Data-driven progress: As informatics evolves, clinicians find ways to stay ahead of illness and revamp care delivery

Data-driven progress:

As informatics evolves, clinicians

find ways to stay ahead of illness

and revamp care delivery

Clinicians and data experts, working in concert, find new ways through digital

informatics to turn the abundance of information housed in patients’ medical

records into better care and fewer readmissions. A panel of experts assembled by

PwC explains how.

Technologically versatile computer systems can be tailored to predict when a patient is at risk of a chronic illness, the likelihood he or she may return to the hospital, or even whether a more dire diagnosis looms. Most importantly this technology can help both patients and clinicians make informed decisions, and provide access to actionable information. This ability to anticipate health patterns, known broadly as predictive analytics, holds much promise for a healthcare industry that must demonstrate value in an increasingly competitive environment.

In February, PwC’s Health Industries practice brought together a panel of three experts from the clinical, technical and operational side of healthcare during the Healthcare Information and Management Systems Society (HIMSS) annual conference to discuss and share best practices in medical informatics. While the participants varied in their pace and depth of commitment to informatics, all agreed that the potential of technology to positively impact patient care and coordination is profound.

At the crux of this effort is rich data from electronic health records (EHR) and other sources that allow clinicians to track everything from the social conditions that impact a patient in the home to the way doctors treat them in the hospital or clinic. Clinical informatics is the mining of this data to develop ways to improve care and outcomes. With mounting financial penalties for avoidable hospital readmissions and a shift to outcomes-based payments, improving care and reducing costs have become imperatives for the healthcare industry. Clinicians say the real bonus of better care through tailored technology is that the patient becomes the central focus, which is a hallmark of the consumer-driven New Health Economy.

“I think we’re beginning to get some insight into truly what’s driving health and disease,”

Cynthia Burghard, research director for accountable care IT strategies at IDC Health Insight, said during an interview with PwC’s Health Research Institute (HRI). “And we’re able to create strategies around that.” Burghard said the use of analytics has evolved. Instead of being reactive, she explains, the industry is capturing more data in real-time and can begin to anticipate when care is necessary.

Creating total awareness about a patient and integrating that view into deep clinical knowledge around the latest in medical innovations is central to any attempt to accurately forecast and prevent adverse health conditions. “We’re growing beyond just reporting on facts,” Burghard said. “We’re realizing that where a person lives, their financial status and a number of other psychosocial factors have as much to do with someone getting an illness as medication management.”

Many of those factors can now be tracked by a variety of new consumer products, such as wearable wristbands that count how many steps are taken each day, or through smartphone devices that monitor pulse rate. The end result is a more health-conscious consumer who can monitor their own health measures in real-time.

Data-driven progress

2

Informatics: Where caregivers, patients

and technology intersect

Burghard is not alone in her assessment. The HIMSS panelists spoke about the innovative ways of using medical data to both diagnose and treat patients. Some call it “big data meets big aspirations,” and the end result is often better-coordinated care and fewer unexpected return hospital visits. The three health systems that participated—Bon Secours Medical Group, Kaiser Permanente and the research-minded Parkland Center for Clinical Innovation—agreed that successful advanced informatics programs are rooted in

new technologies operated by competent clinicians.

Four years ago, Bon Secours Medical Group first adopted the concept of patient-centered medical homes as its framework to reduce readmissions and to better coordinate patient care. The model hinges on the use of registered nurses, dubbed “nurse navigators,” to coordinate services for patients as they move from hospital to home. The program is designed to help patients coordinate visits with a host of doctors and clinicians. What Bon Secours found isn’t all that surprising: once patients leave the hospital, they tend to forget the advice provided by the care team or fall back on familiar patterns, such as spotty adherence to medications. Hospitals often struggle to provide clear instructions to the patients when they are sent home. Too much information can overwhelm,

too little may create gaps in care. Nurse navigators help bridge those gaps, connecting patients to clinicians while addressing some of the more routine medical concerns on their own. For Bon Secours, navigating patients through the care system has been helped tremendously by technology. Bon Secours uses an electronic medical record that is customized to prompt clinicians to follow evidence-based medical procedures when treating chronically ill patients. The system links to broader disease registries that include best practices for treating diabetics or those with heart disease.

By using staff-developed algorithms and formulas, Bon Secours can determine whether a patient has a high, medium or low risk of being readmitted to the hospital. Some factors are obvious—especially if a patient has advanced illnesses and a history of return visits. But other indicators may not appear in standard patient records. For instance, Bon Secours has configured its EMR system to ask about a patient’s insurance status and where they live—socio-economic factors that also can be used to forecast future health patterns. Nurse navigators then determine the appropriate level of follow-up care. Since implementing the model across about a dozen of its Virginia locations, Bon Secours has seen its

In its Virginia hospitals,

Bon Secours lowered its

unplanned readmissions to

where it only had one across

all-cause readmissions drop to under 2%, said panelist Lu Bowman, an RN and clinical supervisor for the health system. Nationally, data collected by the Centers for Medicare and Medicaid Services (CMS) show that about one in five Medicare patients is readmitted within 30 days of a hospital stay. What’s more, Bon Secours saw a $6 million return on its investment in

the informatics systems, roughly $3 for every $1 it spent getting the program up and running.

