Why the integration of location-based intelligence is a game changer for the industry.
Location data is healthcare data. The most significant, underutilized factor directly affecting human health is environmental in all its forms: how people live, work, grow, and play. Many public and commercial entities have rightly integrated awareness of social factors with respect to population health, but it’s not a complete data picture, nor is it a forward vision. A true, 360-degree assessment of how people live their lives — and therefore how all stakeholders in healthcare can drive down costs while uplifting patient outcomes — is only fully attainable by applying the data analytics acquired through spatial intelligence.
In the wake of the global pandemic, we are all adapting to a new normal. True, the healthcare benefits of integrating spatial analytics were on stakeholders’ radar before the pandemic landed in the U.S. For some, in an ancillary way; for others, it was factored in more heavily. But we now live in a moment where proximity to others and how you exist with those around you resonate in everyone’s thinking, all the time.
This increased attention reinforces what early adopters were already “evangelizing” about the untapped value of harnessing location intelligence with respect to mitigation and prevention: When implemented in conjunction with the clinical and financial data payers and providers already have, spatial analytics unlock a more complete picture of the individual, and numerous insights into more effective strategies become possible.
Value-Based Care Demands Value-Rich Content
Healthcare needs technology. And it’s ironic that the industry readily embraces new medical tech to stay cutting edge in the pursuit of delivering maximum good (think bioinformatics, AI-driven robotics, or mini “hearts-in-a-jar” as just a few examples), while often shunning similar technological advancements when it comes to contextualized, streamlined and frictionless data transmission — even when doing so has shown to directly benefit human health outcomes.
Throughout this article, we’ll cover the broad strokes of why the adoption and integration of healthcare spatial analytics not only accelerates the paradigm shift from volume-based models to value-based care, it also significantly improves outcomes along the way.
But first, some perspective: Healthcare expenditures for 2017 amounted to 18% of U.S. GDP, and is on track to become 1/5th of GDP by 2025.
That’s roughly $3.5 trillion, or just about $11,000 per person. (Incentivized yet? Wait, there’s more…)
Those figures are certainly alarming enough to spawn innovation on their own, particularly when compared to the qualitative health outcomes of other countries who spend far less to achieve superior results. Finally, we must factor in how an estimated 30% of U.S. health spend — close to $800 billion — is wasted on overpriced or ineffective expenditures.
Obviously, the numbers cited above for 2017 are all pre-COVID. The direct and indirect costs for healthcare in 2020 will likely take quite a while to accurately tabulate. The financials of contracting and treating COVID-19 are different for everyone who gets it. Wildly different. As payers and providers figure out their next moves, best to keep in mind that they were already grappling with skyrocketing costs.
We can agree that the level and scope of change needed has never been higher, as the cost trajectory pre-COVID was not sustainable. But how do we get there? How do we even start?
The answer lies in our approach to healthcare data capture, analysis, and utilization. Like every other facet of life, data generation has exploded in healthcare. However, when it comes to implementing proactive measures — built from predictive analytics — to improve human health, data utilization is often stuck in the past. Without the proper tools and perspective, this implementation is highly unlikely. And impossible if we’re not looking in the right direction — after all, you can’t change what you’re not measuring. We must seek out meaningful data. Specifically, this means the rich data sources possible when we incorporate the long-neglected or under-emphasized environmental factors showing how people actually live their lives.
Extracting raw data from trusted sources and transforming it into active analytics is an integral part of any enterprise’s data pipeline; in fact, it is perhaps the integral part. After all, the word “integral” means, “necessary to make whole and complete.” And much the way a rising tide lifts all boats, integrating spatial intelligence is a way to identify common denominators that many healthcare stakeholders share, such as testing new initiatives or pilot programs before committing to a costly investment or building out provider networks in a way that best matches up the right members for services in targeted populations or geographic centers.
How is this best accomplished? For one, we need to take healthcare data analytics from predominantly descriptive to increasingly predictive. The importance of this approach has only magnified with the onset of the global pandemic, and forward-thinking tech companies in the health sector are creating proactive tools to highlight probable vulnerabilities among our varied populations. But as a general assessment, simply stating we should pivot from descriptive to predictive is itself a simplification, because much of the descriptive analytics that payers and providers already have in their possession is not being utilized properly, let alone optimized. Legacy systems and siloed data are often the enemies of innovative — and effective — approaches to value-based care. So let’s begin with this: make your data make sense.
