Leveraging Healthcare Data: What to do When You Don’t Know Where to Start
Story #1: Beth is an ED physician who has noticed specific issues within her facility:
- “We have noticed that patients who arrive at our facility with X condition or in Y circumstances require significantly more time or resources to serve,” or
- “With the changes in reimbursement policy we have to figure out how to more efficiently treat patients with X condition.”
She believes that it might be possible to leverage data to understand these issues and figure out how to improve them. But she’s not really sure how to do that.
Story #2: Alan is the CFO of a senior residential facility, and is well aware of all the data his organization has on its operations and resident care, but he isn’t really sure what to do with it:
- “We keep track of what happens to patients/residents/staff in the specific situations, but we’re not really sure what to do with that information,” or
- “We are always changing what we’re offering our residents in terms of activities, but we don’t have any way to track what’s being used or what residents like or prefer.”
He understands that there are potential opportunities to leverage these data, but he’s unclear about how to go about setting that up.
Story #3: Denise is the administrator at a hospital. She has noticed some inconsistencies regarding how staff are collecting and using data:
- “Two departments are both tracking this specific adverse event, but they are doing it differently so we can’t really compare how they’re handling it or see who’s doing so more effectively,” or
- “Our physicians look at these data because they feel they best reflect the quality of care provided, but our reimbursement is based on these data over here.”
She wants to figure out how to get everyone on the same page regarding how things are measured and evaluated, but she’s not really sure where to start.
These are hypothetical examples, but they reflect real-life experiences.
It is common for organizations to have a general sentiment that there is an area of their organization or some aspect of patient care that could be better, and that there exist data that could shed light on how to do that if leveraged appropriately.
It is also common, however, for the same organization to be unclear about how, exactly, to proceed.
If these stories resonate with you, then you probably empathize with their characters. What should they do? It can be hard to know what’s needed or how to proceed to gain more clarity.
In these types of situations, what is often best for the organization is to have someone help guide them through the most appropriate next steps. Someone to ask the right questions, advise on aspects of measurement and evaluation, assess the capabilities of available data, explore the “value” of solving the issue to see whether it’s worthwhile to pursue, etc.
These types of endeavors are often referred to as “Discovery Engagements.” The idea is to help sort through the noise to establish some clarity regarding what the problem is, what would be needed to solve it, and how would that benefit the organization.
Sometimes the outcome of these types of engagements is that a specific project is identified and defined, which can then be separately scoped and budgeted, and eventually pursued. Other times the outcome is simply a better understanding of what is and is not possible and/or the establishment of future goals and clarity on what information is needed moving forward.
There used to be one way to structure a Discovery Engagement, namely, conduct a short, intense endeavor that involved interviews, meetings, calls, and information transfer. The result would be a report that summarized the situation and maybe even provided recommendations for what to do next. This is often a very effective method and is frequently used to help provide clarity and direction. However, one of the key drawbacks is that this type of engagement works best when the organization is already clear about what their issue is. And, once the discovery is over, anything they encounter going forward is outside of the scope because the engagement has ended.
More recently, organizations have begun to warm to the idea of a different type of arrangement: a subscription-based membership model. In this model, organizations enter into an open-ended advisory engagement where select individuals have unlimited access to an external analytic advisor to answer questions, provide feedback, give guidance and advice, make suggestions, etc.; that is, the organization has a “data coach” to help promote and foster proper thinking about data-driven solutions. The advantages of this type of engagement are:
1. It’s flexible: It doesn’t require any level of specificity on the part of the organization – there may only be general sense that there’s an opportunity to improve their data-related activities. As individuals encounter situations or think of questions, they can reach out to their data coach to get immediate and relevant feedback that they can apply immediately.
2. It’s forward thinking: Over time, the data coach is gaining a greater understanding of the organization, its mission, its needs, and its capabilities. This nurtures and promotes forward thinking so that the data coach and the organization together can anticipate future needs, adapt data collection and/or evaluation activities, and prioritize activities to maximize the value and effectiveness of any specific data-driven projects that are identified.
3. It’s relatively inexpensive: Both because it is a “low intensity” engagement, but also because the fees are billed monthly and therefore spread over time.
4. It’s a known quantity: It allows for planning and budgeting, since the fixed fee is known in advance
So, how would this work for the characters in each of our stories? Let’s take a look.
Beth the ED physician: Over a few months, the “data coach” gains a good understanding of Beth’s ED, its workflow, and its patient population. Beth shares several examples she encounters during these months that are like those she previously noticed needing extra resources. Together, she and the data coach identify in these real-time examples what data to explore and how to identify what about these patients is driving costs. Armed with this information, Beth is able to plan and design a targeted intervention that should reduce these costs and increase efficiency without sacrificing care for these patients.
Alan the CFO: After their initial meeting, Alan sends the data coach details about what information they are tracking and which activities they are offering residents. During the next few weeks, he and the data coach refine specific questions he wants answered and identify specific metrics for determining when those questions are sufficiently resolved. Within 6 months, Alan has a much better understanding of how to leverage the information he’s already gathering, as well as what to spend time and resources to collect regarding resident activities to best evaluate which ones are the best ones for his facility.
Denise the administrator: Denise and leaders from 4 other departments are all given direct access to the data coach, and together they learn tools and techniques related to measurement and evaluation that they use to develop standard quality measures that meet everyone’s needs. At each step, the data coach reviews what they have done and offers suggestions for how they might improve the measures or the process of collecting them. Finally, the data coach helps them set up systems to regularly collect and track the measures, and designs a visually-appealing way to display the results to communicate key take-aways to staff.
In each case, the membership model gave these groups the flexibility to meet their unique needs, but in such a way that fit within their budget and timeline. And, as their needs change and grow, their data coach can continue to offer guidance, feedback, and support.
If you’d like to learn more about the membership program I offer, drop me a line or visit this page on my site: https://solidresearchgroup.com/services/club-analytic/