(Editor’s Note: this column is taken from Health Data Management’s Health Intelligence
Supplement, February 9, 2012)

The first article in the supplement is entitled, How Reporting, Analytics and Metrics Affect Outcomes and Save Lives Greg Gillespie -- Showing them what they do; not what they think they do, Gillespie states:

“The U.S. health care industry doesn’t lack for brainpower. The medical profession is a beacon to the best and brightest among us who have answered the call to use their healing skills to make the ill healthy again. But the heartbreaking fact is that even with all those great minds applying themselves to providing the best care, many patients still die needlessly and many with chronic pain suffer endlessly. one reason for the needless suffering is a lack of health intelligence, the reporting, analytics and performance metrics that can identify where and why very smart people end up delivering care that is insufficient or possibly sets patients on a dangerous course.  This supplement spotlights multiple angles of the measurement, reporting and performance analytics that move decision support and proper intervention to the true business end of the health care industry€”where the patient meets the caregiver.”

Following this, the article about SETMA appears. Entitled, A Portrait of Health, Jim Ericson discusses how, “Clusters of data points and statistical analysis give doctors at a Texas practice a comprehensive EMR view of patients.”  His article follows.

“The debate over escalating medical costs has been attributed to a variety of ills: overly comprehensive or redundant testing and care, fraud, waste and various other malignancies. 
“None are the singular cause of the jaw-dropping financial inefficiencies of the health care industry. But together they create a cost curve that must be bent downward, and to do so requires starting with the low-hanging fruit where, without decreasing quality or access, caregivers can still decrease cost.

“Hospital readmission is plainly one of those areas. Especially in an era of aging patients where chronic incurable conditions like diabetes or heart disease are profligate, the best case for a patient is to stabilize their condition, keep them out of the hospital and allow them to live as comfortably as they can.

“Cost, safety, collaboration, convenience are all reasons why we want to control readmissions,” says Dr. James Holly, M.D., CEO of Southeast Texas Medical Associates. Reducing readmission became a central goal at SEMTA, and through the use of electronic records and analytics, the 29-physician practice in Beaumont achieved a 22 percent improvement over six months. 

Clusters of Data

“Holly sees many geriatric patients who might have seven or eight ongoing conditions. The success in treating each one is gauged by a set of outcome metrics by which the level of care and the patient’s response can be compared with standards derived in medical research. 

“The American Medical Association’s Physician Consortium for Performance Improvement (PCPI) is one such source of a quality metric set covering various conditions that allows a provider to measure their own performance at the point of care. Other sources for quality metric sets include the National Committee for Quality Assurance (NCQA) and the Ambulatory Quality Association (AQA).

“Where process metrics measure the degree to which a clinical best practice was followed, outcome metrics are harder to quantify, because outcomes are judged by an assortment of health and quality of life measures, many of which are subjective. These include ‘hard’ metrics

“We discovered that if you’re only tracking one quality metric about a particular condition, it’s really not going to change anything,” says Holly. “But if you’re tracking a cluster of seven or eight quality metrics about a specific condition and you hit the metric on those, you’re very likely going to be changing the outcome for the patient’s health.”

“Across whatever range of conditions a typical patient might have, SEMTA physicians might assemble as many as 50 or 60 quality metrics, a galaxy of multiple clusters. If a patient’s progress can be managed across that many metrics, evidence shows it will change the trajectory and outcomes of that patient's care. 

Data Sets and Deviations

“Doctors using medical records to manage patient outcomes have traditionally had to look back at what happened 12 or 18 months earlier to determine their success. Getting ahead of that time curve to address the current health of a patient calls for an analytical approach that suggests actions to take today or tomorrow.

“Tracking the course of multiple conditions in a single patient requires tools that mine data for different themes, modes, medians and standard deviations. Holly is a big proponent of statistical analysis and using standard deviation to calculate patients’ - and providers’ variance from mean performance - based on statistical evidence. Southeast Texas’ standard deviation for diabetes fell from 1.98 in 2000 to 1.2 in 2010, for example. Statistically, Holly says, that took the practice “from terrible to much better, not perfect but closer to the .9 deviation you might expect in human biology.”

“It’s a relative measure, but that’s the point, Holly has found. ‘There's a thing in health care called clinical inertia where a patient comes in, they are not at their care goals, their blood pressure or lipids are not treated as well as they could be, and yet nothing is done.’ Research suggests that clinical inertia, defined as lack of treatment intensification in a patient not at evidence-based goals for care, is frequently a cause for preventable errors. 

“To counter widespread clinical inertia, Holly and SETMA publicly reports its own doctors' and nurse practitioners' performance for different disorders. The data is posted by populations and not by patient name (though patients are given the information on their own quality of care at every visit).

