This is one chapter of a four-part series that discusses the Digital Maturity Healthcare Journey and provides actionable advice on how to identify your current stage and how to progress upwards in your digital maturity journey as an organization. To check out the additional parts of this series, click here!
The healthcare industry is flush with data on everything from patients to pathologies—the kind of data decision-makers in other industries can only dream about. Now that most healthcare systems have adopted some form of electronic health records the amount of data collected is growing at an astounding rate. In a report by Stanford University, they estimated that the amount of healthcare data collected was roughly 153 exabytes in 2013. That amount was estimated to have ballooned to around 2,314 exabytes—equivalent to 2.314 trillion gigabytes—in 2020.
But for the healthcare industry to see the full benefits of the available data, data collection alone is not enough.
As healthcare providers become more adept at using the data they collect to make better, more informed decisions, they can move with more ease from delivering care on a transactional basis to impacting health on a longitudinal basis.
Let’s look at the different levels on a healthcare provider’s journey to becoming a data-driven operation and how each level improves patient outcomes.
The provider has inconvenient access to raw patient data. This is the baseline which includes healthcare organizations whose collected data is stored in disparate databases with little to no transferability or data-sharing capability.
Just like healthcare systems are built and grow over time so too do their electronic capabilities. As tools became available different locations and departments had differing levels of adoption and standards. Email and calendar apps moved scheduling and communication off of paper. Imaging centers implemented software that drastically cut down on the storage space and retrieval time for images. Electronic spreadsheets were a game changer for Finance. EHRs took us from clipboards to computers.
Each system was implemented to solve a particular problem for a particular group of people. It didn’t take long for other groups to see the value in the data. Logging into multiple systems to find information is time consuming and matching up data is difficult. Every organization starts their journey with the realization that disparate data needs to be brought together.
The provider has organized their data, allowing them to describe patients’ current and past healthcare outcomes. This is the first step toward data-driven healthcare.
When a healthcare organization implements a framework that allows them to organize their data into a centralized, standardized database and access their patients’ information quickly, they will begin to see the early benefits of a data-driven healthcare model.
At this stage, healthcare providers are able to access patient records that combine information from a range of sources, including medical records from their own system and potentially even shared records from other institutions, allowing them to gain a deeper understanding of an individual patient's medical background—providing invaluable historical context to the patient's current medical condition.
Providers may also be able to cross-reference data across their current patient population, giving insight into potential growth opportunities. A clinic that identifies a substantial portion of their patients are receiving treatments that require outsourcing to another facility may decide to add a specialist capable of performing the treatment in-house.
Having an organized and easily accessible data set also means it is easier for providers to track the healthcare outcomes of patients and patient populations over time. Access to high-quality data on the health of a population is a great starting point that allows healthcare providers to measure qualitative health metrics, which can surface pain points that need intervention. As these interventions are implemented, progress can be tracked retrospectively as data continues to be collected over time.
However, while getting to this level is a valuable first step, healthcare providers are limited in what they can do with this data. The approach is inherently backward-facing, capable only of describing what has already happened—similar to checking out an encyclopedia from the library; you only have access to the available information at the time of publication. The ability to dynamically update patient data, view changes in real-time, and share patient files between specialists, clinics, and labs on the fly adds an additional level of value to this data—and opens the door to the next level of data-driven healthcare.
The provider has developed systems that allow them to integrate data streams into their workflows—connecting the right people with the right data at the right time. This is a major milestone toward unlocking the value of healthcare data.
Now that the provider has a standardized database of patient records, the next step is to integrate this information into their workflows.
Clinicians that have easy access to relevant data in their workflows don’t have to waste time looking for information that could be easily missed. Take for example a provider who is seeing a patient for a diabetes checkup. Surfacing records to display and graph the patient’s past A1C levels alongside the last progress note not only saves time, but may allow for connections between treatment and results to be drawn.
Healthcare providers can also create efficiencies by connecting patients with their own records directly through patient-focused digital healthcare platforms. A Digital Front Door can bring together appointment information, electronic check-in, follow-ups, as well as patient medical records and test results in a single patient platform—meaning patients can have more informed discussions and be more active in their treatment.
When patients have more access to, and control over their medical data, it opens up the door to two trends that have become customer favorites in many consumer spaces: Asynchronous communication and digital self-service. Giving patients the ability to book and change appointments online, download and share test results directly from their phone, and send questions or doubts to their provider via chat, means they can avoid unnecessary phone calls and in-person visits, saving time for everyone involved.
Not only do these integrated platforms free up valuable time for receptionists, nurses, and healthcare specialists who no longer need to spend as much time on the phone with patients or searching for charts and test results, they can help providers track a patient’s progress through their visit in real-time, opening up opportunities for more efficient scheduling.
The provider has implemented end-to-end systems that use patient data to inform & predict healthcare outcomes. Organizations at this level are on the cutting edge of the healthcare industry and will be able to innovate to improve patient outcomes.
While the first level focuses on creating value by organizing historical patient data, and the second focuses on integrating data to make current workflows more effective, the third level looks beyond the present moment and uses data to predict and improve future patient outcomes by anticipating needs and incentivizing healthy habits.
We can see many parallels between where the healthcare industry is now and where ‘Big Tech’ was just over a decade ago. As the internet was maturing and claims that ‘data is the new oil’ grew louder, many of the biggest names in tech invested in data collection, hoping to cash in on the big data oil boom. It didn’t take long for these tech titans to realize—as many in the healthcare industry are realizing now—that, just like crude oil, data is much more valuable after it has been refined and used to fuel the engine of growth.
And this isn’t some utopian vision for the future. Hospitals and clinics are already using Big Data analytics tools to predict emergency department visits and hospital admissions, analyze clinician notes to predict the onset of dementia sooner, and build predictive models that more accurately identify patients with a heightened risk of suicide.
The theory is the easy part. Things get a lot more complicated once we get into the real world. This is why we are always looking at how data is being used in real-life situations—and exploring ways to make it easier for healthcare providers to create systems that unlock the value of their data.
Our Director of Healthcare Product and Strategy, Terri Casterton, recently appeared on the Data Book podcast to have a more in-depth discussion of technological innovations and the use of patient-supplied data in healthcare.
Stage 1: Develop consumerism capability
Stage 2: Supercharge growth, engagement, and efficiencies
Stage 3: Anticipate and incentivize