments or medications. Furthermore, it could provide a collated medical history that would save time in the future should the individual ever need to access or review their records.

Insurance

Problem

  • Health insurance premiums are determined by actuarial estimates of the health of an individual. These estimates are typically inclusive of an algorithmic assessment of demographic data such as age, gender, location and family size, but usually do not take into account a detailed medical history of the individual. Given the importance of medical history in determining health outlook, this creates inefficiencies for both the insurer and individuals.
  • For example, healthy people will pay higher premiums than they potentially should do, as their costs are based on an assessment of the ‘average’ of their demographic. If they are more healthy than this average, they are effectively penalized and thus subsidize the less healthy. Furthermore, insurance companies lose out by offering insurance to individuals whose medical history may suggest very high medical costs in the future and who would be statistically loss-making.

Solution

  • Over time, the dynamic described above should result in many individual’s medical histories being bound to the Timegraph. Should these individuals elect to opt-in, the Timegraph could therefore be utilized to offer personalized health insurance premiums that take into account specific histories as opposed to generic demographic data. This offers immediate benefit to both the individual and insurance company on a case by case basis, and over the longer term would result in more accurate actuarial assumptions being employed by insurers.
  • It should be noted that the legalities of insurers requesting or accessing medical data differs notably by jurisdiction, and the feasibility of the above solution would be dependant both on location and sufficient individuals opting in to the scheme.

Research

Problem

  • When medical researchers study the onset and progression of disease, they may make use of data correlations and other associations to draw conclusions into prognoses and risk / mitigating factors. It is common for academic access to medical data to be incomplete or limited. For example, researchers may be able to gain access to medical records on a specific disease, but may lack either timing information on this data or other potentially pertinent inputs such as time-bound lifestyle factors of the individuals whose data is being accessed. This places limitations on the scope of the statistical analysis available on accessed data sets, and therefore breadth of conclusions or associations to research further.

Solution

  • As more individuals submit their medical history to the Timegraph, they may also be incentivized to submit lifestyle data such as sleep patterns, dietary intake, nutritional supplementation, frequency of social interactions, etc. The searchable nature of the Timegraph thus allows health researchers to compile medical databases with a more complete set of variables. This in turn facilitates more comprehensive regression analyses which may identify variables previously not considered or thought to be relevant. This has the likely outcome of deepening our understanding of both how certain diseases are interrelated and the risk and mitigating factors that may exist in our lifestyles.

Corporate

Problem

  • Legal disputes surrounding negative healthcare outcomes are relatively common. Assessments of culpability and liability need to take into account a holistic view of whether a medical protocol was sufficient and whether this protocol was followed correctly. In other words, an overview of who did what and when. Currently, the recording of certain actions may be incomplete or there may be disputes over which parties performed which action at what time.
  • Medical care often requires interconnected scheduling between disparate entities. For example, a certain test result may require follow-up from one or more specialists at other healthcare providers. If these follow-ups need to be booked manually, this could result in scheduling conflicts. Typically, healthcare companies would not be in sync regarding availability of appointments.

Solution

  • Given the prevalence of legal disputes and the financial ramifications of lawsuits, it is probable that healthcare providers could be incentivized to submit time-stamped data of all steps taken in a given medical procedure. This data could then be organized on-chain to serve as an objective record of who did what and when, thus providing reliable evidence of whether medical protocols were adequately followed in the event of a dispute. This would both reduce legal claims and ensure a just outcome where claims do arise.
  • Third-party and unconnected healthcare providers could be linked through Analog’s API to create a synchronized scheduling system. For example, if a diagnostic provider performed a test that necessitated a follow-up with a cardiologist, this test could be automatically submitted to the Timegraph. Once confirmed on-chain, the client could then view on the API a listing of all nearby cardiologists with availability, along with the functionality to book an appointment immediately.

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