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Reflections on Combining Digital Maps and Health IT

Health Care Administration and Underserved Populations:

Modern healthcare and computer programming can work together to create meaningful and systemic changes to population health and health care efficacy in America and around the world. One way to help bring about more accessible changes to the medical industry is to quantify costs and track real-time changes to medical related information. Creating an efficient health-care system means keeping track of medical supplies, prescribing trends among certain population groups, understanding where gaps exist in medical specialties (dental, neurological specializations, cardiovascular, pediatricians, etc.). In order for health IT to create long-lasting continuities in the medical industry our data source must be easily coalesced. Currently, health IT data comes from local, state, national, and private sources that have varying methods of data reporting which make it more difficult to combine how micro-level and macro-level variables interact with one another.

For instance, if we want to analyze how neurological disorders and pharmaceutical prescribing rates interact, it is difficult to discern how associated classes of therapeutics (anti-depressants, neuroleptics, analgesics, OTC therapeutics, etc.) tie into neurological-related disorders/diseases rates in a particular region. In order to more thoroughly understand how diseases rates and prescribing trends relate to one another in a geographic density, it would be ideal to know quantitatively whether certain patterns (among different genders, ages, genetic backgrounds, etc.) and the amount medical supplies needed (time visited doctor, dosage of medicine, proper administration) exist to satisfy each individual in that region. Possibly we could call this number something such as, X1, to estimate the amount of medical capital sufficient to serve a population density. However, this would be an ideal scenario to help better understand how many physicians/medical supplies are needed in an area to adequately serve emergency/routine medical complications.

This concept could be taken once further to denote something such as, X1 of Disorder A, or Estimated Cost of Medical Capital needed to treat Schizophrenia in a particular geographical area. If it is known that therapeutics A, B, and C can be used to treat different dimensions of Type A or Type B Schizophrenia, then we could better understand if relative prices of these therapeutics are efficient for a given geographical area well.

In general, this theoretical method of health information technology relies on up-to date data and geospatial analytics to pinpoint where inefficiencies are occurring. Inefficiencies can be caused by poor health administration (long wait times and short face-to-face interactions with physicians), unethical prescribing patterns (opioid addiction, anti-depressants misdiagnosis, etc.), health insurance gaps (poor HMO coverage, underserved populations for varying medical specializations), and consumer misinformation (going to emergency rooms for minor cases, not getting vaccinated, unhealthy diets).

Effectively all of the inefficiencies above can be theoretically remedied if the proper data sources are integrated on a weekly, monthly, or quarterly basis. However, it is foolish to think that the health care system can be systemically changed without leaving gaps for quality and quantity of care received, there will always be underserved areas of the population. The goal of the health IT landscape should be to provide data sources that can be more easily combined so that disease rates and pharmaceutical prescribing data can be better understood. If data from local, state, and national levels can be integrated then causal patterns about medical costs may begin to emerge.

Digital Mapping and Health IT:

Healthcare information can be better served in a digital map, platforms such as the Google Maps API, allow for a way to visualize data in a modern format. Dynamic maps can be created to make maps updated based on changes that occur on a daily, weekly, or even yearly basis. If the template of the map doesn’t need to change, then the data source (such as JSON, JS array, mySQL database, Excel file stored in Dropbox) can be updated as needed and the data can be pushed real-time to the map or data visualization tool.

Creating dynamic data visualization is a technique to focus more on data-driven stories rather than relying on time rebuilding source code. This is obvious to understand, but it’s worthwhile to contemplate on methods to more efficiently push real-time data into an analytics dashboard or enterprise-level system.

For instance, a platform that can take data integration one step further would be beneficial for assimilating health information from multiple sources. If pertinent data from JSON and mySQL could be combined into a data visualization then we would have the ability to combine datasets without having to normalize the data into the same format. On the contrary, in some instances mySQL could be exported to a JSON format to create a localized database.

Data integration depends on the case and use, however the overarching concept of putting two sources of data into a single data analysis remains beneficial for meaningful patterns to emerge. Once patterns emerge then consumer-to-physician cost inefficiencies can be further analyzed for solutions.

As a student of neuroscience, these are some thoughts on how digital maps and health IT can be combined to better serve population health needs. Possibly in the future some maps and graphics will be added to complement some of the themes described above about prescribing trends.


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