HYPERTENSION
LHS Learning Health System
A STORY OF HEALTH CARE IMPROVEMENT
HOW A LEARNING HEALTH SYSTEM ENSURES EXCELLENT HIGH BLOOD PRESSURE TREATMENT Our health system includes doctors’ offices, public health departments, hospitals, insurance companies, and research labs. People who work in these organizations each follow different sets of guidelines, rules, policies, and laws. To provide health care, monitor population health, pay for services, and investigate disease, a variety of information resources are used. As patients and health professionals, it’s easy to lose sight of the number of ways health information is used. However, if you’ve had to change your insurance plan, or to switch your primary care provider, or to deal with a complicated disease requiring treatment by multiple practitioners, or tried to get a copy of your medical record, you’ve probably seen some of the ways in which our health information infrastructure is broken. The way health information is currently managed is a big problem. Poor communication across organizations slows down treatment and research processes. Data is collected and recorded multiple times. It is difficult to know if the best decisions about health are being made. As health information becomes stored in electronic health records, public health registries, and research databases, it can be shared more quickly and broadly. Will a greater degree of information sharing lead to a more effective health system that can select the right treatment for the right person at the right time? Yes! If we can turn the health system we have today into a Learning Health System.
This story board shows and describes a realistic case of ongoing learning about the best ways to treat blood pressure – a very common disease. A Learning Health System puts the following “loop” into action. The loop involves a sequence of processes that allow people to continuously learn about and resolve many types of real, health dilemmas. Interpret Findings Analyze Data
Assemble Data
Feedback As Advice
LHS LOOP
Change Practice
Gather Data
Data about a health problem is first gathered at the bottom of the loop and then assembled and analyzed on the blue (afferent, “A”) side of the loop. New findings from the data are interpreted by experts at the top of the loop. When the findings are judged to be meaningful, they can be shared with others as advice on the red (efferent, “E”) side of the loop. Behaviors and practices change based on this advice. As these changes occur, more data can be collected so the learning cycle repeats.
CHARACTERS AND ICONS LHS The LHS lane describes functions and features of a Learning Health System
A Learning Health System is one that seamlessly integrates research with health decision making to support innovation and achieve continuous improvement in health outcomes.
A digital knowledge object (DKO). A container to track and move knowledge within a Learning Health System.
A network of associations amongst the digital knowledge objects within a Learning Health System.
INDIVIDUAL Dr. Paula Prescott A primary care physician who participates in the Learning Health System.
The Individual lane involves consumers and providers interacting within a Learning Health System
Conrad, 45 years old. A single man who lives alone. Recently diagnosed with high blood pressure.
Carolyn, 38 years old. Recently diagnosed with high blood pressure. Married with a history of anxiety and depression.
POPULATION The Population lane shows the relationships between individual consumers and the populations and subpopulations they represent for research and decision-making purposes.
Populations, or cohorts, represent a set of patients with similar characteristics or those who are in a particular treatment group.
SUPPORT The Support lane involves organizations and information infrastructure that support the work being conducted within a Learning Health System.
Mike is a research coordinator for a community of interest. He works for the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure.
Subject matter experts give their opinion on data that has been collected and its relevance to the population at large.
The internet is the technical backbone for the network that supports the Learning Health System.
There are various interfaces to the Learning Health System, including patient portals and mobile apps for providers.
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Time: Week 1
Time: Week 2
LHS
Learning Health System INDIVIDUAL “Hello. I’m Conrad. I was recently told that I have high blood pressure when I was screened at a health fair. I also have a history of depression.” “Hello. I’m Carolyn. As far as I can see, I’m pretty healthy and I usually do what my doctor recommends. I feel fine. I just worry a lot over family issues and our finances, that’s all.” “Hi. I’m Dr. Paula Prescott. I am a primary care physician. Carolyn and Conrad are my patients. I work at an organization that operates as a learning health system.”
POPULATION For the LHS, population cohorts are defined by demographic characteristics, disease state, geography, and time. “High” and “low” risk population cohorts can be determined using recommendations from guidelines.
SUPPORT The internet is an important supporting system that helps to make a LHS possible.
“Surely there are other people who are very similar to me. I wonder if there is a way that we can learn from each others’ experiences about how to stay healthy.”