(And let’s not forget that we’re already paying full price for everything that determines health outcomes — medical, genetic, operational, social, physical, behavioral and environmental. So why would we only engage with a part of the available data?)
Transforming Raw Data into Actionable Insight
When a car is brought into the shop for repairs, we catalog the type of work done and how much it costs to fix it. Both useful, necessary metrics to documenting the history and continued health of the vehicle — important “clinical” and financial data. But it’s still looking backward, reactive rather than proactive in nature, and here a straight line can be drawn to traditional methodologies with respect to healthcare treatment: we too often wait until something is wrong, and then we set about using the tools at hand to fix it.
To say that prevention and maintenance are significant factors when it comes to determining outcomes (whether we’re talking about scheduled medical check-ups or routine oil changes) would be an understatement. What we need to apply also has to do with a certain mindset as much tech implementation: we must begin addressing the first-mile inpatient care, instead of predominantly reacting to the last.
To illustrate how spatial intelligence can provide direct benefit with respect to healthcare optimization, we’ll put forth the following data-related questions:
(I) What are the top human-centric health needs to be enhanced via spatial intelligence? (What data jobs are essential? And what data jobs would you like to incorporate, but didn’t think possible or viable?)
(II) What are the top stakeholder business needs to be enhanced via spatial intelligence? (Again, which familiar data jobs could be improved upon with better analytics, and which new, insight-rich data jobs could stakeholders adopt and implement?)
Probable answers include, but certainly not limited to:
- Match individuals more accurately with their true healthcare needs
- Implement real-world modeling for targeted population insight
- Highlight successful programs that align with value-based care
- Employ predictive profiling to scale up member acquisition rates
- Structure results-based community health management
- Visualize service needs and usage patterns with greater clarity
- Gauge trends in both outperformance and underperformance
- Effectively build out networks, efficiently drive down costs
- Predict and mitigate risks with greater certainty
- Optimize expansion planning through competitive market analysis
The benefits and advantages will overlap the same way that stakeholder missions do, but one constant is clear: better analytics equals better outcomes.
The need to analyze health data generated where we live our lives, including biological, socioeconomic, behavioral, and social factors, as well as the physical space a person inhabits, has never been greater. Painting a clearer, more complete data picture — one that gives context to the numbers — helps stakeholders “see around corners,” revealing the data-driven insights that elevate human-centric healthcare.
It sounds pretty straightforward because it is: understanding how people live, work, grow and play is key to helping them stay healthy, and keeping people healthy equates to a reduction in the need for healthcare services. But accurately screening how environmental determinants impact patient health still has a long way to go: a cross-section of hospitals and physician practices reported an average of 24% and 16%, respectively, when it came to screening for food, housing, transportation, utilities, and interpersonal violence — basic social needs or concerns.
An increased awareness of the human-spatial relationship, coupled with decisive action, has already shown how cost containment strategies and expenditure reductions present themselves when health systems are inspired to innovate. They can see the near-immediate ROI; as an example, when an MCO experiences a cost reduction of 10% while simultaneously uplifting the quality of life for a targeted population, a window into a brighter, more efficient future presents itself.
Fee-for-service models, where financial incentives aligned with volume quantity rather than care quality, are in the rearview mirror. As the healthcare paradigm shifts to value-based care, stakeholders with the most actionable data — and the ability to utilize it in context — will be the ones delivering the highest level of patient care, and at the lowest possible cost. This is where spatial analytics allows stakeholders to highlight and understand community service needs and usage patterns in useful, innovative ways that simply aren’t possible using legacy software and clinical and financial data alone.
Doing the Same Thing and Expecting Different Results
Imagine leaving your house, setting out on a cross-country family vacation, and you have access to the latest weather data but choose to ignore it. Sunny skies, torrential rain or blinding snow — anything environmental — isn’t factored in ahead of time. Should something problematic occur, you’ll just deal with it after the fact, and try to mitigate the cost after assessing the damage.
No one does that. Except in healthcare.
