1. Population Health
Identifying Risk and Segmenting Populations: Predictive Analytics for Population Health
Welcome to Identifying Risk and Segmenting Populations: Predictive Analytics for Population Health. This is lecture c.
In this lecture, we will continue to explore the domain of risk segmentation and predictive analytics in the population health context. We will offer a case study of how one tool, the Johns Hopkins ACG System, is applied within the Johns Hopkins Medical Center's own health plan and population health program. We will also talk about the future of the field, including the opportunities offered by the rapidly expanding electronic health record systems now found within most organizations.
Lecture c
This material (Comp 21 Unit 6) was developed by Johns Hopkins University, funded by the Department of Health and Human Services, Office of the National Coordinator for Health Information Technology under Award Number 90WT0005.
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2. Identifying Risk and Segmenting Populations: Predictive Analytics for Population Health Learning Objectives — Lecture c
Discuss a case study of how one common risk segmentation/case finding method has been applied to population health.
Examine the role of various electronic data sources in risk identification/segmentation.
Identify and discuss the developing frontiers in the population-based predictive modeling field.
The objectives for this lecture are to:
Discuss a case study of how one common risk segmentation/case finding method has been applied to population health.
Examine the role of various electronic data sources in risk identification/segmentation.
Identify and discuss the developing frontiers in the population-based predictive modeling field.
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3. Johns Hopkins Medicine
To begin, we’ll examine a case study within the Johns Hopkins academic medical center. The Johns Hopkins Health Care organization (JHHC) is a population-based health plan that receives a capitation rate for government and private organizations to care for large groups of enrollees. This managed care organization (MCO) uses the Johns Hopkins Adjusted Clinical Groups (ACG) System tool. In this lecture, we will offer a case study of how ACGs and other risk measures are used to help manage the care of the enrolled population for which JHHC is responsible.
This slides presents an overview of the Johns Hopkins Health Care's organizational structure, with further details regarding the population-oriented care management services it provides.
6.24 Figure. Jonathan Weiner and Center for Teaching and Learning, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, 2016.
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4. Population Health Database at JHHC
This chart depicts the flow of data for the cohort of individuals who are enrolled at JHHC. It outlines the flow of data: taking claims data; lab and Electronic Medical Record (EMR) data; various ACG information, including the specific Expanded Diagnosis Clusters (EDC) diagnostic components and risk scores. Each of these inputs may be collected on a different time frame, in some cases, weekly, monthly or annually.
The organization has developed data warehouses for monitoring individuals and then sharing reports for various types of users. Users might be an individual case manager, a medical director who is monitoring the entire system, or it could be the patient's personal clinician at his patient-centered medical home.
6.25 Figure. Jonathan Weiner and Center for Teaching and Learning, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, 2016.
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5. A Predictive Model for Stratifying Population of Persons with Diabetes
Here is Johns Hopkins Health Care's version of the pyramid, but in this case it's in reference to people who have diabetes. As alluded to before, we are not just looking at that single condition but also understanding the complexity of all conditions persons with this disease may have. JHHC constructs the various risk segments based on the ACG score, which reflects both the severity of the diabetes and the range and seriousness of other comorbidities. JHHC uses the patient's hemoglobin A1C derived from the EHR or lab data system. The clinicians among you understand that that’s a very important marker for blood sugar level over a period of time. Based on this segmentation, the Johns Hopkins population management nursing team is able to select those persons in greatest need of outreach or other interventions.
6.26 Figure. Jonathan Weiner and Center for Teaching and Learning, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, 2016.
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6. Total JHHC Diabetes Population = $139
Total JHHC Diabetes Population = $139.5 Million or $950 per Member per Month (PMPM)
As shown from the previous Medicare analysis, the local situation is quite similar for the denominator population (those patients who are capitated with the Johns Hopkins MCO). That is, the highest risk individuals use a large amount of services. Here, a total of 139 million dollars of cost is expended by a relatively small group: 10,500 individuals. And it shows the Per Member Per Month (PMPM) breakdown in costs of individuals at each level of the pyramid. So, 543 individuals in this cohort who have diabetes and are also highly complex actually cost the system 39 million dollars over the year, or over $5,500 per month per patient.
6.27 Figure. Jonathan Weiner and Center for Teaching and Learning, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, 2016.
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7. An Example of an ACG “Risk Profile” Report for each Patient in a Cohort for Use by Case Manager
Displayed here is an exemplar output of detailed information on each high-risk individual at Johns Hopkins Health Care MCO. This information is shared with the case manager nurse and represents typical output of the Johns Hopkins ACG software. It reminds us that once the large segments are identified, detailed risk information will be needed to try to understand the unique situation of each person in need.