“It’s been very exciting,” Bowman said. “When we sat down and really started looking at it, and seeing all the ways we’re impacting outcomes and revenue for the system, we got some pretty good numbers.”

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4

Pace of adoption varies

The panelists agreed that some health systems advance faster than others, especially in reducing hospital readmissions. For more proof, look no further than Medicare’s Hospital Readmissions Reduction Program, an Affordable Care Act program now in its second year. Already, more than 2,200 hospitals of the roughly 3,500 across the US paid the federal penalty, totaling more than $280 million. CMS expects more penalties this year.1

The penalties underscore the challenges of using data analytics effectively. A 2011

survey by HRI found that just 13% of providers believe they have been able to impact patient behavior through their informatics program.2 _{That may} be because informatics efforts are still relatively new across the industry and adoption is slow due to the number of competing projects that vie for attention and money. At Kaiser Permanente, an initiative to engage and educate patients with laptops and tablets is still in the early stages and has not yet yielded results. Nationally, however, there are strong signals that the use of predictive

analytics is working to keep people healthy. Carilion Health System in Southwest Virginia used its EMR and analytics systems to identify about 8,500 patients who were at risk of heart disease within a year’s time. About 3,500 were discovered with a program that scours doctors’ notes for clues to a patient’s health that don’t always make it into the medical record, such as the number of cigarettes a person smokes a day. Other systems, such as Seton Healthcare Family in Central Texas, use a similar model to analyze data around congestive heart failure.3

“What they found was that it was alcohol consumption, drug abuse and living situations that were more of the drivers of readmissions than any clinical condition,” IDC’s Burghard said. But such results can take time. For instance, Blue Cross Blue Shield of Massachusetts started its Alternative Quality Contract (AQC) in 2009. Under the program, physicians receive a fixed budget—or “global payment”— for patient care. The payment model encourages doctors to improve the health and wellness of an entire group of patients. Burghard, who tracks the program, said the initial savings were found “in the low-hanging fruit” of preventing emergency room visits and readmissions. Only now, she said, are physicians beginning to see deeper health changes in patients, such as lower blood pressure and cholesterol counts.

“You’re beginning to see some improvements around the outcomes of care,” she said of the program. “But it took a long time.” The results are nevertheless promising. To punctuate that point, Burghard cited growth in the number of software vendors who count predictive analytics programs as part of their services. Panelist Gary Ahwah, the chief information officer for Kaiser Permanente’s Northwest region, has collected and analyzed medical trends since the earliest EMR systems emerged.

“We’ve been on this journey a long time,” Ahwah told the group, adding that Kaiser Permanente saw readmission rates climb in 2008— a troubling indicator for a system routinely viewed as one of the best. The spike in readmissions served as a wake-up call.

Now, Kaiser Permanente uses specific algorithms that, when triggered, prompt clinicians to either ask more questions or follow a particular care plan. Those metrics also help “stratify” patients based on illness or who’s more at risk of a readmission. “Primarily we have been using that as a way to determine who is the most sick versus the not so sick,” Ahwah said. “We’re getting to some level of sophistication using the true analytics piece of it.”

Kaiser Permanente focused on workflow—the process that enables an EMR to prompt clinicians to enter specific data, which is then used to draw a fuller picture of a patient’s health. Patients are part of the effort too as information is presented to them as well.

With more of a handle on care coordination, the next step in Kaiser Permanente’s informatics evolution is focused on keeping patients at home. “We interact with patients in the home, via video, talking with a doctor. In a number of cases, we have prevented readmissions,” Ahwah said.

1. Kaiser Health News, “Armed with Bigger Fines, Medicare to Punish 2,225 Hospitals for Excess Readmissions,” August, 2, 2014, http://www. kaiserhealthnews.org/Stories/2013/August/02/ readmission-penalties-medicare-hospitals-year-two. aspx

2. PwC Health Research Institute, “Needles in a haystack: Seeking knowledge with clinical informatics,” 2011, http://pwchealth.com/cgi-local/hregister.cgi?link=reg/ needles-in-a-haystack.pdf

3. Forbes, “IBM and EPIC Apply Predictive Analytics to Electronic Health Records,” February, 19, 2014, http:// www.forbes.com/sites/zinamoukheiber/2014/02/19/ ibm-and-epic-apply-predictive-analytics-to-electronic-health-records/

Kaiser Permanente uses specific algorithms that, when triggered, prompt clinicians to either

ask more questions or follow a particular care plan. Those metrics also help “stratify” patients

based on illness or who’s more at risk of a readmission.

