PANCREATIC CANCER

LHS Learning Health System

A STORY OF HEALTH CARE IMPROVEMENT

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 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.

Pancreatic cancer is the fourth leading cause of cancer death in the United States. This story board shows and describes a realistic case of

a Community of Interest that forms to learn about the management and treatment of inoperable and refractory (untreatable) pancreatic cancer. This means that the cancer cannot be removed surgically and the cancer is not responding to known drug treatments like gemcitabine. Refractory pancreatic cancer is a dire condition associated with very low rates of survival. There is hope that a Learning Health System (LHS) can accelerate learning about how best to manage and treat the disease.

A Learning Health System puts the following “cycle” into action. The cycle involves a sequence of processes that allow people to continuously learn about and resolve many types of real health dilemmas.

Data about a health problem is first gathered at the bottom of the cycle and then assembled and analyzed on the blue (i.e. left) side of the cycle. New findings from the data are interpreted by experts at the top of the cycle. When the findings are judged to be meaningful, they can be shared with others as advice on the red (i.e. right) side of the cycle. Behaviors and practices change based on this advice. As these changes occur, more data can be collected so the learning cycle repeats.

HOW A LEARNING HEALTH SYSTEM ACCELERATES COLLECTIVE IMPROVEMENT IN MANAGEMENT AND TREATMENT FOR PANCREATIC CANCER

InterpretFindings

GatherData

FeedbackAs Advice

ChangePractice

Analyze Data

AssembleData

LHSCYCLE

The Individual lane involves consumers and providers interacting within a Learning Health System.

Dr. Earl Green An oncologist who benefits from the capabilities of the Learning Health System.

Clara, 58 years old. A single woman who was recently diagnosed with an inoperable form of pancreatic cancer.

Wendy, 53 years old. Wendy is Clara’s sister and is involved in helping to care for Clara.

The Support lane involves organizations and information infrastructure that support the work being conducted within a Learning Health System.

Dr. Muriel Waters is a research coordinator for a Community of Interest (COI) called the National Committee on Pancreatic Cancer Research and Treatment.

A tumor board is a group of practicing health profes-sionals that review ongoing care plans for patients at their institution.

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.

The Population lane shows the relationships between individual consumers and the popu-lations and subpopulations they represent for research and decision-making purposes.

CHARACTERS AND ICONS

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Populations (left image), or cohorts (right image), represent a set of patients with similar characteristics or those who are in a particular treatment group.

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) is 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.

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“My name is Clara. I am a poet and am currently working on my second anthology. A couple of months ago, I was diagnosed with an inoperable form of pancreatic cancer.”

“Hi. I’m Dr. Earl Green. I am Clara’s oncologist. I recently started her on the drug gemcitabine. I am interested in engaging with the Learning Health System to create the best possible care plan for Clara.”

“Hello. I’m Wendy, Clara’s sister. I’ve been going along with Clara to doctors visits and helping her keep track of her treatment.”

For the LHS, population cohorts are defined by demographic characteristics, disease state, geography, and time.

“Clara’s cancer is inoperable and she is currently receiving the drug gemcitabine. In the instance that the cancer does not respond to gemcitabine, there is no consensus on a subsequent treatment course.”

“Hi. I'm Dr. Muriel Waters. I work for the National Committee on Pancreatic Cancer Research and Treatment. The committee coordinates strategies surrounding pancre-atic cancer treatment. One of our interests is to develop an evidence-based guideline to treat patients who have inoperable and refractory pancreatic cancer. This means the cancer cannot be removed surgically and the cancer is not responding to known drug treatments like gemcitabine. But we do not have enough data to create such a guideline.

Members of this committee, which include specialists from across the country, have agreed to work together on this problem by collecting and analyzing data about pancreatic cancer from institutions using LHS and then disseminating the knowledge learned via the LHS.”

A Learning Health System facilitates the work of Communities of Interest (COI) which come together to address a specific problem. The LHS allows COIs to openly share data about past and present cases both within and outside the community, including data about lifestyle issues related to a disease or health problem. A COI discovers and validates new knowledge, curates, and delivers it to the world via the LHS.