“Hello. I’m Mike. I work for the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC). Today I am convening a group of Subject Matter Experts (SMEs) to review some new findings.” SMEs represent a community of interest for comparing hypertension treatments and must determine if new knowledge meets standards for dissemination. The SMEs decided that the latest data about when to begin medication therapy following the diagnosis of high blood pressure are not clear enough to recommend changes to existing hypertension treatment guidelines. Instead, they want to see results from surveying and tracking newly diagnosed individuals to better answer this question.
A learning health system facilitates the work of communities of interest who come together to address a specific problem. In this case, a community of interest called the JNC wants to learn how best to treat high blood pressure. JNC has specified a question that they want answered. They wish to know when is the best time to start medication therapy after a diagnosis of high blood pressure is confirmed. The current high blood pressure treatment guidelines may not be adequate for everyone. Limited evidence suggests sedentary adults age 30-50 years with a diagnosis of depression or anxiety have difficulty maintaining lifestyle modifications. It is hypothesized that this population will benefit from early medication therapy. The JNC would like to leverage the Learning Health System to study this hypothesis. A new digital knowledge object has been created for JNC to help manage medication therapy initiation knowledge. Although lifestyle changes are recommended to lower blood pressure, they don’t always work. Some people may benefit from starting medications immediately. JNC is trying to learn who will benefit from early medication therapy. JNC developed a survey to investigate. I just received a new survey message from JNC. Because of the role I play making my organization a learning health system, I will send this survey to my patients via e-mail.
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Time: Week 3
“I got an e-mail from Dr. Prescott with a link to a survey. She asked me to fill out the survey which inquired about my blood pressure. I indicated that I was recently warned about my blood pressure for the first time.”
Time: Week 3
Individual experience data is updated and includes information about about whether they responded to the message, their responses, relevant clinical data, and references to related Digital Knowledge Objects (DKOs).
LHS-enabled apps will give users an easily accessible interface for accessing health and user-provided data. It will allow currently disparate systems of information to work together to streamline data gathering, analysis, dissemination, and learning.
INDIVIDUAL
Dr. Prescott receives notification that Conrad’s survey responses have triggered a follow up message to Conrad.
Learning Health System interfaces permit others to send data and receive messages, requests, and actionable knoweldge. Some messages are indivdually tailored for recipients based on the recipient’s role, background, characteristics, and stated interests.
Time: Week 3
Conrad receives the following tailored advice: “Make an appointment with your doctor to discuss your high blood pressure treatment.”
POPULATION SUPPORT
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Month 2
Month 2
LHS
Time: Week 4
The blood pressure readings, medication charts, diet plans, and exercise logs that the patients track at home not only link to their electronic health records but also feed the federated data collection of the LHS. Patients may enter some of the data manually, but, to the extent possible, data is also entered automatically through the use of personal fitness trackers (e.g., Fitbit, Jawbone) or biometric readers.
INDIVIDUAL
Conrad considers advice from his LHS app, “Because of my answers to the survey, they suggested I schedule a follow-up appointment with my doctor to discuss my high blood pressure treatment plan.”
POPULATION
SUPPORT
After a cordial greeting, Conrad confirms that he lives alone. He winces when asked about exercise. Dr. Prescott is concerned that Conrad will not be successful with lifestyle modification given their conversation. She is also concerned about his history of depression. Dr. Prescott decides to start Conrad on a medication to lower his blood pressure. She also prescribes a fitness tracking device and a related mobile app. Conrad agrees to take the medication and to try to walk a little more.
Finally, Dr. Prescott asks Conrad to consent to share his health data so others can learn from him. Conrad agrees and provides his consent.
Conrad’s consent to share his health data with outside entities for learning purposes is kept on record with his health system.
After a greeting, Dr. Prescott gently tells Carolyn that her blood pressure is high. Carolyn is surprised. They then discuss Carolyn’s diet and exercise. Carolyn commits to daily exercise and choosing healthier snack foods. Dr. Prescott prescribes a fitness tracking device with a related mobile app.
As they part, Dr. Prescott asks Carolyn to consent to share her health data with others for the purpose of learning. Carolyn agrees to do so.
Carolyn’s consent to share her health data with outside entities for learning purposes is kept on record with her health system.
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Time: Month 3
To support ongoing analysis of data, a target dataset is defined. In this case, the target dataset includes data specifying when individuals were diagnosed with high blood pressure, if and when they started taking medications to treat high blood pressure, and what their treatment outcomes have been.