You may wish to review this sample “risk dashboard” carefully. It is an example of what can be derived with only claims data using a comprehensive risk adjustment tool. If EHR information is available, the dashboard can even be more comprehensive—for example, including blood pressure and height/weight information. This report provides a structured starting point that the care manager can use to prioritize, plan, and manage their assigned patient workload.
The exemplar is a real patient, a 60-year-old male enrolled in the JHHC HMO.
The probability that this person will be a high user next year is arrayed there. In this case, the likelihood is that this person has a greater than 50 percent probability of being in the highest group. These and other predictions, as explained in the previous lecture, are based on his current risk factor weights, based on large benchmark populations with similar characteristics.
This also arrays the various types of actual costs and current medical conditions. You can look at it slowly. It shows the presence or absence of different types of services and different types of coordination markers. In gaining an understanding of this person’s needs and history, it is important to look at not only diagnoses but also patterns of care he received and how well coordinated it has been.
This dashboard report shows the number of physicians seen. Those patients who have seen a lot of doctors are more at risk for inefficient care.
6.28 Figure. Johns Hopkins University, 2016.
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8. Using Decision Support to Help Determine Population-Based “Disease Management” Interventions for Diabetics
Now we return to the pyramid—in this case, a three-level pyramid. It reminds us of using reports and scores, as you’ve just seen. We can think about what a nurse case manager or whole team might do at the different levels of the pyramid.
At the bottom level, there’s outreach and education—for example, a smoking cessation program or a weight-loss program. The outreach could be via or a web portal or a mobile health app.
The second level might include remote monitoring. The program at Johns Hopkins is called TeleWatch. It offers telephonic prompts to participate in interventions such as blood glucose monitoring. It allows the patient to share feedback telephonically.
At the highest levels, because you can’t assign a nurse to everybody in the 30,000-person population, a nurse case manager might have a caseload of 200 or 300. The care manager might be a social worker, an RN, or a lay health worker from the community who is trained to work closely with the patient to address any needs he might have, such as housing or access to healthy food. The latter types of interventions are especially important for many Johns Hopkins patients given the hospital’s location in an inner-city community.
6.29 Figure. Jonathan Weiner and Center for Teaching and Learning, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, 2016.
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9. EHR and other HIT Data Offer Profound Opportunities to Measure Risk Beyond Current Claims-Based Models
To date, most predictive modeling and risk segmentation within the field of population health have used administrative risk data derived from hospital admissions records and health insurance claims information. Some also use surveys, but the problem with surveys is that there are many non-respondents, and it’s very expensive to collect those data. The outcomes of most of the risk adjustment tools to date have been on cost and utilization variables.
This is a very exciting time in the field of predictive risk measurement. Many researchers across the country, including at the Johns Hopkins Center for Population Health IT, are looking at all types of new sources of information, as shown here, that can increase both the accuracy and scope of the models. It can include the expanded clinical information found in the electronic health records. It can also move into population and consumer information from a variety of data sources. We will discuss a few examples of these new opportunities in the remaining part of this presentation.
6.30 Figure. Jonathan Weiner and Center for Teaching and Learning, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, 2016.
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10. “Provider order entry” (POE). “Investigation “results.”
New Electronic Sources of Risk Factor/Health Status Input Data Include: — 1
EHR “charting.”
Clinical findings, history, biometrics.
EHR workflow.
Clinical decision support (CDS), time stamps.
“Provider order entry” (POE).
E-prescribing, test-ordering.
“Investigation “results.”
Lab, imaging, EKG and cardio.
One new source of information that could be used to expand our knowledge of an individual’s risk factors can come from the charting function of the electronic health record (EHR).
The core objective of the EHR is to capture information to support the interaction between the clinician and the patient. Some examples are: clinical findings, medical history, biometric information, blood pressure, body mass index (BMI), family history, symptomatology. One of the challenges is that in addition to closed-end “click” entries, the EHR is used in a major way to capture free text. That is, the doctor and nurse just taking notes, as they did in the old days with paper and pen. And this does present some challenges, as most of the risk models derived from claims require specific fixed data fields.
The clinical workflow may include clinical decision support (CDS), offering guidance to the provider. This workflow is useful for documenting who knew what when. It can be used particularly for predictive modeling of a clinical phenomenon to look at the flow of the symptomatology. For example, when did a lab value change, and when did the doctor respond to that information?