In this story, the National Committee on Pancreatic Cancer Research and Treatment make up the COI; their specific problem involves learning how to best treat patients who have inoperable and refractory pancreatic cancer.

“We currently collect detailed, standardized case reports on patients who have inoperable and refractory pancreatic cancer through the LHS. While our goal is to collect enough data to define best practice guidelines for pancreatic cancer care, we are at an early stage of gathering and organizing case data and do not have enough case data yet to make statistical claims.”

The LHS supports ongoing data collection, which can draw upon a number of sources, include data from electronic health record systems. All data can be traced back to their original sources for auditing, validating, and refining data collection and entry methods.

“I reviewed Clara’s most recent imaging studies and lab tests which indicate that there has been little response to the drug gemcitabine. Next, we have to decide on the follow-up treatment course. There is no strong evidence for what follow-up would give Clara the best outcomes. The LHS can help determine if there is any knowledge about which follow-up treatments have worked best for patients like Clara.”

An application can be used to query and access the latest knowledge from the COI and other sources that focus on how to treat patients like Clara.

“Our committee regularly aggregates and analyzes pancreatic cancer data. We use an application to share our findings with the LHS.”

The LHS allows the COI to create and manage the knowledge resources they obtain in the form of a Digital Knowledge Object (DKO). Once the COI authors a DKO, others with appropriate access can query the LHS at any time and read the DKO’s contents.

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.

In order to research specific problems regarding pancreatic cancer patients, it is necessary to categorize the population of patients who suffer from cancer into cohorts. Subpopulations can be defined based on demographic characteristics, treatment course, goals of care, etc.

The Internet is an important supporting system that helps to make the LHS possible.

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“My name is Clara. I am a poet and am currently working on my second anthology. A couple of months ago, I was diagnosed with an inoperable form of pancreatic cancer.”

“Hi. I’m Dr. Earl Green. I am Clara’s oncologist. I recently started her on the drug gemcitabine. I am interested in engaging with the Learning Health System to create the best possible care plan for Clara.”

“Hello. I’m Wendy, Clara’s sister. I’ve been going along with Clara to doctors visits and helping her keep track of her treatment.”

For the LHS, population cohorts are defined by demographic characteristics, disease state, geography, and time.

“Clara’s cancer is inoperable and she is currently receiving the drug gemcitabine. In the instance that the cancer does not respond to gemcitabine, there is no consensus on a subsequent treatment course.”

“Hi. I'm Dr. Muriel Waters. I work for the National Committee on Pancreatic Cancer Research and Treatment. The committee coordinates strategies surrounding pancre-atic cancer treatment. One of our interests is to develop an evidence-based guideline to treat patients who have inoperable and refractory pancreatic cancer. This means the cancer cannot be removed surgically and the cancer is not responding to known drug treatments like gemcitabine. But we do not have enough data to create such a guideline.

Members of this committee, which include specialists from across the country, have agreed to work together on this problem by collecting and analyzing data about pancreatic cancer from institutions using LHS and then disseminating the knowledge learned via the LHS.”

A Learning Health System facilitates the work of Communities of Interest (COI) which come together to address a specific problem. The LHS allows COIs to openly share data about past and present cases both within and outside the community, including data about lifestyle issues related to a disease or health problem. A COI discovers and validates new knowledge, curates, and delivers it to the world via the LHS.

In this story, the National Committee on Pancreatic Cancer Research and Treatment make up the COI; their specific problem involves learning how to best treat patients who have inoperable and refractory pancreatic cancer.

“We currently collect detailed, standardized case reports on patients who have inoperable and refractory pancreatic cancer through the LHS. While our goal is to collect enough data to define best practice guidelines for pancreatic cancer care, we are at an early stage of gathering and organizing case data and do not have enough case data yet to make statistical claims.”

The LHS supports ongoing data collection, which can draw upon a number of sources, include data from electronic health record systems. All data can be traced back to their original sources for auditing, validating, and refining data collection and entry methods.

