Every healthcare stakeholder accepts that it is a period for clinical analytic to get up and run. But merely because everyone’s behind an attempt surely does not make it convenient.
clinical analytic, at this level in its evolution, is truly a race against time. For contributor organizations, the future of value-based care came much faster than hoped: Medicare recently declared a passionate timeline for transitioning to value- and risk-based reimbursement models, and state governments and private insurers have been relentless in their attempts to tie compensation to quality, with the overriding objective of cutting their entire prices of healthcare.
In response, contributors are scrambling to embed clinical analytics into their networks to recognize present and future high-risk sufferers. With so much revenue at threat, letting a handful of sufferers slip through the cracks and rack up great treatment costs can sink the best attempts to control the prices of huge populations.
For instance, at Atrius Health, a push further into population health has disclosed a “gray area” within its sufferer population that typical risk-modeling and clinical analytic efforts overlook, claims Joe Kimura, MD, chief medical officer at the Boston-based not-for-profit alliance of medical groups that contains 42 locations, 750 physicians and 6,800 workers.
“With the tools present now, it is quite simple to know your highest-risk sufferers, because they are in and out of your services and the hospitals—you have various encounter data to work with,” he claims. “But we are quite much at complete threat for our sufferer population, so we require to get into that gray place of our population that we do not have much of a dialogue with, generally males between 20 and 40 years old who nearly never come to see us, and when they do, they come in for something minor, such as a sprained ankle.”
Atrius Health is utilizing natural language processing software (Kimura refusal to recognize the vendor) to observe unstructured growth notes and other unstructured text within the EHR to extract clinical ideas to run through its risk algorithms. “Even when someone comes in for a minor occasion, we do what we always do, and complete a note, and within that note is data about lifestyles and socioeconomic problems and other clues about whether the sufferers are at threat for diabetes and obesity,” Kimura states. “There is so much data is in that narrative that can be utilized by our algorithms for sufferers that in the past would not be flagged because we did not have sufficient occasions to key a picture of them.”
Various healthcare agencies are finding analytics attempts difficult to undertake.
At University of Mississippi Medical Center, executives were behind a huge push to move into population health management and predictive analytics to respond to the incessant economical pressure to lower prices while keeping sufferers healthier. So the medical center began assembling the information sets deemed essential to observe its population.
The attempt to build up data sets took 7 long months. When the information was compiled, the organization started the procedure of scrubbing and eradicating outliers, filling in null values and otherwise transforming that information in a number of ways. For John Showalter, MD, UMMC’s chief health information officer, the procedure seemed to be taking forever.
After discussing through the issue with Jvion, a predictive analytics firm with which the medical center worked, Showalter took UMMC’s attempt in a drastically different direction—rather than spending months cleaning up the EHR data, UMMC began sending Atlanta-based Jvion raw “dumps” from its EHR, which Jvion then feeds into its machine-learning network and adds various other clinical and socioeconomic variables, like U.S. census data and information from credit reporting firms.
What comes out are predictive risk scores for a range of health problems UMMC is targeting, involving heart disease, hospital readmission, pressure ulcers, blood clots and hospital-acquired infections. The risk scores are brought back into the medical center’s EHR, from Verona, Wis.-deployed Epic Systems, and are visible to physicians when they are in the patient’s record. At that point, the physician can put a sufferer on a risk protocol for a specific condition, and trigger orders and alerts for other caregivers.
“I consider everyone is trying to resolve how to lessen the ‘time to value’ of their data."
“Our overriding objective was to get advanced clinical analytic up and running, and when I began seeing what could be done with the raw information we were sending, I realized that previously we were going about it in the wrong way,” Showalter claims. “We are at the point now where we can tell Jvion we need a new use case with our information, and in 6to 10 weeks we can have a new assumption built into our infrastructure.
“I consider everyone is trying to resolve how to lessen the ‘time to value’ of their data. The data’s not ideal, and it never will be, but with the machine-learning tools and neural networks out there today, it does not have to be ideal to be meaningful. For instance, you might have a sufferer whose blood pressure is recoded as being 1,000, which is impossible, but machine-learning can be taught to avoid that and weigh the other data points about that sufferer for threat. So if it is looking at ten data points to make clusters of sufferers at risk, it will forget about the blood pressure and determine that 9 other pieces of data about that sufferer put them in a particular risk cluster.”
Various contributors have found their clinical analytic planning are hitting a wall because they are not well-connected to the real agents of change in medicine—the caregivers who are now living their days in EHRs and other data systems, all the while being deluged with information and documentation. That disconnect has had a cascade impact on the establishment of clinical analytics, as contributors, unhappy with the value yielded by their analytic attempts, are initiating to reconsider their strategies.
Few are now putting more resources into developing their own analytics systems by establishing algorithms and tools; others are trying out latest technologies to get clinical analytics closer to caregivers. The result, in accordance to Joe Van De Graaff, research director at KLAS Enterprises, is a very distinctive move in the analytics market.
“It is actually a piranha tank right now,” he claims. “We have spoken to various hundred health systems in the past eighteen months, and we estimate that up to 30% of them are looking to replace their population health and analytics products. There is a very powerful requirement to get analytics into workflows, and many agencies are uncertain if their present analytics platforms will be capable to do that.
“A few years ago, there was not a huge concentration on utilizing a core EHR platform for business intelligence and analytics, but that is changed drastically, as organizations have shifted deeper into new care and reimbursement models.”