Time: Month 4
Individuals with appropriate access can query LHS knowledge resources. Queries of LHS knowledge resources may take place at any time and may be prompted by a specific need or general interest. Digital knowledge objects can encode provisional knowledge of suggested, but unconfirmed relationships. Carolyn has access to an information resource about hypertension through the app prescribed by Dr. Prescott. She decides to investigate whether her history of anxiety and depression would impact her hypertension treatment. She enters two queries and receives the following responses: “Although unconfirmed, data suggest that anxiety may raise blood pressure.”
Patient history results in their assignment to population cohorts. Based on their disease and treatment histories, Conrad is placed in the early medication therapy cohort while Carolyn is placed into a cohort that will attempt making changes in their behavior before intervening with medication therapy. In addition to Conrad and Carolyn, many other individuals’ data are being collected for both cohorts.
“Although unconfirmed, data suggest that depression may interfere with efforts to control blood pressure.” Despite the fact that she has been diagnosed with anxiety and depression in the past, this information does not change Carolyn’s belief that she can maintain a normal blood pressure without taking medications.
Time: Month 5
INDIVIDUAL Conrad’s checkup begins by evaluating his body weight. He and Dr. Prescott are relieved that his weight is unchanged. Conrad shares his fitness data which shows he has not been walking regularly.
Dr. Prescott asks Conrad if he prefers not to exercise and he answers honestly that his preference is not to exercise much at all. Conrad’s blood pressure readings are acceptable but Dr. Prescott wants to know if these readings are consistent. She asks conrad to begin measuring his blood pressure at home. Conrad agrees to track his blood pressure and to continue taking his blood pressure medication.
POPULATION
SUPPORT An application can be used to query and get the latest knowledge.
Applications may be used to collect data, ask questions, and provide advice
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LHS
Time: Month 5
INDIVIDUAL Carolyn returns to see Dr. Prescott after several months. She has lost 3 pounds. Carolyn expresses her pleasure at the news and indicates she has been regularly walking with her husband. Still, Carolyn’s blood pressure remains “on the high side of normal.”
Dr. Prescott recommends that Carolyn continue with her treatment plan and Carolyn agrees. She thanks Dr. Prescott for prescribing the fitness tracking device and mobile app. Carolyn expresses that the app sends her motivational messages that she finds helpful.
POPULATION
SUPPORT Applications may be used to send individualized messages for a variety of purposes.
Time: Months 6 to 10
Large cohorts can sometimes be quickly studied using heterogeneous data from many sources and organizations. To answer the question of interest to JNC about for whom and how soon medication therapy should be initiated upon a first diagnosis of high blood pressure, data has been gathered about Conrad’s cohort and Carolyn’s cohort in a relatively short period of time.
Just as Conrad and Carolyn did, others newly diagnosed with high blood pressure are consenting to “opt-in” and share their health data to help JNC and others learn about the disease of hypertension. My team at JNC is now analyzing the data that has been gathered about Conrad’s and Carolyn’s cohorts in a relatively short period of time. We anticipate potential updates to our current guidelines regarding hypertension treatment. Further, new correlates related to high blood pressure treatment are suggested by initial data analyses.
Time: Month 10
“Soon I will see Carolyn and Conrad again in the clinic. I am hopeful that we will all begin to see new knowledge that results in part from sharing their data.”
Analysis shows that economic resources or social support may be predictive of early high blood treatment outcomes within the six months after a first diagnosis. It is too early to conclude that these two factors are important. Instead, we first need to gather more data about these factors to see whether they are indeed significant.
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Time: Month 11
Conrad returns to the clinic for an evaluation of his blood pressure. Unfortunately, he has gained a couple of pounds over the past six months. Conrad accepts this news with a shrug and again he tells Dr. Prescott that exercising is just “not his thing.” However, Conrad’s home blood pressure monitoring data indicate his blood pressure is quite well controlled.
Time: Month 11
Month 11
INDIVIDUAL Carolyn returns to the clinic for an evaluation of her high blood pressure. Dr. Prescott finds her fit and healthy. Her blood pressure is within normal limits. The mood is celebratory!
Conrad purchases a new smart phone. He decides to load the app that Dr. Prescott previously prescribed for him. For the first time Conrad really studies the app and he notices that it offers him a way to record his energy level periodically. Conrad activates this feature. Now the app will periodically ask Conrad to rate his energy level on a 5-point scale and save the results. Eventually a picture of Conrad’s energy level throughout the day, week, and month could result.