“I reviewed Clara’s most recent imaging studies and lab tests which indicate that there has been little response to the drug gemcitabine. Next, we have to decide on the follow-up treatment course. There is no strong evidence for what follow-up would give Clara the best outcomes. The LHS can help determine if there is any knowledge about which follow-up treatments have worked best for patients like Clara.”

An application can be used to query and access the latest knowledge from the COI and other sources that focus on how to treat patients like Clara.

“Our committee regularly aggregates and analyzes pancreatic cancer data. We use an application to share our findings with the LHS.”

The LHS allows the COI to create and manage the knowledge resources they obtain in the form of a Digital Knowledge Object (DKO). Once the COI authors a DKO, others with appropriate access can query the LHS at any time and read the DKO’s contents.

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.

In order to research specific problems regarding pancreatic cancer patients, it is necessary to categorize the population of patients who suffer from cancer into cohorts. Subpopulations can be defined based on demographic characteristics, treatment course, goals of care, etc.

The Internet is an important supporting system that helps to make the LHS possible.

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The morning after the Tumor Board meeting, Dr. Green meets with Clara to discuss the trial drug options.

“Although these trials don’t guarantee results, they both aim to increase survival beyond the standard of care among patients like you. You are free to consider either or none of these trials.”

Dr. Green thoroughly explains the cost and side effects of each treatment option. “I’ll give you some time to think it over.” He steps out to give Clara and Wendy some privacy.

A query to the LHS can also return a tool to estimate cost to the patient and hospital for the various treatment plans to assist with the allocation of resources.

Even though Wendy is hesitant about her sister’s participation, Clara insists, “I wouldn’t have gotten this far if it weren’t for patients before me participating in trials. If at any point the drug starts affecting my ability to write, I will stop the trial.” Clara calls Dr. Green back and tells him that she is willing to try Drug X.

To search for more information on treating patients with inoperable pancreatic cancer that are not responding to gemcitabine treatment (i.e. refractory pancreatic cancer), Dr. Green submits a request into the LHS. He receives the following categories of responses packaged in DKOs:

Consensus guidelines, Completed studies, Ongoing studies, Case Series/Reports, and patients at my hospital.

An application is used to conduct queries for the LHS.

Dr. Green meets with Clara and Wendy to discuss the hospital’s upcoming Tumor Board meeting during which Clara’s case, as well as other patient cases, will be reviewed and individualized goals for their treatment will be established. Clara expresses that whatever treatment she gets, she would like to maintain clear-headedness in order to finish her poetry anthology.

Dr. Green also asks if Clara would like to participate in the LHS: “Clara, the National Committee for Pancreatic Cancer Research and Treatment is working on creating a guideline for treating patients like yourself. However, they do not have enough data. Would you like to anonymously share your patient information with this group?”

Meanwhile, doctors and researchers continue to add data from Clara’s subpopulation to the LHS and the cohort grows to 11 patients.

The Tumor Board is a regular, multi-disciplinary meeting convened to determine and follow treatment courses for patients. In this story, the Tumor Board includes an invited guest from the Community of Interest (COI), Dr. Muriel Waters.

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.

“I am happy that Clara decided to share her data with the LHS. Also, now that I know that Clara wants to maintain a clear mind while we manage her disease, I will be sure to discuss this when presenting her case to the Tumor Board.”

During the meeting, Dr. Green shares the specific data analyses he had found from the LHS regarding Drug X and Drug Y. The preliminary results show that Drug X had an increased risk of marrow suppression and Drug Y had a risk of dizziness and confusion.

After reviewing the information presented by Dr. Green regarding Clara’s case, the Tumor Board expresses favor for Drug X, given the preferences Clara communicated to Dr. Green. Once all patient cases on the agenda are addressed at the Tumor Board meeting, the team breaks to carry out the various care plans or inform patients of their options.

“I am eager to know the outcome of the Tumor Board meeting and learn more about my care plan.”

Time: Day 1 Time: Day 6Time: Day 3 Time: Day 7

LHS presents to Dr. Green DKOs that contain possible treatment courses that are ranked by outcomes and the quality of evidence. Treatment course ranking may use criteria such as disease stage, disease status, and number of partici-pants in each treatment course.

