transforming medicine with data - sc2i · technology is reshaping medicine around the world. from...

Transforming medicine with data

Surgical Critical Care Initiative Report

Transformingmedicinewith data

Improving patient outcomes and reducing the cost

of healthcare delivery requires medical research

that uses data in new and innovative ways. The

Surgical Critical Care Initiative (SC2i) does just

that. It has pioneered the use of advanced analytics

to create powerful clinical decision support tools,

which hold the potential to not only improve

the lives of thousands of patients, but also

save billions of dollars. This report details

those cost savings.

estimated annualcost savings forU.S. Military HealthSystem

$111M

estimated annualnumber of improvedpatient outcomes inU.S. healthcaresystem

467,700

estimated annualnumber of improvedpatient outcomes inU.S. Military HealthSystem

5,619

estimated annualcost savings forU.S. healthcaresystem

$10B


Technology is reshaping medicine around the world. From the rapid proliferation of telemedicine to the widespread use of tools and techniques that support a variety of clinical decisions. All of these technologies are creating new opportunities to improve patient outcomes while concurrently reduce the cost of healthcare delivery. Clinical decision support tools Today, healthcare researchers are gaining access to an ever-increasing amount of digital data. With the application of new technologies, they have been able to use that digital data to create clinical decision support tools (CDST), many of which have shown remarkable potential to produce clinically useful insights. Ultimately, those insights enable clinicians to raise the prevailing standard of medical care for their patients. The power of CDSTs resides in their ability to rapidly ingest vast quantities of data and to avoid common biases that undermine human judgment. That allows CDSTs to make predictions with great accuracy. Future CDSTs

will be able to not only make predictions, but also compare those predictions with newly collected data, and then evolve to make even more accurate predictions. Developing such CDSTs has traditionally required considerable time and resources. But those research efforts that concentrate on successfully combining advanced analytics and machine learning can now deliver insights far faster. The U.S. Department of Defense’s Surgical Critical Care Initiative (SC2i)—the U.S. military’s premier critical care research program—is one of those efforts. Two of the CDSTs developed under SC2i’s auspices are already in use today, one with the U.S. military and the other with Emory Healthcare in Atlanta. Several of SC2i’s CDSTs currently have patents pending. Of course, not all CDSTs are created to the same standard. Many are simple rules-based procedures to avoid basic clinical errors. Other CDSTs have begun to combine advanced analytics with a patient’s clinical and biological profile to recommend personalized treatments. Though still uncommon—even among the predictive tools offered by electronic health records systems—the latter sort of CDSTs will


ultimately make the promise of “precision medicine” a reality. SC2i strives to do just that for both civilian and military critical care delivery. The initiative develops its CDSTs using all of a patient’s available medical data, which may include biomarker, clinical, drug, imagery, and laboratory data. Combining them with advanced analytics, SC2i’s CDSTs have already begun to support better medical treatment decisions that will improve patient outcomes and reduce healthcare costs. Cost savings from SC2i’s CDSTs This report seeks to estimate the cost savings that SC2i’s CDSTs could generate once they are widely deployed across the United States and the U.S. Military Health System (MHS). In both civilian and military healthcare settings, the potential cost savings are likely to be substantial. The SC2i CDSTs we assessed address the following medical conditions: • Surgical wound closure • Sepsis • Pneumonia • Acute kidney injury

• Bacteremia • Venous thromboembolism • Severe traumatic brain injury Collectively, we expect SC2i’s CDSTs could yield annual cost savings of $9.5 billion for the U.S. healthcare system and $110.6 million for the MHS. To create those aggregate cost savings estimates, we separately examined each of SC2i’s CDSTs to determine how much money each tool can save from the treatment of the medical condition that it targets. For each CDST, we considered two general attributes: the costs associated with treating a patient with the medical condition that the CDST targets and the addressable patient population which experiences that medical condition in the United States and the MHS. While the insights from some of SC2i’s CDSTs can wholly prevent a medical condition from arising, others may only reduce the severity a medical condition. In the former cases, all the costs associated with a medical condition can be eliminated. But in the latter cases, only a portion of such costs might be removed, as a patient would still require some level of medical treatment. In those cases, we had to

