I’m in favor of working out model solutions for use of resources available for the purpose of supporting healthcare initiatives because this allows one to evaluate by comparison those proposed for governmental policy or those available currently from private companies.  If one can develop model technology designs, and policies for deploying or implementing them, and you have the metrics to establish the value they could deliver, then you can compare these to others to find the roadblocks in government or the inertia in private business that keep optimal solutions from happening.  Private business always experiences inertia because of the weight of their prior investments in particular product and service design and delivery strategies.  There is also a considerable amount of ego investment on their part of the managers of both private and public organizations, so no matter how good your model product or service is, it will receive no support or resources from existing organizations if it’s mere existence is too critical of their accomplishments. 

The strategy that will likely succeed therefore is an iterative one implementing AI to enhance existing intelligence producing and decision making workflows, keeping those in charge responsible through transparency and accountability of the technology.  This is in fact how business and governments are proceeding, with proper caution, and making reasonable efforts to develop and maintain ethical standards.  The big new innovation is likely not to occur in existing businesses, nor in bureaucratic government agencies, but rather in a funded startup that forward thinking investors place faith in.  This fails far more often than it succeeds, but I think this is where to look for real bake-from-scratch model based innovation which will drive change through public education respecting what is possible and, thereby, through competition with existing means of health value delivery.

Written and posted October 1, 2025:

I was unaware of the existence of health data warehouses and the development of big data sources supporting healthcare and health policy designs when I started my Population Health course at CSU Global in the M.S. in Information Technology Project Management degree in August of 2025.  I was aware of the emerging deployment of artificial intelligence components and platforms through coursework I did at American Public University in my last term there during the first half of 2024 (B.S. in Health Sciences), and have since been working with various LLM based chat services for research and educating myself through various online means regarding the history of AI and the current implementations of it in ordinary business operations, IT security, and forensics generally.  The application of AI to big data sources is a new development in healthcare and elsewhere, although Palantir’s projects privatizing the Federal Government’s “total situational awareness” initiative in response to the 9/11 attack has been in development for two decades now.  Being a national security initiative, publication concerning it has been severely limited, making it difficult for researchers and the public to scrutinize its capabilities or the uses to which it is put.  The potential of such a diverse and integrated warehouse of information on individuals and groups, including health relevant data, combined with the potential of AI to analyze behavior patterns, creates potential for social good in health policy design on the order of a “God’s eye view.”  So far, I have not seen any productive application of Palantir’s capabilities to this problem, although there are state level initiatives which integrate health data into warehouses for study and policy design which are somewhat effective.  

This is a serious under utilization of the potential of AI applied to health data warehouses, implying reluctance on the part of those in possession and control of them to apply their capabilities to producing analyses which are outside of current norms or organizational structure justifications.  Given the political grid lock the U.S. has been experiencing for the past couple of decades, it is likely more productive to look to developments in other countries for examples of productive and ideologically unhindered implementations.  If Congress passed a law requiring that only the most reliable and powerful information technology reasonably available be used for policy related analysis, expressing standards for use in its acquisition, deployment, and field evaluation, then progress would be mandated.  To my knowledge, no such requirement has been proposed, leaving the necessary competence of private industry in the thought leadership role.  Other than through the usual and somewhat structured market forces, this category of authority remains unaccountable except as expressly regulated by law.  The result is under utilization of powerful new technology by organizations which can be held to the ethical standards necessary for healthcare and health promotion in populations.  

This is one problem that this organization attempts to solve through express adoption of IEEE’s Code of Software Ethics for Software Engineers, published at https://www.computer.org/education/code-of-ethics.  With strict adherence to these ethical standards, we can bring the power of the health data warehouse to the customer to support them in their health journey.

My name is Keith Watson an I'm the administrator of this website / bulletin board.

I am currently located / residing in the Brownsville, TX area.
I invite locals to chat and perhaps meet to discuss.

