Future of health records

Rethinking electronic health records (EHR’s) by aggregating provider data silos into one system owned by the patient.

In the United States, most if not all, people in their lifetime visit doctors and seek medical care. And throughout their healthcare journey, we generate a large volume of data that is used to diagnose, monitor, and treat our illnesses. This data is generated at every stage of a patient’s journey through a variety of means. Below is an outline of a patient’s typical medical journey, and the various data types collected throughout each touchpoint.

Patient Medical Journey

1. Request Appointment

A patient requests an appointment with a provider. Examples of data collected here are full name, contact information, health insurance details

2. Pre-Visit

If it’s a first-time visit, paper-work is generated. If it’s a return the paper-work is reconfirmed. Examples of data collected here are medical history, food diary, symptom tracker, legal medical forms

3. Intake & Visit

Here you sync with a nurse to intake you before your appointment, then you meet with your provider.

Examples of data collected here: personal identification card, base vitals, nurse soap notes, doctors write up, on-site testing data, off-site testing orders, prescriptions

4. Post-Visit

Typically the medical loop starts over here unless you’re required to visit an off-site testing facility, before checking back in with your doctor. This is also the part where a new stakeholder is introduced, the pharmacy, where you receive your medication.

Some examples of data collected here are laboratory specimen testing, radiology, and pharmacy. All of this data is extremely complex, and nuanced on a patient by patient basis, but remains with the individual providers as fragments and the patient doesn’t own or control their data.

Framing the problem

As mentioned above, in the current healthcare system the patient’s data lives in provider silos and doesn’t evolve rapidly enough with new data resulting in outdated fragments, stakeholder communication gaps, and the possibility of misdiagnosis. These inefficiencies are expensive in terms of time and money which prevents providers from functioning effectively. An interesting insight to that point, a recent white paper published by John Hopkins reports that 86% of medical errors, and misdiagnosis, are a result of administrative errors; which lead to it being the number 3 reason for medically related fatalities in America.

Data silos are not the only gaps in the system that prevents a holistic healthcare model. More recently, amid a global pandemic (COVID-19), the US, struggles to grapple with delivering effective medical care to the masses. The pandemic has shined a light on the systemic problems that have troubled our medical system necessitating a need for an overhaul and a focus on holistic care. As a person living with a chronic illness, I struggle daily to navigate such outdated processes and infrastructure.

Due to the complexity and breadth of our healthcare system, for this case study, I have descoped the end-to-end patient journey by narrowing into one component, patient data, more specifically, Electronic Health Records (EHR’s). According to the Centers for Medicare & Medicaid Services, define EHR’s as an electronic version of a patient’s medical history, typically created and maintained by a medical provider, that is used to archive a patient’s state at the time of visit, or to facilitate as an artifact for diagnostic reasons. The below aims to articulate the flow of the scenario the typically results in the creation of an EHR.

Defining the vision & goal

According to the New England Journal of Medical Catalyst Group, the current model of healthcare deliverance is a fee-for-service model, where providers are paid on the number of healthcare services they deliver, whereas, in a value-based healthcare model, providers get paid depending on “patient health outcomes.” To have a system that focuses on delivering quality healthcare over quantity, we must shift our healthcare model to a value-based healthcare system the rewards the stakeholders across the board, and encourages collaboration.

Because currently, patient data exchange between various providers is not seamless; the data is not updated in real-time and each provider only receives parts of the data rather than a comprehensive updated view of the patient’s medical history. Defragmenting provider data silos and creating a centralized data system that the patient owns may help increase patient engagement, and could create a system that is conducive to providing value-based care. Apart from serving as a more informed care model, a centralized data system could also benefit all stakeholders by helping to reduce costs in the long term. For instance, it might help lower a patient’s medical cost by reducing the need for their provider to order redundant tests, which in turn would also save their insurance company money. You could also hypothesize that having glanceable succinct data may save the doctors time trying to synthesize the data.

Existing challenges to adopting a new data aggregation system

The age of the systems, the difference in processes, and compliance around privacy are some of the challenges to implementing a solution that seeks to resolve the problems mentioned in the above section.

1. High volume and unpredictability of medical data (System level)

Modeling healthcare data is primarily challenging due to its sheer volume, however, the constant variations in the new data make it even harder to build models that are intelligent. We need models that can evolve quickly based on new incoming data while staying context-aware of the data prior.

2. Nuance of information

While big data enables us to model trends from a large population, the data might not always be a fair representation of the entire population under consideration leading to issues of bias and data discrimination. Using small data techniques could help mitigate some of these problems and help provide contextual information to data helping us get deeper insights into health problems, at both a personal and societal level.

3. Data Fragmentation

There are multiple stakeholders in an individual’s healthcare journey such as, the patient, doctor, provider, laboratory, pharmacy, insurance, and each of these stakeholders has their own systems that don’t necessarily communicate amongst each other leading poorly designed seams. As a result, the flow of data between these systems is not smooth and efficient.

4. Regulation, Privacy, and Systems Integration

One of the big blockers for innovation in the healthcare industry is regulation and cost at scale, this tends to stifle innovation and favor solutions that provide a return on investment without the risk of disruption. Disruption at scale is expensive and is volatile as it could have outcomes that are hard to forecast. Additionally, the solution needs to be secure because it contains people’s medical data which is highly sensitive information. Therefore, we need an approach and policy that not only roadmaps a safe transition plan to leverage new technologies that may be in their infancy but could be impactful.