Engines of Cost Savings:Surgical Critical Care Initiative’s CDSTs

$10B $111M

Sepsis

Surgical Wound Closure

Pneumonia

Acute Kidney Injury

Venous Thromboembolism

Bacteremia

Severe Traumatic Brain Injury

UNITED STATES MILITARY HEALTH SYSTEM


Engines of Patient Care:Surgical Critical Care Initiative’s CDSTs

467,700 5,619

Sepsis

Surgical Wound Closure

Pneumonia

Acute Kidney Injury

Venous Thromboembolism

Bacteremia

Severe Traumatic Brain Injury

UNITED STATES MILITARY HEALTH SYSTEM



assess not only what the total costs associated with a medical condition would be, but also what the costs to treat a milder version of that condition could be, after the insight from one of SC2i’s CDSTs is applied. The difference between the two figures would represent the true cost savings for that CDST. Cost discussion When addressing medical costs, there are many types to consider. Ultimately, most of them are the product of different perspectives, whether from the standpoint of cost allocation (direct vs. indirect), price setting (healthcare provider vs. healthcare insurer), payment regime (private vs. public), geography (here vs. there), or time (before vs. now). Each perspective introduces an additional layer of variability into any assessment of medical costs. Cost allocation When health economics studies stratify costs, they usually do so into two categories: direct and indirect. Direct costs are typically those healthcare expenditures related to a patient’s diagnosis, treatment, and rehabilitation. They

involve the direct labor (allocated portions of medical professionals’ time) and materials (ranging from the allocated portions of medical equipment to the per-night cost of a hospital bed). On the other hand, indirect costs can be quite broadly defined. They may include healthcare expenditures associated with the function of a healthcare institution, such as maintenance, security, or waste disposal. They could also involve direct labor (allocated portions of healthcare administrators’ time) and materials (ranging from allocated portions of medical technology, like an electronic health record system, to the construction of a new building). Even further afield, some studies have included the consumption of non-healthcare related resources, such as transportation, relocation, and home care, as indirect costs. Unfortunately, most studies do not identify the kinds of costs they cite as direct or indirect. Wherever possible, we focused our research on direct costs, those that can be directly attributable to the treatment of a medical condition. Furthermore, our research methodology focused on a particular sort of direct cost, hospital charges, and purposely excluded another, clinician charges, to avoid


inconsistencies across our findings. That is because reported costs for most of the medical conditions we studied either did not include clinician charges or were unclear whether they did so. By narrowly focusing our research, we likely underestimated the cost savings of some CDSTs—most notably those which address conditions, like severe traumatic brain injury, that either require a great deal of clinician attention or carry with them high indirect lifetime costs. Price setting Differences in perspective also play a major role in the wide discrepancies between reported medical costs. Nominally, healthcare providers, which directly serve patients, set the prices for the treatment of medical conditions. Healthcare providers tally up the charges for treating patients and send them to patients’ healthcare insurers. However, as is widely known, healthcare insurers will reimburse only a fraction of the total charges. In effect, health insurers have considerable influence over how the prices for the treatment of medical conditions are set.

Given the often-large divergence between what healthcare providers charge and what healthcare insurers provide in reimbursement payments, one could view the latter as the better representation of the true costs to a healthcare system. However, the great variation in reimbursement payment regimes—including commercial, managed care, self-pay, worker’s compensation, Medicaid, or Medicare—made it difficult for us to use reimbursement payments as the foundation of our cost analysis. Therefore, we focused our research on healthcare provider charges, which were more readily available across the medical conditions we evaluated. Payment regimes As mentioned earlier, different payment regimes also impact medical costs. One analysis of healthcare provider charges for over 2 million American patients from 2005-2010 demonstrated that privately insured patients incurred significantly higher average charges than did Medicare, Medicaid, self-pay, or no-charge patients, even after normalizing for patient condition and treatment.1

1 Velopulos CG, Enwerem NY, Obirieze A, Hui X, Hashmi ZG, Scott VK, Cornwell EE 3rd, Schneider EB,


Meanwhile, the difference between the charges that private and public insurers have been willing to pay grew. In 1996, private insurers paid about 6 percent more than public insurers, like Medicare and Medicaid, did. Two decades later, that gap had grown to between 25 and 75 percent.2 As a result, there are substantial variances in charges and payments for treatments of the same medical conditions between groups of healthcare providers that serve patients under different payment regimes. To avoid such distortions, we focused our research on cost data from nationwide patient samples. Geography Unsurprisingly, the costs associated with medical conditions vary across geographies. The clearest evidence of that is across national