It bears repeating: When considering the potential impact of spatial analytics as a measurable driver in improving healthcare outcomes, it’s important to remember the stakes at play here if we don’t change the status quo with respect to implementing location intelligence.
One glaring example: the CDC estimates a full 75% of all U.S. health spend is on chronic disease.
We know that a better understanding of environmental factors would only reduce that astronomical number, especially when coupled with tracking the real-time impact of preventative measures or test initiatives deployed to at-risk populations. Spatial analytics takes contextualizing dynamic, nonclinical risk from a largely conceptual notion to a more operational task.
This is all the more significant when we consider the $2.5 billion health systems invested in social determinants between 2017 and 2019. It’s a considerable number, but it represents an allocation of less than 1/10th of 1% of the above-mentioned $3.5 trillion annual spend — and that’s to address the human-relevant data that healthcare professionals often say accounts for 80% of health outcomes.
If we let the simple math sink in for a moment — you have $100 to put toward solving a problem, and just one thin dime set aside for addressing 80% of that solution (the other $99.90 goes elsewhere) — it’s easy to see how embracing the active analytics possible through spatial intelligence will start bridging that gap. The best part? It’s quantifiable technology that’s available today.
Adopt, Adapt or Perish
Healthcare systems have a consistent track record of taking a long time to embrace and adopt new technology, particularly when it comes to the efficient transfer of patient data. Data sharing, where it does exist, is often woven together in ways that are more than ready for an updated, streamlined approach, revamping an unnecessarily redundant and heavily siloed framework built upon legacy systems and methodologies. (And let’s not omit the bloated admin costs associated with data inefficiencies. If there’s one thing about healthcare we can all agree on, it’s that the costs keep escalating. And we’re all paying the price.)
In contrast, location data is focused on the human-spatial relationship. The real-world insights derived from its adoption provide little value sitting in place, or waiting a turn inside a bottlenecked system. Those insights require efficient distribution via SaaS platforms calibrated to optimize healthcare data and must do so with a 2020s perspective, not a mindset from when fax machines roamed the earth.
There is an upbeat note about the future of interoperability with healthcare analytics, that of HHS promoting a new approach to be universally adopted with respect to anti-data blocking. The new rules, issued by CMS and the Office of the National Coordinator for Health Information Technology (ONC), support their (correct) assertion that, “New innovations in technology promote patient access and could make no-cost health data exchange a reality for millions.”
Some health professionals are banding together against this innovative step, whether for legitimate concerns about patient data or monopolistic desires to leave things as they are because profitability is threatened. But data rights are human rights, full stop. We live in a data-saturated world, and putting roadblocks between patient-authorized B2B data sharing is the opposite of “First, do no harm.”
Charting the Future of Dignified, Individualized Healthcare
We all want technology to work for us. In fact, we readily assume as much being the standard default in our daily lives and even take for granted that innovation drives the adoption of new tech, and vice-versa. The proof may be in your hand right now as you read this.
In the decade between 2010 and 2020, market penetration of smartphones leaped from 20% to over 70%. (Would you settle for 2010 tech in your current phone?) Reviewing healthcare milestones during the same ten-year period should continuously inspire us to better implement available technology so that we may improve human health outcomes throughout the 2020s.
Making the necessary data connections in order to optimize performance and delivery models in value-based care — identifying, measuring, and evaluating how people truly live their lives — is an idea whose time has come.
It’s difficult to conceive of another massive industry — think real estate, financial services, manufacturing, etc. — that doesn’t put an emphasis on geospatial intelligence and the frictionless transfer of secure data. When all relevant, external data is aggregated and contextualized into useful, actionable analytics, then real-world modeling becomes possible and predictive insights generated. Every other industry expects as much. Shouldn’t we demand the same from ourselves when seeking to optimize healthcare?
In closing, the intersection of healthcare, data science, and spatial intelligence is unlocking new avenues of insight and efficiency for every stakeholder, every day. From personalized healthcare analytics to assist individuals and their providers make better, more effective choices and informed decisions, to data-driven insights applicable across a large payer or provider ecosystem, helping build or “bake in” efficiencies absent from their current business models — a growing number of cutting-edge industry leaders and conscientious healthcare professionals understand what is literally “hiding” in plain sight: location data truly is healthcare data.