population health management (PHM) requires validated data.

Without data integrity, it is difficult for care teams to know which patient care gaps exist and where the oppor-tunities for quality improvement lie. Validated data are also essential for risk stratification, care management, quality measurement, and scalabil-ity of population health across an organization.

Recent studies show that the data in electronic health records do not always provide an adequate founda-tion for PHM. For example, the percentage of completeness of EHR problem lists in one study ranged from 4.7% for renal insufficiency or failure to 50.7% for hypertension, to

61.9% for diabetes, to a maximum of 78.5% for breast cancer.1 Another study of EHR-derived quality data in primary care practices found that preventive services had been system-atically undercounted.2

Without data integrity, it is dif-

ficult for care teams to know

which patient care gaps exist.

In most cases, the missing clinical information was documented somewhere in the record, but often it did not appear in discrete fields, where it could easily be used for quality measurement or to build the

registries used in population health management.

Many healthcare organizations that are moving toward PHM have just started to abstract raw EHR data into registries. Often, these provider groups are populating registries with scheduling and billing data from their practice management systems. These administrative data can help organizations assess quality of care and identify individual care gaps, but it can’t be used to generate the analysis needed for PHM.

Group practices engaged in PHM initiatives are well aware of these data integrity issues and are devising solutions that will enable them to move forward. Other groups can learn from the experience of practices that are in the vanguard of this new healthcare delivery model.

Limitations of Administrative DataIn the initial phase of PHM,

practices may use their billing data to create registries that help them identify and reach out to patients with gaps in care. When combined with automated outbound messag-ing, this approach has proved to be effective in encouraging patients to make appointments for needed preventive and chronic care.3

But, billing data has some inherent limitations. For example, it cannot be used to identify certain subpopulations, such as patients with elevated blood pressure or higher than normal HbA1c values. In addition, physicians do not always bill for every service they perform. Poor charge capture reduces not only revenue but also

Scaling Population Health Management Starts with DataBy Karen HandmaKer, m.P.P.

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the value of administrative data for PHM.

For example, Bon Secours Richmond Health System in Richmond, Virginia uses billing data to feed a registry that triggers outbound messages to patients based on clinical protocols. Robert Fortini, vice president and chief clinical officer of Bon Secours Physician Group, says that physician miscoding occasion-ally results in patients being called unnecessarily. If a patient came in for an acute problem and the doctor also treated a chronic condition but did not code that diagnosis, the patient might later receive a call suggesting he or she should make an appoint-ment. Then, when the patient returns, the physician will discover that the problem has already been treated.

When something like this occurs, Fortini suggests that the physician meet with a certified coder to review coding practices.

Using Registries to Support PHMSome practices are starting to

leverage EHR data as the basis for population-wide registries. These can be utilized not only for proactive patient outreach but also for patient risk stratification, alerts to providers and care managers, quality measure-ment for payers, and performance evaluation at both the individual and group levels.

A registry system provides dedicated functions that can collate clinical and administrative informa-tion across care settings, normalize it, and provide the insights required for PHM. Registries improve the quality of data by performing the difficult work of extracting, mapping, clean-ing, and validating them through a multi-staged process, yielding reliable information required for quality reporting.

Donald Simila, M.S.W., FACHE, CEO/CFO of the Upper Great Lakes Family Health Center in Marquette, Michigan, is enthusiastic about the possibilities of applying a PHM analytics program to the center’s data. When this application

is fully rolled out to the group, he says, “it will give us a tremendous opportunity to elevate the level of service to our population” by identifying subsets of patients with particular needs.

For example, he notes, he and his colleagues want to know how many diabetic patients are included on their panel, how many are well controlled, and how many have an HbA1c of 8 or above. “This is going to give us an opportunity to be aggressive about those subsets of patients, including those with COPD or hypertension. We’ll be able to identify all the patients who are hypertensive, all who are well controlled, and all who are not.”

Challenges of Externally Generated DataBut to do that, the group will need

accurate and complete data on every patient. For example, Simila notes, Marquette’s physician group has a robust interface between its EHR and the main hospital lab. Consequently, lab results can flow back into the correct EHR fields with virtually no errors. Outlying clinics, however, have to send tests to local labs that are not connected to the EHR. They fax the results to the practices, which must scan them into the system as documents. Since these data are not structured and manual data entry would be too labor-intensive and error-prone, Marquette is building interfaces to community hospital labs.

