Using data in healthcare is hot: how to define a strategy not to burn your fingers!?
How to find the added value of using data (science) for healthcare
Introduction
Healthcare slowly starts to realize the enormous potential of data. A thousand reasons exist why data is so interesting for healthcare, but one reason should be enough here: healthcare costs are continuously increasing, and this is simply not sustainable. To illustrate this: as a ratio of GDP, the government health expenditures in Europe went from 6.2% in 2002 to 7.1% in 2016[1]. Healthcare, therefore, must become more efficient and one of the ways is by using data. A great example is the Portuguese hospital Sao Joao by implementing the HVITAL system. The HVITAL system collects physiological signs of every patient in the hospital and analyses this real-time to identify and give alerts regarding patients at risk for clinical deterioration[2]. This way the doctor or nurse receives a message when additional assessment or treatment is needed and care is given when- and where it is necessary.
But how do you get to these kinds of data solutions? What problems can you solve by using data? How do you know the best way to extract value from data and what data do you actually need? It is important to realize that data is always a means and not a goal in itself. To successfully become more data-driven, a coherent data strategy is needed that describes the purpose of using data and how it can bring added value. This data strategy guides you in knowing what to aim for and how to accomplish this step by step. In this blog, I’ll give you some insights on how to determine a good data strategy by a process we at Orikami call Data Value Mapping.
Data strategy in healthcare
A data strategy is a strategy for organizing, governing, analyzing and deploying an organization's information assets.[3] In other words, it is a strategy describing how data can have added value for your organization and how this added value can be obtained. In any organization, numerous possibilities of using data are possible. A good data strategy prioritizes these possibilities and shows you the way to go. The data strategy is best visualized in a so-called data roadmap, showing the steps you must undertake to go from the current situation to the future (data-driven) version of your organization.
To give you some ideas of how data can provide added value in healthcare, some examples are given below for three different segments within the healthcare domain. I’ll elaborate on these possibilities in a future blog. In the end, all examples come down to making your organization smarter by using data leading either to increased cost efficiency, better care or preferably the combination of both [4].
How to determine a good data strategy in healthcare?
In the Data Value Mapping process, we map the (potential) added value of current- and future data sources on the strategy and vision of the organization. This intertwinement of the data strategy and the company strategy is essential to achieve long-term constructive results. The data value mapping process consists of several steps which are schematically visualized in the figure below. In the following section I´ll guide you through the different steps of a data value mapping and within the quotation marks a specific example of a data value mapping we did for Hersenz. Hersenz is a treatment program, given by 13 different institutes in the Netherlands, for people with acquired brain injuries such as a cerebrovascular accident.
1. Exploration of current and future data sources
Before starting anything new, start by looking at what you already have. You’ll be surprised by how many data sources are already present in your organization and it would be a waste to not use these. In order to see their (potential) value, it is important to assess these data sources on accessibility, quantity, and quality. The quality and consistency of your data determine the quality and consistency of the conclusions you will draw from your data, so don’t underestimate this step. Explorative trend- and correlation analysis can thereby help to see the potential value of this data. To get a complete picture of data possibilities for your data strategy it is essential to do the same assessment for other possible data sources. For example, you can think of the use of wearables to generate data.
The most important data source at Hersenz is the Routine Outcome Monitoring (ROM) database. This database contains the results from five different questionnaires that are periodically administered to their clients. In this first step, an explorative analysis on this ROM data is performed to see the amount of data available per client, how many missing or wrong values are present and how consistent the actual data is with the acquisition protocol. The potential value of this ROM data is assessed by looking at the treatment effect for different parameters and correlations analysis of these outcomes with gender and age.
2. Define company strategy
Data solutions need to contribute to the goals that your organization has, e.g. to achieve the best possible care with reduced costs. One, therefore, needs to define the organization's strategy before a good data strategy can be built. Brainstorm about the ideal future state of your organization [5]. Due to the ever-changing interference of the payers in healthcare, it is important to also assess your business model. Will it stay as it is or do you foresee a different model for the future? A second step is to think about the current situation, for example in the form of a Strengths Weaknesses Opportunities Threats (SWOT) analysis [6]. A stakeholder mapping, for example by a power interest matrix [7], can also provide valuable insights giving direction to your data strategy.