This could be adopted with a discussion of how profits above security and profits and power overriding respect for the user, as general operating principles, have corrupted wholesale the profession and business of information technology development.

Official adoption of an ethics code written decades ago while the industry can make no such claim of ethical standards makes the organization legally accountable and works to inspire user, developer, contributor, ... confidence.

The one that I am adopting as the default prior to any discussion of the issue is that published at this URL:
https://www.computer.org/education/code-of-ethics

It is the IEEE Computer Society's official Code of Ethics.
 
Here's the preamble:

"The short version of the code summarizes aspirations at a high level of the abstraction; the clauses that are included in the full version give examples and details of how these aspirations change the way we act as software engineering professionals. Without the aspirations, the details can become legalistic and tedious; without the details, the aspirations can become high-sounding but empty; together, the aspirations and the details form a cohesive code.

"Software engineers shall commit themselves to making the analysis, specification, design, development, testing and maintenance of software a beneficial and respected profession. In accordance with their commitment to the health, safety and welfare of the public, software engineers shall adhere to the following Eight Principles:

Quote:1. PUBLIC – Software engineers shall act consistently with the public interest.
2. CLIENT AND EMPLOYER – Software engineers shall act in a manner that is in the best interests of their client and employer consistent with the public interest.
3. PRODUCT – Software engineers shall ensure that their products and related modifications meet the highest professional standards possible.
4. JUDGMENT – Software engineers shall maintain integrity and independence in their professional judgment.
5. MANAGEMENT – Software engineering managers and leaders shall subscribe to and promote an ethical approach to the management of software development and maintenance.
6. PROFESSION – Software engineers shall advance the integrity and reputation of the profession consistent with the public interest.
7. COLLEAGUES – Software engineers shall be fair to and supportive of their colleagues.
8. SELF – Software engineers shall participate in lifelong learning regarding the practice of their profession and shall promote an ethical approach to the practice of the profession."

The effect of artificial intelligence (AI) in healthcare, in particular with respect to determinants of health or illness, is primarily one of greater depth and certainty in the analysis of population data.  The ethical issues inherent in aggregation and use of massive health data warehouses consist primarily of the risks involved in the potentially expedient means to achieve the value promised through application of AI, along with the usual barely legal methods employed in commercial exploitation of such.  Almost any behavioral or social determinant of health can be studied using artificial intelligence provided sufficient data with relevant content is available.  This includes the value of health data streams directly from sensors worn or implanted in the patient themselves, the advantage of AI being its ability to learn from the vast quantities of detailed data which are thereby made available.  For the purposes of this paper, the focus will be on the utility of health data warehouses for studying determinants of health in populations. 

Population studies of the behavioral determinants of health, such as studies of nutritional and exercise habits and practices along with recreational drug use and the ethically distinct category of individual liberty in drug use, that promoting health, can all be potentially studied through application of deep learning AI to health data warehouses.  Social determinants of health are intertwined with individual behavioral ones primarily to the extent that they influence individual behavior, although the built environment’s contributions to health outcomes cannot be ignored.  Obvious examples include the presence of lead in water plumbing and the prevalence of car exhaust caused smog prior to the legal requirement of catalytic converters on all gasoline combustion engines.  One less obvious example currently under study is that of the presence of microplastics in the environment causing health effects through their tendency to enter through ingestion.  Determinants of health which can be studied with AI applied to data warehouses are the focus of this paper, and ultimately this includes almost all of them.  The primary drivers of ill health therefore should be selected, such including exercise, nutrition, drug use, and cultural membership or beliefs affecting selection or rejection of health protecting or promoting behaviors generally. 

The Health Belief Model provides a framework for study of both cultural influences and beliefs respecting adoption of health promoting behaviors and cessation of unhealthy ones.  It’s basic premise is that the primary motivators of behavior change are beliefs respecting the risks inherent in particular behaviors and those respecting the benefits achievable with adoption of others (Nash et al., 2021).  The determinant in this case, influencing all others, is belief. 