POPULATION Dr. Prescott thanks Conrad for collecting the data which can now be shared with others. She asks him to continue taking his medication and checking his blood pressure at home. Conrad agrees to do both of those things.
After a brief time of talking and sharing, Dr. Prescott asks Carolyn to maintain her diet and exercise regimens and generally to “keep up the good work!”
As more and different types of data are collected, cohorts can be more precisely and specifically defined.
SUPPORT
Apps can be used to support the routine collection and periodic sharing of individual health data
Apps can be used to solicit survey responses and trigger the routine collection of relevant trending data over a period of time.
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LHS
Time: Month 12
Time: Month 13
Time: Month 14
INDIVIDUAL POPULATION
SUPPORT
Conrad gets a link to a new survey about personal economics and social support. He feels that it is somewhat invasive, but Dr. Prescott’s endorsement empowers him to complete the survey. Carolyn is delighted when her health system sends her a survey related to her hypertension management. She is enthusiastic about contributing to the health of others.
SMEs recommended no change at this time to current guidelines with respect to socioeconomic considerations, but do recommend further analysis. This decision by the SMEs prompts a new round of surveys to augment data collection. Mike’s team develops a new survey for the JNC that adds economic and social support questions.
“Carolyn and Conrad will be returning for their next follow up appointment in 2 months. I look forward to discussing their progress so far.”
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Time: Month 1 15
Time: Month 15
Time: Month 17
INDIVIDUAL
Conrad gets a message reminding him of his next appointment with Dr. Prescott.
Conrad’s cohort is dynamic and continuing to grow as others begin hypertension treatment. A majority of patients in the cohort have good blood pressure control at the 12 month checkpoint, but commitment to exercise is poor.
Carolyn gets a tailored message from her health system that suggests that family and other forms of social support are helpful when patients are trying to comply with treatment.
Carolyn’s cohort, that has adopted lifestyle modifications alone, despite having a history of depression or anxiety, has demonstrated poorer blood pressure control compared to Conrad’s cohort. This group demonstrates a clearer correlation between diet, exercise and app compliance and improved blood pressure control.
Conrad and Dr. Prescott share a pleasant greeting at his next visit. Dr. Prescott comments on the social support survey that Conrad completed and thanks him for his participation. She tells Conrad that social support may be a key factor in helping individuals keep their blood pressure under control. Because at this visit Conrad’s blood pressure is on the high side of normal, Dr. Prescott expresses mild concern. She asks Conrad if he would consider joining a support group for individuals with high blood pressure. Conrad shows interest and expresses his hope that he will meet others like him.
POPULATION SUPPORT
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Time: Month 18
Time: Month 18
LHS
Time: Month 17
INDIVIDUAL After a friendly greeting, Carolyn and Dr. Prescott discuss the association between social support and improved control over blood pressure. Carolyn tells Dr. Prescott she has seen a bulletin about social support and high blood pressure and wants to learn more about it. Dr. Prescott tells Carolyn that it is too early to know precisely how social support and one’s ability to control their blood pressure are related. She suggests that social support may be helping Carolyn in her efforts to control her blood pressure. Carolyn is pleased to hear it.
POPULATION
SUPPORT
Conrad is starting to take his high blood pressure seriously. A member of his support group has been encouraging him to pay more attention to his health, especially to his diet and exercise regimens. Conrad’s friend is a stroke survivor and she intends to make sure all of her friends maintain a healthy blood pressure.
After about a year of data collection and analysis, my team at the JNC has been able to further investigate when to initiate medication therapy and the impacts of anxiety, depression, and social support on hypertension outcomes—the findings that will next be presented to our Subject Matter Experts. The most important finding could turn out to be that social support is a critical factor in determining how well individuals control their blood pressure. We will see what the experts think.
Today is Carolyn’s birthday. She celebrated by dining out and having a cheeseburger and french fries. Then she and her husband went for a long walk. She feels good and is delighted that she has been successful at keeping her blood pressure under control by making healthy choices and without the use of medications.
We have met and decided that there is enough evidence to update the JNC hypertension treatment guideline. The details of the update will include a new assessment of social support for those newly diagnosed with high blood pressure. We have concluded that social support is one of the factors that should inform treatment decisions for high blood pressure.
EPILOGUE
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WHAT WAS LEARNED WHEN, AND BY WHOM? In the story, the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC) represents a community of interest wanting to provide the best possible evidence-based guidelines for high blood pressure treatment. They monitor, evaluate, and update their guidelines within a Learning Health System (LHS).