Similarly, many practices have dis-covered that it is difficult to measure their performance on screenings such as mammograms, because the facili-ties that perform those tests rarely have interfaces with their EHRs. Consequently, the practices receive mammography results as faxes. Their staffs scan these into the system but don’t always enter the data into discrete fields in the EHR.

Improving Data Quality and ConsistencyUntil health information

exchanges help solve these kinds of issues, externally generated data will continue to present challenges to

groups seeking to implement PHM. But there is plenty they can do now to improve data quality, which will allow them to create scalable popula-tion health management processes.

Attribute Data to the Right ProvidersIt is essential that the name of

a patient’s primary care physician be designated appropriately. One reason is that Medicare’s Meaningful Use incentive program, accountable care organizations and health plans’ pay-for-performance programs link metrics and quality scores to par-ticular physicians. If the individual isn’t identified as a particular doctor’s patient, that physician won’t receive credit for services rendered.

Correct data are also vital for follow-up on required care. If a phy-sician receives a list of patients with care gaps and some of those patients don’t belong on the list, he or she may not finish reading the report. Physician distrust of reporting could undermine the viability of a group’s entire PHM initiative.

In some cases when a patient hasn’t selected a primary care doctor, practices can assign the person to a nurse practitioner or specialist who has provided primary care.

However, confusion can result. Wynn Hazen, chief operating officer of the Jackson Health Network in Jackson, Michigan, says these kinds of errors have created problems downstream: “Sometimes, a diabetic patient would go see an endocri-nologist who also practiced general internal medicine, and the patient might have appeared on the wrong list. Or if the patient went to an ob/gyn and had a mammogram, she might have appeared on the wrong list, and a communication to the patient might have come from the wrong practice.”

One way to prevent incorrect provider attribution is to have schedulers ask patients for the name of their primary care physician every time they call for an appointment, or to validate the primary care provider within their personal health record.

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It’s an extra step in the work process, but it can avoid considerable rework down the line.

Explain Numerators and Denominators

Especially in practices that are fairly new to EHRs, there may be variability in the percentage of data that is entered into the proper fields or entered as structured data. As a result, practices and individual physicians may receive lower quality scores than they should.

Management should educate staff on how quality measures are constructed, pointing out that the numerators and denominators of quality data are equally important. By entering all relevant diagnoses and documenting what was done for each patient as discrete data, physi-cians can help ensure their quality scores fairly reflect their performance and that they have the data they need to achieve Meaningful Use and other quality initiative goals.

Standardize Data EntryBon Secours has designed a

process to encourage its physicians to standardize data input within the EHR. As part of its effort to have physicians attest to Meaningful Use, the organization distributes reports to every practice on the extent to which providers are entering discrete data related to the Meaningful Use metrics. “It’s a way for us to identify which practices may be slipping and need a little optimization training,” Fortini says.

The Jackson Health Network, which includes Allegiance Health-owned practices and independent community physicians, is using another method to clean up its EHR data as it moves further into PHM. The clinically integrated network is focusing on the problem of standardizing data inputs so that everyone enters the same data in the same fields of the community EHR, known as the Jackson Community Medical Record ( JCMR).

Hazen attributes the data issues his organization has encountered to a lack of standardization: “Providers and care teams document differently in the medical record. We have to make sure that when you’re record-ing the last time a patient had a colonoscopy that the data appear on the right screen in the right place in the record. Flexible documentation options within the EHR can result in recording that the test was done but then not putting in the date it was done, or putting the information in the surgical area rather than the health maintenance screen.”

As Jackson Health Network pilots the use of an EHR-based registry and a new analytics program it has adopted, it is using its new reports to spot inconsistencies in data entry and correct them. The analytics software has enabled the network to implement several levels of data integrity and mapping to improving the end result—and the robustness of quality reporting. In the mean-time, the organization is distributing information to practices about how information is supposed to be entered in the EHR.

Share Reports with StaffTo validate the EHR data, Hazen

says, the clinicians involved in Jackson’s PHM project are doing a “deep dive” into the record to make sure that all the right data on the right patients is in the right place. In addition, they’re showing chronic disease patient lists to physicians at the pilot sites to make sure that the correct patients are included in the reports and that no one who should be there has been overlooked.

Registry-generated reports should be shared not only with physi-cians but also with other care team members. More often than not, the staff understands what the issues are and where they came from. So show them the errors in reports and tell them, “We’re not judging you on this, but we need your help to fix it.”

After the EHR data have been validated, quality assurance com-mittees can use them to monitor data-related aspects of quality improvement. For example, they can utilize the reports to spot-check the integrity of structured data entry, the training of new hires, and the retraining of staff when new pro-cesses are introduced.