“I looked at the therapy options for pancreatic cancer that did not respond to gemcitabine. I verified that there were no consensus guidelines on therapy additions to gemcitabine. I turned to the list of clinical trials response from my query and because no completed trials yielded promising results, I reviewed ongoing trials. I found that Clara was eligible to participate in a trial involving Drug X and another trial involving Drug Y.”

“Since it’s anonymous, and I will benefit from these guidelines, I will share my information with the LHS.”

With Clara’s informed consent, information of her treatment course, as well as her history of environmen-tal/occupational exposures, are linked to the LHS. Clara is added to the subpopulation group of patients with inoperable and refractory pancreatic cancer in the LHS.

Query results in the LHS can be prioritized based on authority of the source, amount of evidence, and recency of knowledge.

Time: Day 1 Time: Day 3

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The morning after the Tumor Board meeting, Dr. Green meets with Clara to discuss the trial drug options.

“Although these trials don’t guarantee results, they both aim to increase survival beyond the standard of care among patients like you. You are free to consider either or none of these trials.”

Dr. Green thoroughly explains the cost and side effects of each treatment option. “I’ll give you some time to think it over.” He steps out to give Clara and Wendy some privacy.

A query to the LHS can also return a tool to estimate cost to the patient and hospital for the various treatment plans to assist with the allocation of resources.

Even though Wendy is hesitant about her sister’s participation, Clara insists, “I wouldn’t have gotten this far if it weren’t for patients before me participating in trials. If at any point the drug starts affecting my ability to write, I will stop the trial.” Clara calls Dr. Green back and tells him that she is willing to try Drug X.

To search for more information on treating patients with inoperable pancreatic cancer that are not responding to gemcitabine treatment (i.e. refractory pancreatic cancer), Dr. Green submits a request into the LHS. He receives the following categories of responses packaged in DKOs:

Consensus guidelines, Completed studies, Ongoing studies, Case Series/Reports, and patients at my hospital.

An application is used to conduct queries for the LHS.

Dr. Green meets with Clara and Wendy to discuss the hospital’s upcoming Tumor Board meeting during which Clara’s case, as well as other patient cases, will be reviewed and individualized goals for their treatment will be established. Clara expresses that whatever treatment she gets, she would like to maintain clear-headedness in order to finish her poetry anthology.

Dr. Green also asks if Clara would like to participate in the LHS: “Clara, the National Committee for Pancreatic Cancer Research and Treatment is working on creating a guideline for treating patients like yourself. However, they do not have enough data. Would you like to anonymously share your patient information with this group?”

Meanwhile, doctors and researchers continue to add data from Clara’s subpopulation to the LHS and the cohort grows to 11 patients.

The Tumor Board is a regular, multi-disciplinary meeting convened to determine and follow treatment courses for patients. In this story, the Tumor Board includes an invited guest from the Community of Interest (COI), Dr. Muriel Waters.

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.

“I am happy that Clara decided to share her data with the LHS. Also, now that I know that Clara wants to maintain a clear mind while we manage her disease, I will be sure to discuss this when presenting her case to the Tumor Board.”

During the meeting, Dr. Green shares the specific data analyses he had found from the LHS regarding Drug X and Drug Y. The preliminary results show that Drug X had an increased risk of marrow suppression and Drug Y had a risk of dizziness and confusion.

After reviewing the information presented by Dr. Green regarding Clara’s case, the Tumor Board expresses favor for Drug X, given the preferences Clara communicated to Dr. Green. Once all patient cases on the agenda are addressed at the Tumor Board meeting, the team breaks to carry out the various care plans or inform patients of their options.

“I am eager to know the outcome of the Tumor Board meeting and learn more about my care plan.”

Time: Day 1 Time: Day 6Time: Day 3 Time: Day 7

LHS presents to Dr. Green DKOs that contain possible treatment courses that are ranked by outcomes and the quality of evidence. Treatment course ranking may use criteria such as disease stage, disease status, and number of partici-pants in each treatment course.