One type of analysis done by social media platforms that addresses beliefs is called sentiment analysis.  According to Wikipedia (Sentiment Analysis), this “is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.”  With the deepening power of AI applied to advertising on such platforms, particular product advertisements could be shown to particular users with a high likelihood of believing that that product would be good to acquire.  Applied to health behavior modification, particular messages could just as easily be targeted to those most susceptible to receiving and acting on them based on the beliefs that can be inferred form the information available on them.  AI applied to health data warehouses in combination with content of social networking sites would be particularly effective in custom tailoring messages that motivate healthy behaviors or inhibit unhealthy ones. Social media platforms can provide escape from physical cultural membership constraints and an avenue to new social experiences, and there is no reason to waste the beneficial influence these new social interactions can have to reinforce beliefs that support healthy behaviors. 

Positive messaging respecting exercise can come from authoritative sources such as the American College of Sports Medicine, which helped craft the exercise promoting health messaging of the United Stated government which started in the early 1990’s (ACSM, 2025).  The dramatic decline in the incidence of chronic diseases caused by sedentary lifestyle induced thrombosis, or abnormal blood coagulation, occurring during the period since then can be no coincidence, as such authority supported the growth of the inexpensive popular gym industry exemplified by Planet Fitness. 

Health equity is frequently frustrated, however, by nutritional behaviors, those of the lower income demographics showing an increasing trend toward lack of attention and discipline in this behavioral determinant of health (Ong et al., 2024).  The prevalence of high energy, low nutritional value foods and the busy, active work life of the low income family create a reliance on convenience food at supermarkets and fast food restaurants.  The recent enactment of requirements for nutritional labeling of fast foot items is a form of messaging which simply makes selections more competent, providing information to the consumer and incentive to the producer to improve the nutritional value of their products.  Determining the effectiveness of such information availability can be a subject of study using AI applied to health data warehouses provided sales information, including historic, can be obtained from fast food restaurant chains.  Dining behaviors can also be inferred from social media content, so with sufficient breadth of sources AI can help here also.  This one example demonstrates the power of providing information needed for competent decision making, which is also the primary value to populations of the availability of inexpensive digital technology, something no software developer could miss.

Another impediment to health equity is the quality and types of recreational drugs available to lower income individuals and communities combined with their general lack of education respecting their short and long term health effects.  Of the domains related to social determinants of health, the two most relevant to this determinant are education access and quality and social and community context.  Both of these can be studied using AI applied to the current health data warehouses, which generally include social networking derived information also, in particular if one accounts for the popular authority of entertainers who are perceived as free from the usual social constraints which limit opportunities to lower socioeconomic strata generally.  The social context provides readily available escapes and other solutions to social stratification with education being the hard road that few are willing to travel for the duration required.  Messages reinforcing positive educational behaviors can be designed and tailored using application of AI to the vast quantities of data already available, and with the emerging ability to re-identify social networking sources, can be targeted for effect.  The potential for re-identification can be seen as entirely ethically negative except for the rare exception which this application would be an example of, which of course must be carefully regulated or government controlled to have a constructive effect.  One need only study the aggressive frauds of the cigarette industry as their campaign to preserve their marketing efforts was steadily shut down through regulation to understand the potential for evil inherent in such targeting.

The determinants of health which AI can be effective with respect to are numerous and diverse since it can be used to study any and produce competent analysis usable for effective policy designs as well as commercial IT product design.  The ethics of application of AI to achieve the benefits of knowing what will motivate people to actually change their behavior, for instance, is a complex question fought with risks of various kinds.  To what extent can we use AI to re-identify a subject for messaging or other interventions and what level of risk to the individual or others would justify it?  Can we ignore the power of AI in identification of individuals at risk or a danger to others and therefore fail to implement the AI needed for re-identification?  The ethics of studying exercise and nutrition attitudes and practices of particular demographics, among other valuable application, cannot be denied, and such study is ethically sound, although it does require a risky levels of health and social networking data integration into vast, high value warehouses which under currently law can be commercially exploited, sending potentially sensitive data to entities with unknown ethical standards.  The risk needs mitigation with governance standards and competent regulation, making possible reasonably safe realization of the potential of AI to analyze and reveal the details of various population determinants of health.