ConclusionIn a recent study of how safety

net clinics used information technol-ogy to improve diabetes care, the researchers noted that physician practices tend to underestimate the time it takes to learn how to use EHRs and registries for qual-ity improvement.4 Therefore, they said, it is a mistake to rush into Meaningful Use. The same might be said of PHM, which is closely aligned with Meaningful Use.

Even after a practice has validated its data and begun learning how to use them in PHM, the group cannot rest on its laurels. Data integrity is not a “one and done” process; it requires constant vigilance, spot checks of e-charts, and process changes as problems or inconsisten-cies are discovered.

Lean principles should be applied wherever possible to prevent data entry errors or omissions. Prevention may require staff to do more up front so they can avoid problems that will be harder to fix down the road. If managers notice people are doing workarounds, they should analyze why they are occurring and revise the process as necessary.

Based on the experience of physi-cian groups that are heading down the path of PHM, it is clear that comprehensive, clean, structured data are indispensable to this approach. Such data are not easy to produce, even if a practice has successfully implemented a leading EHR. But groups that focus on data integrity have the best chance of using PHM to improve patient care.

Continued on page 48

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References1. Adam Wright, Justine Pang, Joshua C.

Feblowitz, et al. 2012. Improving Com-pleteness of Electronic Problem Lists through Clinical Decision Support. Journal of the American Medical Informatics As-sociation, published online January 3, 2012. Accessed February 21, 2012 at http://jamia.bmj.com/content/early/2012/01/03/amiajnl-2011-000521.full?sid=a7d2da2d-9922-484a-83a3-a599ceb71bdb.

2. Amanda Parsons, Colleen McCullough, Jason Wang, and Sarah Shih. 2012. Valid-ity of Eelectronic Health Record-derived Quality Measurement for Performance Monitoring. Journal of the American Medi-cal Informatics Association, published online January 16, 2012. Accessed February 21, 2012 at http://jamia.bmj.com/content/early/2012/02/08/amiajnl-2011-000557.full?sid=c0e8768f-7e6a-43fb-9f05-4f04e59e3835.

3. Ashok Rai, Paul Prichard, Richard Ho-dach, and Ted Courtemanche. 2011. Using Physician-Led Automated Communica-tions to Improve Patient Health. Journal of Population Health Management, 14(4): 175-180.

4. Jane Taylor and Susanne Salem-Schatz. 2010. Harvest of Lessons Learned from Accelerating Quality Improvement Through Collaboration. Report prepared for Califor-nia Healthcare Foundation: 6. Accessed April 2, 2012 at http://www.chcf.org/~/media/MEDIA%20LIBRARY%20Files/PDF/A/PDF%20AQICLessonsLearned.pdf.

Karen Handmaker, M.P.P., is director of population health management services for Phytel.

States with Australia, Canada, Germany, the Netherlands, New Zealand, and the United Kingdom, our system ranks last or next-to-last on quality, access, efficiency, equity, and healthy lives.3

Our patients and communities are asking for—and deserve— access to well-coordinated care that is less costly than current models. Clinical integration is a powerful mechanism to achieve this goal. A partnership between physicians and hospitals that focuses on achieving clinical integration is the best way to provide continuing and more efficient services. With everyone on the same team, we can redefine the current ineffective, poorly coordinated and unnecessarily costly U.S. healthcare system.

References1. Kenneth E. Thorpe, Lynda L. Ogden,

and Katya Galactionova. 2010. Chronic Conditions Account for Rise in Medicare Spending from 1987 to 2006. Health Af-fairs, 29: 4718-4724.

2. CMS Office of the Actuary Report, 2010. Estimated Financial Effects of the “Patient Protection and Affordable Care Act,” as Amended. Accessed February 20, 2012 at https://www.cms.gov/ActuarialStudies/Downloads/PPACA_2010-04-22.pdf.

3. Karen Davis, Ph.D., Cathy Schoen, M.S., and Kristof Stremikis, M.P.P. 2010. Mirror, Mirror on the Wall: How the Performance of the U.S. Health Care System Compares Internationally, 2010 Update. The Com-monwealth Fund, June 23, 1010.

Jeff Wasserman is vice president of strategy and executive leadership services at Culbert Health Solutions, a professional services firm serving healthcare organizations in the areas of operations management, revenue cycle, clinical transformation, and informa-tion technology.

Scaling Population Health Management

Continued from page 40Making the Case for Clinical Integration

Continued from page 47