“I looked at the therapy options for pancreatic cancer that did not respond to gemcitabine. I verified that there were no consensus guidelines on therapy additions to gemcitabine. I turned to the list of clinical trials response from my query and because no completed trials yielded promising results, I reviewed ongoing trials. I found that Clara was eligible to participate in a trial involving Drug X and another trial involving Drug Y.”

“Since it’s anonymous, and I will benefit from these guidelines, I will share my information with the LHS.”

With Clara’s informed consent, information of her treatment course, as well as her history of environmen-tal/occupational exposures, are linked to the LHS. Clara is added to the subpopulation group of patients with inoperable and refractory pancreatic cancer in the LHS.

Query results in the LHS can be prioritized based on authority of the source, amount of evidence, and recency of knowledge.

Time: Day 1 Time: Day 3

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Dr. Green corresponds with the doctor who is heading the trial for Drug X to learn more details about the trial. After going over the logistics with Dr. Green, Clara is reassured and officially consents to partici-pating in the Drug X trial. Through the LHS, Dr. Green can securely send her information into the trial and formally enroll Clara.

Clara passes the screening for the study and the necessary clearance from insurance to start Drug X. Clara and Dr. Green’s team receive the treatment course for Drug X via a customized DKO, which plugs into Clara’s patient record.

“I have been following my treatment course closely for the past couple of months. But I still have not been meeting my milestones as quickly as expected, so the doctors have made modifications to my plan.”

Since enrolling Clara, Dr. Green has enrolled two more of his patients with inoperable and refractory pancreatic cancer in the LHS.

Even though the cohort of patients like Clara has grown in the LHS since she was initially diagnosed, there is still not enough evidence-based knowledge about how to address Clara’s progressed pancreatic cancer. Sadly, her condition quickly begins to worsen.

Due to the progression of her cancer, the disease claims Clara’s life, but not before she is able to finish her poetry anthology.

Wendy relays to Dr. Green, “Clara was relieved that she was able to maintain her mental clearness to finish her work. I could tell that my sister felt more at peace knowing that she was able to share her poetry with the world.”

Patients with inoperable and refractory pancreatic cancer are benefitting from the knowledge that the COI generates and disseminates via the LHS, and thus also consent for their information to be shared on the LHS.

Large cohorts can sometimes be quickly studied using heterogeneous data from many sources and organiza-tions. To answer the question of interest to the COI, data has been gathered about Clara’s cohort in a relatively short period of time.

The National Committee on Pancreatic Cancer Research and Treatment does not have enough data to make a guideline yet, so they continue to collect data via the LHS.

“Although the cohort size has grown five times its previous size, there is still not enough data to support a guideline for treating refractory pancreatic cancer.”

More doctors ask their patients to participate in the LHS, and the number of patients like Clara begins to grow. A larger sample size of 50 patients helps doctors make more informed decisions.

The LHS can track how the treatment is meeting or missing Clara’s milestones and the team can modify the treatment plan accordingly. Such modifications update the DKO that characterizes the plan.

Time: Day 7 Time: Day 90Time: Day 75 Time: Day 100Time: Day 9 Time: Day 60

Clara’s treatment plan is divided into steps and milestones that are shared across the entire care team. Clara’s status can be evaluated as it relates to these target outcomes.

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Dr. Green corresponds with the doctor who is heading the trial for Drug X to learn more details about the trial. After going over the logistics with Dr. Green, Clara is reassured and officially consents to partici-pating in the Drug X trial. Through the LHS, Dr. Green can securely send her information into the trial and formally enroll Clara.

Clara passes the screening for the study and the necessary clearance from insurance to start Drug X. Clara and Dr. Green’s team receive the treatment course for Drug X via a customized DKO, which plugs into Clara’s patient record.

“I have been following my treatment course closely for the past couple of months. But I still have not been meeting my milestones as quickly as expected, so the doctors have made modifications to my plan.”

Since enrolling Clara, Dr. Green has enrolled two more of his patients with inoperable and refractory pancreatic cancer in the LHS.