Haider AH, “National cost of trauma care by payer status,” J Surg Res. Sep. 2013; 184(1):444-9. 2 Thomas M. Selden, Zeynal Karaca, Patricia Keenan, Chapin White, and Richard Kronick, “The Growing Difference Between Public and Private Payment Rates for Inpatient Hospital Care,” Health Affairs Dec. 2015; 34:122147-2150; Tami Luhby, “Medicare vs. private insurance: Which costs less?” CNN Money, Apr. 21, 2014.

boundaries, where both the payment regime and the standard of care of one country may differ from those of another. Moreover, fluctuations in currency pairs make comparisons of medical costs between two countries, especially over time, highly fraught. Through our medical literature review, we also observed meaningful differences in medical costs across various states (and regions) of the United States. Ideally, one would seek to normalize those differences by aggregating data from healthcare institutions across all 50 states. But few comprehensive studies do so with the fidelity we require to properly examine the specific medical conditions that SC2i’s CDSTs target. Ultimately, our research methodology combined cost data from regional and national studies to best determine the potential cost savings that SC2i CDSTs could yield. Time Naturally, time also influences medical costs, given the steady drumbeat of healthcare inflation. In our review of medical literature from 1990 to the present, we noted a significant rise in medical costs for similar treatments in the United States. In fact, the


inflation in medical costs exceeded the nominal rate of inflation throughout that period. From 2000 to 2005, critical care medical costs per day increased by 30.4%, whereas the nominal rate of inflation over that same period was 13.4%. That suggests that any comparison of cost data collected across a long span of time would be subject to substantial error. Thus, we focused our research on a narrow window of six years, from 2011 to 2016. Addressable patient population discussion While accurately associating medical costs to certain medical conditions is challenging, equally if not more challenging is determining the patient populations that SC2i’s CDSTs could address. That in turn required an understanding of the incidences of the medical conditions that SC2i’s CDSTs target. Ultimately, several factors constrained our research of addressable patient populations, the most prominent of which were the lack of relevant epidemiological studies, changes in medical condition definition, healthcare institution types, and geography.

Relevant epidemiological studies Since SC2i’s CDSTs are designed to help clinicians make decisions at various points during their treatment of certain medical conditions, we sought to ascertain the incidences of those medical conditions at those points within the populations of the United States and the MHS to define the CDSTs’ addressable patient populations. Unfortunately, few epidemiological studies have been conducted with such detail in mind. For example, SC2i’s acute kidney injury (AKI) CDST targets those patients whose AKI condition has advanced to the stage at which renal replacement therapy might be necessary. But no epidemiological studies, which focus on that AKI stage within the MHS population, have been published. Generally, we used the broadest epidemiological studies to serve as the foundation of our research. We then used smaller studies to focus on the points where our CDSTs make their impact on clinical decision-making. Naturally, most of our research on the incidences of medical conditions within the


U.S. population relied on data and studies from the Agency for Healthcare Research and Quality’s Healthcare Cost and Utilization Project, given its nationwide reach. Similarly, much of our research on the incidences of medical conditions within the MHS relied on data from the MHS’s Annual Report to Congress and extrapolated data from the MHS’s National Capitol Region. Medical condition definition changes Growing awareness of the benefits from data-centric medicine has recently led many clinical communities to refine their definitions of medical conditions. While very valuable for the future data analysis, what that has also meant is that some patients who were once diagnosed with a certain medical condition may no longer fit that diagnosis. That makes incidence data collected prior to any definition change difficult to compare with data collected today. Such medical-condition definition changes impacted the breadth of historical studies that could be used to assess the cost savings of SC2i’s CDSTs, particularly its AKI and bacteremia CDSTs.

Possibly the most significant case was that of SC2i’s sepsis CDST. Citing the “inadequate specificity and sensitivity of the systemic inflammatory response syndrome (SIRS) criteria,” The Third International Consensus Definitions for Sepsis and Septic Shock published a new definition for sepsis in the Journal of the American Medical Association in early 2016. Rather than a collection of inflammatory indicators (body temperature, heart rate, white blood cell count) in conjunction with an organ dysfunction, sepsis would be defined as an infection in conjunction with an organ failure assessment score of two points or more. The scope of the new definition is not only different, but also meaningfully narrower than that of old definition. Since SC2i’s CDST was created with sepsis’ new definition in mind, we could not use most historical studies of sepsis to determine its incidence. Moreover, the change in sepsis’ definition was so recent that no new epidemiological studies have been conducted yet using the new definition. In such cases, we focused our research on incidence data of medical conditions that are close facsimiles to those we sought.