Where does Hersenz want to stand in 2030? Together with a few representatives from Hersenz we did a brainstorm session in the form of a cover story[5] to find the answer to this question. One of the headlines in this vision was that the positive effect of the Hersenz treatment on the long-term quality of life had been proven.
3. Combining and explore (the “actual” Data Value Mapping)
The actual Data value mapping is to map the possibilities of current- and future data sources to accomplish the company’s strategic goals. In our experience, you need to do this in a co-creation process in which data experts, company leaders, and domain experts are present. The outcome of this mapping is a list of possible uses of data with their potential value. The SWOT analysis can be used to prioritize the abundance of ideas to find the biggest opportunities.
By connecting the current data sources and possible future data sources to Hersenz’ vision for 2030 many possibilities arose. For example, Hersenz’ treatment can be optimised by using the ROM data on the effect of the treatment in a real-time feedback loop to the practitioner to better monitor progress and adjust the treatment for the individual client. Another example is that Hersenz´ can benefit from using new data sources such as digital biomarkers and wearables to provide real world evidence towards payers and being able to show long term effects.
4. Development of the data roadmap
Develop a data roadmap showing the most promising ways of using data and which steps need to be done at what moment to become more data-driven and a smarter organization.
For Hersenz, all the possibilities discovered at step 3 were prioritised based on their potential in added value and amount of work that is yet needed. The most promising idea was worked out in concrete steps that Hersenz must take from now until 2025. Most important for Hersenz is the generation of evidence on the effect of their treatment, both for the payers and for themselves to be able to improve the treatment. Orikami gave advice on how to improve the current data acquisition and how to use the current- and other future data sources in the future to optimally generate evidence Hersenz is able to use towards payers and for treatment optimisation.
What to do with the data strategy — data roadmap?
So now we have a data strategy in the form of a data roadmap, what’s next? Having a data roadmap means you have thought about all the possible ways to extract value from data and formulated the biggest opportunities and what work needs to be done. Next step is the actual doing! Work out the most promising ideas in proof- of concepts and, if successful, implement them as full products. By doing this in an iterative fashion you can quickly move on once something turns out to be unsuccessful and keep implementing the successful proofs-of-concept at the same time.
Note: Don’t forget to occasionally revise your data strategy! Just as your business strategy, it might change because of the ever-changing environment.
To summarize…
Data in healthcare offers great potential for offering better- and more efficient care, for example in the form of personalization. If you don’t want to end up with random data products with limited use- and limited added value, it is essential to start by making a good data strategy. A way to do this is by following our 4-step Data Value Mapping process. Once you have developed your data roadmap you can start extracting value from data step-by-step!
If you want more information about data strategy in healthcare or if you require help to develop data solutions for healthcare, please don’t hesitate to contact me at frankdegraaf@orikami.nl or look at our site www.orikami.nl
References
[1] https://ec.europa.eu/eurostat/statistics-explained/index.php/Government_expenditure_on_health
[2] Almeida, J. P. (2016). A disruptive Big data approach to leverage the efficiency in management and clinical decision support in a Hospital. Porto Biomedical Journal, 1(1), 40–42. https://doi.org/https://doi.org/10.1016/j.pbj.2015.12.001
[3] https://hbr.org/2017/05/whats-your-data-strategy
[4] https://blog.orikami.nl/the-birth-of-a-digital-biomarker-a4a829370201
[5] https://gamestorming.com/cover-story/
[6] http://mci.ei.columbia.edu/files/2012/12/An-Essential-Guide-to-SWOT-Analysis.pdf
[7] Https://www.researchgate.net/figure/stakeholder-mapping-the-power-versus-interest-grid-the-gird-shows-stakeholders-on-a_fig2_275894050