Chicago-based NorthShore University HealthSystem, for instance, has been building up its stable of homegrown analytics tools for the past some years, and it is learned through trial and mistake that to be effective, analytic tools have to be visible—or more to the point, visual, claims Ari Robicsek, MD, the health system’s vice president of clinical analytics.
“Conventionally, what we would do is send a report out once a month to someone about how their department is operating, but that was not offering analytics for the clinical workflow, which is where it really requires to be,” he states. “What we are concentrating on now is creating visualization dashboards that let them explore the information themselves, and integrating those visualizations right into their workflow.”
NorthShore—containing 4 hospitals and 2,100 affiliated physicians, involving a 900-physician medical group—recently rolled out various applications that are crunching massive, real-time data sets to come up with predictive analyses that enable them to concentrate attention on sufferers at instant and longer-term risk for health issues.
"You are analyzing few health systems with the resources and know-how deciding they can construct clinical tools better than anyone else in the market."
In accordance to John Moore, founder and managing partner at Boston-based healthcare analyst firm Chilmark Research, there is a dearth of vendor products on the market designed for clinical analytics, and certain if any of those are integrated with EHRs and workflow.
“There really are not many good choices out there—EHR vendors are beginning to construct their own clinical analytics into their products, but that is an attempt that is really just getting initiated,” Moore claims. “For that reason, you are analyzing few health systems with the resources and know-how deciding they can construct clinical analytic tools better than anyone else in the market. It is a expression of the maturity of the marketplace for analytic products that use clinical analytic data—mostly what is being provided are standalone products that do not mesh with workflows.”
NorthShore utilizes visualization software from Seattle-based Tableau—the software sits atop its enterprise data warehouse. The health system has established various predictive algorithms and analytic tools, and connected them to its Epic EHRs system to enable case managers and physicians to approach multilayered visualizations and utilize that data on the go.
For case managers, a predictive algorithm on a regular basis observes data flowing into the EHR about hundreds of thousands of sufferers, and offers a dashboard look at the sufferers at greatest risk for, as Robicsek puts it, “bad things happening to them,” be it a hospital readmission, acute psychiatric problem or cardiac issue. From there, case managers can drill down to understand why the sufferers are deemed at greatest risk and decide whether they should be enrolled in a case management plan. The predictive tool also assists the case managers, deployed on all present information; determine what day and time of day is best for making that previous contact with the sufferer. “We have found that it is extremely significant that case managers have a concept what the optimal time is to reach out to a sufferer and make that 1st connection,” Robicsek claims. Once a sufferer is enrolled in a program, the visualization tool tracks his or her growth or lack thereof by mentioning clinically important changes in health status.
NorthShore has also acquired a lot of traction with clinical analytics through links with its EHR while using Tableau’s visualization software. It recently rolled out an application called the My Panel dashboard that enables physicians to click on a button within the electronic health record that takes them to a visualization of how they are performing various different clinical quality metrics for their sufferers. They can also shift to a view that reflects the predictive model data for each of their sufferers and what the threat score is for readmission within thirty days, as well as other potential threats.
Color bars indicate whether they are above or below certain aims, like how their sufferers are controlling their diabetes or hypertension. They can then make lists of sufferers who are not on aim for each metric, and hover the cursor over each individual metric to reflect care gaps and instructions for each. Through another button, a physician can send a straight message to an individual sufferer to tell the sufferer to schedule an appointment, or send messages to other caregivers to order tests and other services. Within that similar dashboard, they can see a map that indicates them where their sufferers live and what kinds of health services are nearby.
The dashboards do not really reside within the EHR; clicking the My Panel dashboard takes consumers to a web page that runs the visualizations. But the experience is seamless to physicians, Robicsek claims, and links embedded in the visualizations take consumers right back into the sections of the electronic health record. “Since those visualizations and predictive tools live within the skin of the electronic health record, physicians feel like they are working in the similar place, and there are no extra steps in their workflow. Since we rolled this out, 100% of our physicians have utilized the My Panel dashboard, and 75% are repeat consumers. In the world of physician technology adoption, anyone will inform you that is absolutely major—and it indicates that no matter how great a predictive algorithm or other clinical analytical tool is, it will not get adopted unless it is right there in that clinical workflow.”
Getting analytics embedded into EHRs and consequently the workflow of contributors is an important element for various health systems, but there is a 2-way street there: Within the EHR itself is a trove of unstructured information that can be used by ever-more sophisticated analytics engines to approach the sufferers’ health risks.
Natural language processing is also a significant technology in the University of Mississippi Medical Center’s attempt to widen the analytics net, claims Showalter. “Getting the clinical ideas out of that information is really a key at the population health level,” he claims. “It flags a lot of the issues that we cannot really recognize with our structured data. For instance, if someone comes in for abdomen pain, the scan might find that it is being due to a kidney stone, but besides the stone there is a six-centimeter dilation of the aorta, which has to be repaired surgically. That is the kind of data that is not put into a field but is clinically significant.”
Showalter claims the NLP platform, from Franklin, Tenn.-based M*Modal, can observe about 98% of the medical center’s unstructured electronic health record information. “The mere information we cannot really bring into our clinical analytics attempt are waveforms. We do not have good use cases on how to manage that data, so I guess that will be our next obstacle to get over.”
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