Provider order entry (known as POE or CPOE) such as e-prescribing or test ordering, can also be used to derive risk information. With such data, it is possible to examine the prescription the doctor ordered versus what medication the patient actually obtained in the pharmacy. Most of the predictive modeling tools to date use pharmacy claims information, not the e-prescribing. Understanding the difference between what was prescribed and what was actually obtained is an example of some information that can be mined from the workflow function of the EHR.
Increasingly, results investigations, which generally include both laboratory and imaging (x-ray, EKG, electrocardiogram, and other cardiovascular), is all digital. One of the disadvantages of insurance claims information is that it does not have the clinical texture, such as this information. From claims, you only know that a test was ordered; you do not know the result. Inputting these factors into the predictive modeling process would potentially represent a great advance.
The domain of EKGs and imaging using so-called PACS (picture archiving and communication system) and other computerized representation of the images is increasingly coming online. But these are sometimes very difficult to input into a risk adjustment model as the images are not standardized. Labs, on the other hand, report very specific results, and one knows whether it is in or out of the normal range. These are rapidly being integrated into predictive modeling tools.
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11. Home devices, sensors, mHealth
New Electronic Sources of Risk Factor/Health Status Input Data Include: — 2
Home devices, sensors, mHealth
Patient health records (PHRs), patient portal
Consumer preferences, actions, and functions
Social networks / e-interactions
Doctor-patient, patient-patient, doctor-doctor
Community surveillance, public health networks
We are using many new types of devices: home devices, telemedicine, mHealth (“mobile health”), sensors, Fitbits. Apple is trying to market its iWatch to health care systems. The idea is to integrate consumer movement, heart rate, and other things captured on the sensors of the iWatch directly into the EMR. That’s a little bit in the future, but this increasingly will be part of predictive models.
Certainly for a long time, consumer information from health risk appraisal (HRA) surveys have been input. Again, the problem is that sometimes, sick patients don’t complete those surveys and the response rates are fairly low. But obviously, consumer information and preferences and functional status—how well they’re able to accomplish various tasks—is also an important data source. And to the extent that consumer-based e-health facilitates capturing these data, this represents a potential advance.
Also on the consumer side, some commercial insurance companies and others are now mining the same financial information and Internet information that other marketers are using. You know, what type of magazines you read, what type of websites you visit, what is your credit rating, what community you live in. And these things could potentially be used not just to sell you something but also to understand your risk level and improve your health function.
Understanding the patterns of electronic interaction between consumers and doctors will gain in importance for those assessing risk. This type of interaction is increasing rapidly in wired organizations who use and web portals. Also related to this is interaction not between providers and consumers, but between consumers and other consumers via social networks. Some studies have suggested that understanding these patterns can also help understand current and future health patterns.
The area of community surveillance and public health data is one that’s increasingly being blended into medical care delivery systems. It’s still relatively new to include geographic level data into a health plan or a delivery system’s predictive modeling score for case finding, but this is beginning to happen. For example, if a person lives in an area with high environmental risk, low food availability, or a high rate of communicable disease, these are factors that might be included in calculating their overall risk score.
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12. “Morbidity trajectories” over time
Moving Beyond Cost and Utilization: Some New Targeted Endpoints and Outcomes of EHR-Based Predictive Modeling — 1
“Morbidity trajectories” over time
Real-time population health, community surveillance
Real-time clinical action for individual consumer
Functional status, frailty
Biometric attributes
Cardiovascular, other physical functions
Up until now, and in most of this presentation, predictive models and risk adjustment have focused mainly on health care costs and utilization because those are the type of data that have been available. As EHR data are coming online, increasingly they are becoming interoperable (that is, linked across providers). This will allow us to capture patient outcomes across the entire care continuum.
As this happens, we can use EHR and other data sources to identity new endpoints of population-based predictive models and risk measurement systems. That’s exciting. Here is a summary of some of the endpoints or targets.
These new models may predict the trajectory of disease over time: for example, when is it likely to get worse or better. They might predict the health in a community—following more the public health model of surveillance.
The model might target functional status. For elders, it’s particularly important to not just look at disease but also function. There could be people with significant disease who are high functioning, and there can be people with lower levels of disease who are low functioning.
It is important to understand biometric attributes: blood pressure, for example, or blood sugar. There have been great advances within clinical forecasting and modeling in some cases, using genomic information. To date, that has mainly been of scientific inquiry or focused clinical conditions. This area of work hasn’t been expanded into the population domain yet, where we attempt to understand risk in communities or other large denominators. But in the future, it certainly will be.