Healthcare institution type and geography Among the epidemiological studies that are publicly available, many of them focus on certain healthcare institution types, such as Level I trauma centers. While useful in many respects, those studies are poorly suited for nationwide extrapolation. For example, Level I trauma centers are far more likely than community hospitals to admit patients with trauma. As a result, the incidences of medical conditions related to trauma are likely to be skewed at Level I trauma centers. Similarly, the incidences of medical conditions in certain geographies are likely to be biased to those incidences that are most prevalent in them. Thus, our research methodology focused on the combination of two different approaches to determining the incidences of medical conditions: a top-down approach and a bottom-up approach. Our top-down approach used nationwide patient samples to create a relevant patient population baseline. However, using such an approach in isolation, given the generality of nationwide patient samples, could not produce the level of detail needed to determine the incidences of medical conditions that SC2i’s CDSTs target.

Thus, we turned to a bottom-up approach, which used smaller studies to focus on the incidences of medical conditions (and their specific aspects) that SC2i’s CDSTs target. However, since those smaller studies may have investigated incidence data from a particular healthcare institution type or geography, we could not immediately extrapolate their findings. Our research methodology integrated both approaches. As a baseline, we used nationwide patient studies to ascertain the number of trauma and intensive care unit (ICU) patients in the United States and the MHS. Then, we sought smaller studies that examined the incidence of medical conditions related to SC2i’s CDSTs within trauma and ICU patient populations. Estimated efficacy of SC2i CDSTs SC2i is developing several CDSTs simultaneously. A few of these have already achieved a positive predictive power, a measure of their accuracy, of over 90%. However, given that most of them are still under development, we estimated that they would achieve a positive and negative


predictive power of 85% at the time of their deployment for the purposes of estimating their cost savings for the U.S. healthcare system and the MHS. Estimated efficacy and cost of clinical prophylaxis Of course, even the most accurate CDST will not produce any cost savings unless a clinical prophylaxis can be applied to effectively prevent the onset of the medical condition identified by the CDST. Thus, the effectiveness of prophylaxes can have a meaningful impact on the cost savings that CDSTs can deliver. Unfortunately, prophylaxes are not always effective. For example, the most common prophylaxis for venous thromboembolism (VTE) is effective in 54.4% of all cases of the condition. So, even if a CDST accurately predicts the onset of VTE in a patient and clinicians implement a prophylaxis, that patient may still develop VTE. That is particularly relevant for SC2i’s CDSTs that address hospital-acquired infections (HAI). Over the last decade or so, hospitals

have already taken stringent steps to prevent the occurrence of HAIs. Apart from redoubling those efforts, it is uncertain how much more effective they will be to prevent HAIs in patients—even those who a CDST have predicted would acquire a HAI. Generally, we estimated that the prophylaxes for the medical conditions which SC2i’s CDSTs address are 50% effective. Finally, we had to consider the costs of the applied prophylaxes. While a CDST can avoid the cost needed to treat a medical condition, it cannot avoid the cost of a prophylaxis. That cost can vary widely, depending on the medical condition. In most cases, the cost of a prophylaxis is a small fraction of the cost needed to treat its targeted medical condition. For example, we assessed the costs of AKI’s and VTE’s prophylaxes to be 10% and 14% of the costs needed to treat their respective medical conditions. At other times, the cost of a prophylaxis may be higher. Generally, we estimated that the costs of the prophylaxes for the medical conditions which SC2i’s CDSTs address are 25% of the costs needed to treat those medical conditions.


Estimated cost savings of SC2i CDSTs Surgical Wound Closure SC2i’s surgical wound closure CDST, called WounDxTM, aims to predict when clinicians should close open wounds to minimize the likelihood of dehiscence among trauma patients. Properly timing the closure of open wounds—aiding the human body’s natural proclivity to heal—reduces the need for surgical debridement and shortens the amount of time surgical patients require for recovery and rehabilitation. Greater efficiency in treating trauma patients means lower costs at every stage of their surgical recovery and rehabilitation process. Cost savings, annual

U.S. $2,312,263,222 MHS $41,427,773 Patient outcomes improved, annual

U.S. 67,930 MHS 1,217

Sepsis SC2i’s sepsis CDST aims to predict whether ICU patients will develop sepsis, using not only clinical data, but also real-time data from a full suite of ICU bedside monitors. Early identification of patients who are likely to develop sepsis would enable therapeutic interventions that could prevent the onset of sepsis and thereby avoid the costs needed to treat the condition in the ICU. However, our assessment needed to account for the fact that many of those patients would still incur costs related to treating precursor conditions, such as a single infection or organ failure, that could manifest themselves prior to the full onset of sepsis. Cost savings, annual