References:

McCarron, T.L., Noseworthy, T., Moffat, K., Wilkinson, G., Zelinsky, S., W, D., Hassay, D.,
Lorenzetti, D. L., & Marlett, N.J. (2019), Understanding the motivations of patients:
A co‐designed project to understand the factors behind patient engagement, 
Health Expectations,  August, 2019.

Nash, D.B., Skoufalos, A., Fabius, R.J., Oglesby, W.H. (2021), Population Health, Creating
a Culture of Wellness, Jones and Bartlett Learning, LLC.

Ong, J. C. L., Seng, B. J. J., Law, J. Z. F., Low, L. L., Kwa, A. L. H., Giacomini, K. M., &
Ting, D. S. W. (2024). Artificial intelligence, ChatGPT, and other large language
models for social determinants of health: Current state and future directions.
Cell Reports. Medicine, 5(1), Article 101356. https://doi.org/10.1016/j.xcrm.2023.101356.

Thompson, W. (senior editor) (2010, 2025). ACSM’w Guidelines for Exercise Testing and
Prescription, 8th and 12th editions. Walters Kluwer, 2010 and 2025 respectively.

Privacy online has many supporters:

18 Privacy Focused Organizations You Should Know About:
https://identityreview.com/18-privacy-or...ould-know/

"Nowadays, with the Internet, any piece of knowledge can be accessed with minimal effort. What many fail to understand is the true cost of such capacity. Oftentimes, big tech firms such as Google and Facebook store user data and share them to other companies, in which they use to personalize ads. Many users are not aware of this considerable breach in user privacy."

2025-09-30:
Some of these shouldn't be trusted, including WhatsApp and Facebook Messenger, since they're owned and run by Meta/Facebook, whose business model requires them to collect as much information about you as possible. Read the article carefully to find the express guarantees of others. They exist.

There is a valid case that we the people are negligent about our health and that many of our short and especially long term illnesses could be prevented if we were. Since we are not required by law to be doing what we can to maintain and to improve our health to the extent reasonably possible to avoid both chronic and shorter term illnesses, however, the governments have failed to enact reasonable requirements to protect us from the pain and expense resulting. Simple exercise and nutrition requirements which are verifiable should be used to help determine whether or not the illness subject to insurance is actually caused by something outside of the reasonable control of the patient. Only in this case can the coverage actually fall under the general understanding of “Insurance,” which is meant only to compensate the injured for injury which is not their own fault. Punishing people arbitrarily for this collective negligence by cutting off necessary medical care in a way which creates profits, however, is not just, and those merely gaming the current legal system and medical establishment to maximize their gain through aggressive and abusive business practices are only exploiting the lack of enforcement of reasonable standards on the insured.
Those who accept their responsibility for their own health are already using digital technology to advance it and to prove both their efforts and their results.  The government's negligence creates opportunities for digital technology developers to support them, and such is a very socially responsible enterprise.  The main question, then, is how much value can we deliver to achieve the goals of developing training competence in the user and generally supporting their health building journey.

For the free version, explain to the user what the usual rules for handling their data are and how we can make money selling, leasing, or or otherwise engaging in business using it.

In the first level of paid, we give them dashboard level visibility of what we collect and time stamped event logs showing all transmission of any part of it.

In the next level of paid licensing, all transmission is off by default and they get the dashboard to view and analyze everything collected and stored on their device, along with notices of request for use of any part. At this level, they own and control all of the collected data pertaining to them.

The user pays for protection.