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13. Social needs and challenges Consumer health related behaviors
Moving Beyond Cost and Utilization: Some New Targeted Endpoints and Outcomes of EHR-Based Predictive Modeling — 2
Social needs and challenges
Consumer health related behaviors
Mortality and longevity
Continuing on with new targets for predictive models are social needs and challenges.
One could predict, for example, in a disadvantaged pediatric population, those who didn’t have adequate access to nutrition, things that weren’t on their own medical outcomes but really are all-important to health. There are models and approaches that are starting to do that.
Also knowing how important certain consumer behaviors are for health, take exercise or smoking as examples, it would be helpful to have models that predict a consumer’s tendency toward high-risk behaviors.
And while there have been models from a research perspective looking at longevity and mortality, in the future these will be integrated more into a population-based perspective. Today, some models are being applied to help guide end-of-life and hospice care programs.
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14. Risk Metrics Can Be Applied at Geographic Levels: Using Past Healthcare Use Data Files by Maryland Health Information Exchange to Predict Risk of Readmission by Place of Residence
This map depicts an activity going on in Baltimore, Maryland, where the health information exchange known as Chesapeake Regional Information System for our Patients (or CRISP, for short) is working with Johns Hopkins and other organizations to try to do predictive modeling at the entire state level, linking in data from all the hospitals, potentially data from all the health insurance plans, and, increasingly, data from the electronic health records to try to identify who is at risk for readmission. There is a unique situation in Maryland in that the hospitals are now paid a flat global budget, and they are being held accountable for an array of outcomes, including readmission. So there is a lot of incentive to try to predict and intervene at the population level and not just wait till the patient is admitted.
6.31 Figure. Chesapeake Regional Information System for our Patients (CRISP). Used with permission.
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15. HIT Will Allow Great Advances in Population Health Risk Measurement and Predictive Modeling — 1
Ways to integrate disparate “numerators” and “denominators” to define true populations and communities.
Ways to identify those “at-risk” both at the community and patient-panel level.
This field of risk segmentation and predicative modeling in population health is evolving very rapidly as new electronic data sources come online and as our health care system increasingly reorients itself towards population health. Before we close this lecture and unit, it is worth reviewing some of the major ways health IT is likely to lead to advances in coming years.
The old model of medical care, just focusing on the patient in the hospital bed or in the ambulatory office, has given way to really an understanding of the cohort from which that patient has come from, as well as understanding how we can be most efficient at targeting people with great need. Increasingly, with new data sources and analytic tools, we will be able to identify who is part of the population denominator and who will fall into the numerator of the various risk pyramids we have showed you. This sounds simple, but it isn’t.
Speaking of increased denominator focus, in the past, virtually all predictive modeling and case-finding focused on those cared for by a provider organization or insurance plan. Now with community-wide interoperable medical care data and non-medical public health and social data, it will be possible to accomplish this process for geographic communities.
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16. HIT Will Allow Great Advances in Population Health Risk Measurement and Predictive Modeling — 2
Advanced tools for extracting and analyzing unstructured data from many sources.
Models and tools to help medical care systems move towards “population value” perspectives.
Increasing integration of population health analytics and decision support.
Most of the data sources to date have been for risk calculation and have come from fixed, structured data sources. Increasingly, through data science techniques, we’ll be able to do data mining that will identify unstructured data. The classic one could be the doctors’ or nurses’ notes that are just in text form. But there is a lot of other unstructured data that can be captured.
Historically, the risk information and predictive modeling have focused on outcomes that are based on cost or a medical model. But as we try to understand the multi-faceted equation of what a population values (it could be social, it could be function), increasingly it’ll be possible to pull together a broad array of information and develop a dashboard for the care manager — ultimately for the consumer, for the clinician — of the different variables, the risk, what can be done to intercede.
Just as there have been great advances in clinical decision support, there are likely to be great advances in population decision support, tools using predictive models, risk segmentation that will help population management really enter a new era — at the patient panel level, at the community level, at the system level.
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17. Identifying Risk and Segmenting Populations: Predictive Analytics for Population Health Summary — Lecture c — 1
Population-health oriented integrated delivery systems, like Johns Hopkins Health Care, increasingly will apply comprehensive risk adjustment and predictive modeling tools using computerized data as described here.
Electronic health records and other new sources of data will offer opportunities for developing new types of predictive modeling tools that will input a wide range of risk data that can be applied to many different targeted outcomes and population health contexts.