Time: Month 18
At the outset, the JNC used preliminary findings to draw a hypothesis that a group of individuals existed for whom immediate medication therapy to treat high blood pressure would be beneficial (instead of trying to change eating or exercise habits first). They sought to test this hypothesis and to determine how to identify the members of such a group. A survey was used to gather data.
The HTN DKO from the SMEs is updated with the new data references. It also contains new action items for patients and care providers. The history of the changes and decisions of the SME group are recorded for provenance.
INDIVIDUAL
Dr. Prescott has received the new JNC hypertension guideline with updates about the effects of social support on hypertension treatment selection. Dr. Prescott is happy that Carolyn and Conrad were able to contribute to the Learning Health System and improve the health of others.
Dr. Prescott was made aware of JNC’s new hypothesis about early medication therapy in patients with a comorbidity of depression or anxiety. She began to start medication treatment immediately for some of her patients and not for others. She asked all of her patients to contribute data about themselves and their treatment outcomes to the LHS. Meanwhile, Carolyn, who was just diagnosed with high blood pressure, did her own search of an LHS information resource and found out about known associations between a medical history of anxiety or depression and increased difficulty maintaining lifestyle modifications. Later, findings reviewed by JNC experts indicated there may be two factors impacting early high blood pressure treatment outcomes, economic resources, and social support. Again, more data was called for and collected. In the end, the JNC determined that medication therapy should be started immediately for those newly diagnosed with high blood pressure who have minimal social support. An update about social support as a determining factor in early treatment decisions was made to their high blood pressure treatment guidelines and the new JNC guidelines were then shared with the nation.
POPULATION SUPPORT
Thanks to the virtuous learning loop of the LHS, our collective ability to provide the best individual and population care for high blood pressure was improved. This story is a stylized one, but it represents an achievable goal of systematic, ongoing, participatory learning afforded by a LHS.
ABOUT LEARNING HEALTH SYSTEM INITIATIVES AT MICHIGAN This story of health care improvement exemplifies the emergent and transformative power of a Learning Health System (LHS). It is a product of the University of Michigan Learning Health System Initiatives. This work was supported by a grant from the University of Michigan Office of the Provost Third Century Initiative Global Challenges program. The Global Challenges program focuses on urgent, complex, multi-disciplinary problems that affect our state, the nation, and the world. The Learning Health System Third Century Initiative project engages a multidisciplinary team of over 60 faculty, senior staff members, and graduate students across the University of Michigan. The team includes representatives from the School of Information, the Medical School, the School of Public Health, the School of Nursing, the School of Dentistry, the College of Engineering, the College of Pharmacy, the School of Natural Resources and the Environment, the University Library,
and the University of Michigan Health System. The project leadership includes: • Principal Investigator: Charles Friedman, School of Information, School of Public Health, and Medical School • Program Officer: Joshua Rubin, School of Information and Medical School • Project Manager: Kathleen Ludewig Omollo, Medical School • Task Force Co-Chair - Science: Marcy Harris, School of Nursing • Task Force Co-Chair - Science: Carl Lagoze, School of Information • Task Force Co-Chair - Technology: David Hanauer, Medical School • Task Force Co-Chair - Technology: Satinder Singh, College of Engineering • Task Force Co-Chair - Engagement: Ted Hanss, Medical School • Task Force Co-Chair - Engagement: Lynn Johnson, School of Dentistry • Task Force Co-Chair - Policy: Julia Adler-Milstein, School of Information • Task Force Co-Chair - Policy: Daniel Lee, School of Public Health
Authors This storyboard was authored by Learning Health System Initiatives team members Allen Flynn, Johmarx Patton, and Jodyn Platt, with graphic designer Aimee Andrion and copyedit by Melissa Bruno. Copyright Images from The Noun Project: package, public domain; internet icon adapted from Brandosaur.us, CC BY and Gilbert Bages, CC BY; computer by Alex Valdivia, CC BY; tablet and phone by Pham Thi Dieu Linh, CC BY; population by Wilson Joseph, CC BY; subject matter experts by Julieta Felix, CC BY. People images from Shutterstock. Other images created by Aimee Andrion. Copyright 2013-2014 The Regents of the University of Michigan. All rights reserved. The Block M logo is a registered trademark of the University of Michigan.