“When such information is posted, Holly says, ‘The first complaint you hear from providers is, ‘the data is wrong, I do a better job than that.’ The need to back reports with defensible data led to an ‘exhaustive” business intelligence implementation that involved Cognos software and IBM business partner LPA.  For doctors to buy in, the methodology for auditing and analysis had to be bulletproof as well. 

Southeast Texas analysts initially authenticate data with chart reviews and hand checking to confirm the correlation, which Holly says became ‘spot-on excellent’ for the purpose. ‘Once providers understand the data is accurate and is being publicly reported, it really stimulates them to pay attention to each individual patient. We spent a lot of money and time adapting Cognos to health care to get to that level of accuracy, but when you take care of each individual you'll be taking care of a panel or population in the process that supports that.’  

“When deal with hospital readmissions, different patient populations were compared with those who came back to the clinic as scheduled or not, and whether (and how) that led to readmission. SETMA’s analytics looked at co-morbidities, secondary or associated symptoms patients had and whether that affected their return. Readmission was checked by demographics of ethnicity, gender, age, insurance or lack thereof. Analysts then drilled down to look for small process ‘levers’ or indicators in health care standards and practices that might suggest a minor intervention that would reduce the likelihood of readmission.

“One lever for reducing readmission is in a patient’s transfer from hospital to outpatient care. Since 1998 when SETMA began collecting electronic medical records, SETMA has used its EMRs to build comprehensive data profiles of patients in hospitals, clinics and other settings. The records contain dozens of discrete fields of data in one large database that can be examined at once with analytical tools. 

“SETMA compared individual patient’s admitted to the hospital with a set of 14 data points and four actions published by PCPI, the ‘things that needed to be done’ to define quality care in care transitions from the inpatient to the outpatient setting.. Back in 2007, Holly realized the practice was already monitoring all but one of the PCPI Care Transitions data points, so capability was added in the EHR to store all that data already collected in order to analyze it.

“We put it together with other data points regarding clinical and transitional care and came up with some very interesting analyses of the principle reasons why people get readmitted to the hospital and then make some interventions.”

“Those interventions were where the 22 percent decrease in preventable readmissions were realized. The individual patient data points and best practices lined up statistically in areas of contact with the patient, not only physical contact but through home health, hospice, physical therapy and phone calls, supported by the data clusters and indicators for each patient’s symptoms. 

Counterintuitive Care

“Some of the findings were obvious but were only prioritized once the data was exposed. An indirect cause of readmission for diseases like diabetes is seasonality where patients often lose control around the holiday social calendar. Starting in 2009 patients, including those who’d been seasonally readmitted were contacted before the holidays and asked to sign a contract and redouble their efforts to maintain their nutrition and keep their appointments. In February, 2011, SEMTA analysts looked at the 2010 data and saw they had ‘totally’ eliminated that problem, Holly says. ‘With the evidence, we know data, information and a plan can change behavior and benefit the patient.’

“Other findings were less intuitive. Business intelligence and data mining give the practice the ability to look at trending across variables of age, ethnicity and income demographics. One finding across the practice was that the elderly were showing better than expected results that indicated superb care for their diabetes. It occurred to Holly that instead of treatment, it might be that some elderly were chronically malnourished, which would positively affect their diabetes but negatively affect their overall health. 

“What the data helps you do is not overlook or neglect things which, in combination, can really affect outcomes and things like readmission,” Holly says. What follows the finding is a new level of attention to the patient outside the hospital that is usually neglected.

“The day after a patient leaves the hospital they receive a 12-to-30 minute telephone call, not a check off, but a coaching call with data in the EMR and a chat about medications, reactions and symptoms. Most SETMA patients will have six or seven medication reconciliations in a year in a single patient. Holly says up to 70 percent of readmissions can be tied to medication, and the closer, more frequent the contact, the lower the infirmity and readmission rate.

“The American Medical Association is conducting an analysis of SETMA’s program, the first to have implemented the PCPI quality metric set on care transitions. 
“Holly says the richness of the EHR data gives doctors a portrait, rather than a mere silhouette of a patient’s health, with granularity and precision. ‘A portrait gives you detail, skin tone, texture, hair and eyes. If you have that kind of a medical record, no matter where you see the patient you have a great deal of data that adds up to much more than dots on a matrix.’

“It's a fascinating way to practice medicine that doesn't mean doctors have become geeks, or insensitive to human concerns, Holly says. ‘We know we're dealing with real live people who are dear and precious to others. But with analytics we can know what in the world we are doing, and we can design interventions that will make a difference for everybody and not just the ones who have an easier time of staying well.’”