U.S. 196,877 MHS 587


$2.1B


$5.5M

Sepsis

Surgical WoundClosure


$2.3B


$41.4M


$1.9B


$10.1M

Pneumonia

Acute KidneyInjury


$1.6B


$18.0M


$919.7M


$8.4M

Bacteremia

VenousThromboembolism


$626.6M


$21.6M

Severe TraumaticBrain Injury


$24.9M


$5.6M


Pneumonia SC2i’s pneumonia CDST aims to predict whether trauma patients will develop pneumonia during their stays in a healthcare facility. Early identification of patients who are likely to develop pneumonia would enable therapeutic interventions that could avoid the costs needed to treat pneumonia. Cost savings, annual


U.S. 83,231 MHS 667 Acute Kidney Injury SC2i’s acute kidney injury (AKI) CDST aims to predict whether ICU patients who are at risk of AKI will eventually suffer a kidney injury or failure that requires renal replacement therapy. Early identification of those patients who are likely to suffer those conditions would enable therapeutic interventions that could prevent the need for expensive renal replacement therapy.

Cost savings, annual


U.S. 45,269 MHS 1,358 Bacteremia SC2i’s bacteremia CDST aims to predict whether trauma patients will develop bacteremia during their stays in a healthcare facility. Early identification of patients who are likely to develop bacteremia would enable therapeutic interventions that could avoid the costs needed to treat bacteremia. Cost savings, annual

U.S. $919,669,351 MHS $8,373,836 Patient outcomes improved, annual

U.S. 37,832 MHS 514


Venous Thromboembolism SC2i’s venous thromboembolism CDST aims to predict whether trauma patients will develop venous thromboembolism during their stays in a healthcare facility. Early identification of patients who are likely to develop venous thromboembolism would enable therapeutic interventions that could avoid the costs needed to treat venous thromboembolism. Cost savings, annual


U.S. 35,586 MHS 1,067 Severe Traumatic Brain Injury SC2i researchers are studying several approaches to potential severe traumatic brain injury CDSTs. The most promising approach involves predicting whether trauma patients will experience vasospasms after an aneurysmal subarachnoid hemorrhage during in-patient stays. Early identification of

patients who are likely to experience vasospasms would enable therapeutic interventions that could avoid the costs needed to treat them and one of their major subsequent complications. Cost savings, annual


U.S. 974 MHS 208 Conclusion Today most clinicians would admit that the volume and complexity of the decisions that they regularly encounter have become overwhelming. CDSTs offer them a way to ease that burden. With the broad adoption of electronic health record systems over the last decade, healthcare institutions are well positioned to take CDSTs out of research and put them into practical use. Among those CDSTs that are nearly ready for deployment are ones that were developed


within SC2i. To create its CDSTs, SC2i took full advantage of key advancements in analytic and machine-learning technology, particularly the unique ability to create highly accurate predictive models using sophisticated Bayesian networks and built entirely from input data and without human bias. Once in common use, SC2i’s CDSTs are likely to not only improve patient outcomes, but also lower the cost of healthcare delivery. The potential cost savings that CDSTs could achieve in the United States are substantial. McKinsey, a global consultancy, “estimates that [data-centric approach to healthcare] could account for $300 billion to $450 billion in reduced [annual] health-care spending, or 12 to 17 percent of the $2.6 trillion baseline in US health-care costs.”3 SC2i’s CDSTs could meaningfully contribute to those cost savings. Across the CDSTs that SC2i has in advanced stages of development, we anticipate that they will generate substantial cost savings for the U.S. healthcare system

3 Peter Groves, Basel Kayyali, David Knott, Steve Van Kuiken, “The ‘big data’ revolution in healthcare: Accelerating value and innovation,” McKinsey & Company, Jan. 2013, p. 8.

and the MHS. What SC2i needs now is a way to rapidly deploy its CDSTs across the United States. More than ever, healthcare providers need to adopt new technologies to diagnose and treat their patients if they aspire to deliver better outcomes at an affordable cost. The time is right for them to use CDSTs to impact the way health insights are embodied and delivered. While the big-data revolution in healthcare is still evolving, SC2i’s CDSTs are forefront of innovation. They also offer the opportunity to create meaningful cost savings for both the U.S. healthcare system and the MHS. The broader benefits from their use hold the potential to be positively transforming for not only healthcare systems, but also the patients they serve.

transforming medicine with data - sc2i · technology is reshaping medicine around the world. from...

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