Mapping the system & UX principles

I wanted to approach hypothesizing a concept in a thoughtful manner, given the sensitivity of medical data, and the complexity of the problem space. Thus, I synthesized my research into the following principles to guide my solution.

1. Human, succinct, and intelligent

The system should be human and should display contextual information that adapts to the viewer. The system should also enable the user to use queries as a means of interfacing and have the ability to learn, by the user being able to have a conversation with their data.

2. The EHR system should be robust

There is a huge influx of medical data and EHR systems need to constantly evolve with new data and should stay up to date to be able to provide meaningful insights even under unpredictable circumstances such as COVID-19. Being able to leverage both your medical history, the history of others like you, and then data at a large scale like research studies can create more dynamic and relevant insights.

3. Respect legacy systems

As with banking, health care has numerous pre-existing legacy systems that have been used for a long time. Building new systems that can leverage existing systems will go a long way in lowering the barrier to adopting new technologies, the cost of implementing, and the opportunity for larger impact.

The concept

The premise of my solution is a medical record system that aims to make patients the primary owner of their medical data, so as their data evolves, or they encounter different medical needs, their health data remains up to date, relevant, and follows them through their healthcare journey. The system also aims to provide the patient with a touchpoint for monitoring, understanding, and sharing their health records across different providers. This connected system, in which the patient chooses who to give access to their records, could help reduce administrative intake errors, alleviate the trouble of manually transferring records from provider to provider, as well as provide a preventative approach for monitoring health. By creating a patient-centric platform we aim to bridge the gap a patient has between their medical data, treatment plan, and wellness journey.

The design principles described in the above section provided a framework for me to build a system that could address:

1. Connecting with data

How can I connect with my data and how can I connect with all of my doctors under one roof?

This vignette shows how a user will connect to all their medical providers to sync their data into this system using OAuth.

Possible Outcome: Being able to have all of the data aggregated into one system provides the patient and their doctors the ability to look at the evolution and nuance of how the data evolves over time.

2. Drawing insights from the data

How can I interact with my data, more specifically how can I search and interpret my medical data for deep insights?

This vignette shows how the data is intelligent and query-able.

Possible outcomes: Giving patients an easy way to digest their medical data through the use of language that’s human could empower them to make informed choices regarding their health.

3. Sharing my data to my doctor (both digitally and in-person)

How might my data inform more holistic medical care and how might it be shared amongst providers?

This vignette shows how a patient can share their data with their provider if they have a question, both remotely and in-person.

Possible Outcome: The patient has the ability to quickly show doctors an overview of their medical history. This overview can provide information in an easily digestible form from which the doctors can provide a quick yet thorough diagnosis, leading to accurate treatment plans for the patient.

Technical details

The system works by creating a dynamic API end-point, where a patient can log into their provider website and download all their health records, which would then get integrated into a database with similar records previously imported from another provider system. Since the patient is the owner and the source of truth for this data, we can cross-check old records against new records to make sure they’re current, or get updated.

Concluding discussion–ownership and business modeling

I’m surely not the first to propose the idea of connecting health, electronic health records, or digitization of data. There are many organizations, research groups, and big-tech companies like Apple, trying to propose solutions in exactly the exact same problem space. The reason why my approach is different is that I aim to make the patient the owner of their medical data, and eliminate the aggregation of data in silos. Secondly, an effective and high impact solution shouldn’t aim to reinvent the wheel, because as outlined in the challenges section, it can prove to be too costly, and hard to integrate with existing systems. Therefore, to strategically position my approach, I wanted to leverage the pre-existing infrastructure.

Bridging gaps in the healthcare system is a wicked problem that lacks a straightforward solution. There are numerous stakeholders involved throughout a patient’s healthcare journey. Therefore to see a larger change we need systemic solutions in the form of policy change and legal protocols regarding medical data management.

Implementing the solution

For an integrated healthcare data management system to be successfully implemented and operated, it requires careful consideration of ownership of not just the system but the data as well. There are multiple viable models of ownership each with its own advantages and disadvantages.

1. What if Big Tech owns the system?

This is a compelling model because Big Tech companies have a wide reach due to their large user base which can support widespread adoption. They also have the technological infrastructure to implement such a system. However, we need adequate policies and regulations to protect the interest of the user and prevent a monopoly. While this model could have a large reach, it could also exclude those who don’t use their system, preventing them from having equitable access to health care.

2. What if the government owns the system?

The government provides the structure, through policies that help set the protocols that could bring alignment across different health care providers. But this model has the risk of being mismanaged and slow implementation. Along with robust policies we need a system that supports innovation and rapid evolution.

3. What if health insurance owns the system?

As with everything healthcare-related in the United States, it is defined by who is paying for it, typically if you have health insurance, this is where they step in. In the United States, health insurance providers are key stakeholders as they help patients pay for their medical bills. So, a model in which a health insurance company owns a system that manages health records could be a viable option since the health insurance company would already have access to the health records from healthcare providers sending it over to them for cost reimbursements. Further, this model could provide an opportunity for a rewards system that rewards people for being prompt about seeking healthcare similar to how car insurance rewards customers for being good drivers. However, in this model, we need to ensure that patients are protected from prejudices against them that may arise from a medical history that the insurance company doesn’t consider ideal leading to a refusal of coverage.




Designing for humans.

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