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6

Organizational challenges give way

to better outcomes

The challenging path to lowered readmissions and improved care started in 2010 at Bon Secours. Physicians were unclear about the role of nurse navigators, the nurses themselves were uncertain about their fit in the care coordination model—and neither group understood how it would all piece together. Other panelists agreed that disconnects are common at the beginning.

Since the Bon Secours program bridges inpatient hospitals with outpatient doctors’ offices and clinics, staffs on both ends were confused, Bon Secours’ Bowman said. “Working on relationships with the hospital can be pretty challenging,” she added. “Traditionally the care of the patient has primarily happened in the acute setting. But we’re moving into a new world where a lot of the care will be happening in the community.”

At first, she said, doctors were hesitant to have nurse navigators involved. “Now they can’t do without them,” she added. “The hospitalists understand what it means when they get a call from a nurse navigator.”

Turf battles aside, the nurse navigators serve as an important extension to the care physicians provide. “When we focus back on the patient, it really all makes perfect sense,” Bowman said. “We always try to go back to that.”

“The technology has to be submissive to the clinical workflows, and

it’s important that providers, nurses and other folks feel as though

there’s been no distraction or time taken away from their activities.”

—Ruben Amarasingham

Few have more experience in the field of predictive analytics than Ruben Amarasingham, a physician who leads the Parkland Center for Clinical Innovation, which builds data mining predictive modeling and surveillance solutions. During the panel discussion he described the clinical analysis effort as one “that takes a village.” Multiple clinicians are often involved, he added, meaning they all need to be versed on the use of specialized technologies. “Clearly the clinical integration is critical,” Amarasingham said. “The technology has to be submissive to the clinical workflows, and it’s important that providers, nurses and other folks feel as though there’s been no distraction or time taken away from their activities.”

One size does not fit all: Build flexibility into your informatics program

Amarasingham offered five on-the-ground predictive modeling lessons: 1 Tailor an informatics program based on hospital type.

Patients differ, as do the hospitals that treat them. Yet many health systems base their predictive modeling on a one-size-fits-all approach. “You can get more accurate prediction and risk stratification if you build a model configured to your institution,” Amarasingham said. For instance, public or inner city hospitals may see patients who are far more impacted by social factors than those treated at private or suburban hospitals. Modeling readmission risk so that it aligns with what is happening in a community improves the predictive power.

2 Hospitals and health systems change over time. So should predictive modeling tools.

Patient volumes will change. The proportion of certain diseases will change. Patients will change. “We think it’s important for predictive models to adapt and change over time, because a hospital in 2013 may not be the same hospital in 2014,” Amarasingham said.

3 When possible, predictive models should rely on real-time data.

Clinical data pulled within 24 hours of admission gives medical staff the entire length of the hospitalization to prepare and plan for what happens after the patient is discharged. This includes physician notes, physiological data, labs, vital signs and a range of other metrics captured when a patient is admitted. “That gives you a great capability to have a really high model performance in the first 24 hours,” Amarasingham said.

4 Risk prediction models should be based on a combination of disease conditions.

Disease conditions vary, and it’s rare that a patient has only one. Typically, chronically ill patients have multiple health problems. The use of a system based on “natural language processing,” allows for software to decipher physician notes, pulling tidbits of information that may otherwise go ignored. The computer system can detect the primary diagnosis even if there are many.

5 Prepare for even more sophisticated data analysis.

Predictive models have moved from claims-based data to clinical information that can be analyzed in near real-time. The next wave, Amarasingham said, will involve assessments of risk based on social factors, and beyond that, data aggregated from consumer

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8

Conclusion

Despite varying levels of readiness, commitment and achievement, the healthcare sector understands the value in analyzing even the minutest levels of information that can be gleaned from a patient’s record. Armed with constantly evolving clinical research and knowledge as well as new customizable technology, clinicians are able to consume information from medical records so they can predict which patients may be sicker than others, and which ones are likely to return to the hospital. Knowing and acting on both those factors is central to any health system’s commitment to improve care coordination and drive down unnecessary readmissions.

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Acknowledgments

HRI Regulatory Center

HRI Advisory team

Richard Judy

Principal, Health Industries Advisory 415 498 6218

[email protected]

Kulleni Gebreyes

Director, Health Industries Advisory 703 918 6676

[email protected]

Nikki Parham

Principal, Health Industries Advisory 214 756 1815

[email protected]

Marketing

Todd Hall, Nadia Leather, Art Karacsony, Hindy Shaman

Health Research Institute

Partner

Health Industries Leader 214 754 5172

[email protected] Daniel Garrett

Principal & Leader of PwC’s Healthcare IT Practice (HIT)

267 330 8202

[email protected] Ceci Connolly

HRI Managing Director 202 312 7910 [email protected] Trine Tsouderos Director 312 298 3038 [email protected] Sarah Haflett Senior Manager 267 330 1654 [email protected]