This concludes Lecture c, Identifying Risk and Segmenting Populations: Predictive Analytics for Population Health.
In this lecture, we reviewed a case study of how a population-health oriented organization, the Johns Hopkins Health Care managed care organization, implemented risk segmentation to better manage the large population of persons for whom it is responsible.
We also offered some insights into new uses of electronic health records and other data sources that will likely expand this field considerably over the coming years.
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18. Identifying Risk and Segmenting Populations: Predictive Analytics for Population Health Unit Summary
Risk adjustment and predictive modeling are essential for managing risk in today’s health care system.
There are several methodologies for gathering electronic health information on patients so that we can segment populations into sub-groups to identify those with higher risk levels.
Predictive modeling tools are developed using large benchmark populations:
Both analytic approaches and clinical logic are applied to these tools.
The Johns Hopkins ACG® System shows how these tools are constructed and used.
EHRs and other new sources of data will guide future developments in predictive modeling tools.
Let's review the main points of this unit:
Risk adjustment and predictive modeling are essential for managing risk in today’s health care system.
There are several methodologies for gathering electronic health information on patients so that we can segment populations into sub-groups to identify those with higher risk levels.
Predictive modeling tools are developed using large benchmark populations:
Both analytic approaches and clinical logic are applied to these tools.
We looked at a commonly used predictive modeling tool, the Johns Hopkins ACG System, to learn how these tools are constructed and used.
And lastly, EHRs and other new sources of data will guide future developments in predictive modeling tools.
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19. Identifying Risk and Segmenting Populations: Predictive Analytics for Population Health References — Lecture c — 1
References
Abood S. (June, 2002). Quality improvement initiative in nursing homes: the ANA acts in an advisory role. [Electronic version.] Am J Nurs;102(6).
Carlson BM. (2004.) Human Embryology and Developmental Biology. 3rd ed. St. Louis: Mosby.
Amarasingham, R., Patzer, R.E., Huesch, M., Nguyen, N.Q., Xie, B. (July, 2014). Implementing electronic health care predictive analytics: considerations and challenges. Health Affairs, 33(7): Retrieved from:
Clark JM, Chang HY, Bolen SD, Shore AD, Goodwin SM, Weiner JP. (August, 2010). Development of a claims-based risk score to identify obese individuals. Popul Health Manag; 13(4):201–207. Retrieved from:
Haas LR, Takahashi PY, Shah ND, Stroebel RJ, Bernard ME, Finnie DM, Naessens JM. (September, 2013). Risk-stratification methods for identifying patients for care coordination. Am J ManagCare;19(9):725–32. PubMed PMID: Retrieved from:
Johns Hopkins ACG “Predictive modeling” system,
Wharam JF, Weiner JP. The promise and peril of healthcare forecasting. (March 1, 2012). Am J ManagCare; 18(3): 382–5. Retrieved from:
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20. Identifying Risk and Segmenting Populations: Predictive Analytics for Population Health References — Lecture c — 2
Tables and Figures
6.24 Figure. JHU Organizational Chart. Jonathan Weiner and Center for Teaching and Learning, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University. (2016).
6.25 Figure. Population Health Database at JHHC. Jonathan Weiner and Center for Teaching and Learning, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University. (2016).
6.26 Figure. A Predictive Model for Stratifying Population of Persons with Diabetes. Jonathan Weiner and Center for Teaching and Learning, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University. (2016).
6.27 Figure. Total JHHC Diabetes Population. Jonathan Weiner and Center for Teaching and Learning, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University. (2016).
6.28 Figure. An Example of an ACG “Risk Profile” Report for each Patient in a Cohort for Use by Case Manager. Johns Hopkins University. (2016).
6.29 Figure. Using Decision Support to Help Determine Population-Based “Disease Management” Interventions for Diabetics. Jonathan Weiner and Center for Teaching and Learning, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University. (2016).
6.30 Figure. EHR and other HIT Data Offer Profound Opportunities to Measure Risk Beyond Current Claims-Based Models. Jonathan Weiner and Center for Teaching and Learning, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University. (2016).
6.31 Figure. Healthcare Use Data Files. Chesapeake Regional Information System for our Patients (CRISP). (n.d.) Used with permission.
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21. Population Health Identifying Risk and Segmenting Populations: Predictive Analytics for Population Health Lecture c
This material (Comp 21 Unit 6) was developed by Johns Hopkins University, funded by the Department of Health and Human Services, Office of the National Coordinator for Health Information Technology under Award Number 90WT0005.
No audio.
End.
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