Even though the cohort of patients like Clara has grown in the LHS since she was initially diagnosed, there is still not enough evidence-based knowledge about how to address Clara’s progressed pancreatic cancer. Sadly, her condition quickly begins to worsen.

Due to the progression of her cancer, the disease claims Clara’s life, but not before she is able to finish her poetry anthology.

Wendy relays to Dr. Green, “Clara was relieved that she was able to maintain her mental clearness to finish her work. I could tell that my sister felt more at peace knowing that she was able to share her poetry with the world.”

Patients with inoperable and refractory pancreatic cancer are benefitting from the knowledge that the COI generates and disseminates via the LHS, and thus also consent for their information to be shared on the LHS.

Large cohorts can sometimes be quickly studied using heterogeneous data from many sources and organiza-tions. To answer the question of interest to the COI, data has been gathered about Clara’s cohort in a relatively short period of time.

The National Committee on Pancreatic Cancer Research and Treatment does not have enough data to make a guideline yet, so they continue to collect data via the LHS.

“Although the cohort size has grown five times its previous size, there is still not enough data to support a guideline for treating refractory pancreatic cancer.”

More doctors ask their patients to participate in the LHS, and the number of patients like Clara begins to grow. A larger sample size of 50 patients helps doctors make more informed decisions.

The LHS can track how the treatment is meeting or missing Clara’s milestones and the team can modify the treatment plan accordingly. Such modifications update the DKO that characterizes the plan.

Time: Day 7 Time: Day 90Time: Day 75 Time: Day 100Time: Day 9 Time: Day 60

Clara’s treatment plan is divided into steps and milestones that are shared across the entire care team. Clara’s status can be evaluated as it relates to these target outcomes.

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More and more patients with inoperable and refractory pancreatic cancer consent to share their information in the LHS.

“We are so excited that the number of patients like Clara has quickly grown to a size which provides enough data for us to create an evidence-based guideline!”

This new guideline is stored within a DKO whichis disseminated and made available to the LHS community. Now, patients like Clara will receive evidence-based treatment that is most effective and efficient for them.

The population of patients like Clara continues to grow and the additional knowledge drives continuous improvement of the guidelines.

“Due to continuous cycles of knowledge generation and dissemination, each year we are able to create more tailored guidelines and improve practice of how we treat inoperable and refractory pancreatic cancer patients, giving them the best chance of survival and higher quality of life.

We are grateful to all of the patients that have contributed to the LHS so far—it is their information that allows continuous improvement in the lives of future patients diagnosed with pancreatic cancer and increases our chances of finding a cure.”

Although Clara passed away before she could benefit from the guidelines created by the Community of Interest with the vital support of the LHS, her contribution to the LHS means that her health information continues to play a major role, even after her untimely death, in improving the chance of survival of newly diagnosed patients.

“In just 15 months, we were able to develop a treatment guideline for inoperable and refractory pancreatic cancer.”

Time: 15 Months Time: 15 Months Time: 24 Months

LHS lowers the barriers to generating, accessing, and implementing knowledge needed for treating inoperable and refractory pancreatic cancer. Thus, the standard of care advances more rapidly.

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More and more patients with inoperable and refractory pancreatic cancer consent to share their information in the LHS.

“We are so excited that the number of patients like Clara has quickly grown to a size which provides enough data for us to create an evidence-based guideline!”

This new guideline is stored within a DKO whichis disseminated and made available to the LHS community. Now, patients like Clara will receive evidence-based treatment that is most effective and efficient for them.

The population of patients like Clara continues to grow and the additional knowledge drives continuous improvement of the guidelines.

“Due to continuous cycles of knowledge generation and dissemination, each year we are able to create more tailored guidelines and improve practice of how we treat inoperable and refractory pancreatic cancer patients, giving them the best chance of survival and higher quality of life.

We are grateful to all of the patients that have contributed to the LHS so far—it is their information that allows continuous improvement in the lives of future patients diagnosed with pancreatic cancer and increases our chances of finding a cure.”

Although Clara passed away before she could benefit from the guidelines created by the Community of Interest with the vital support of the LHS, her contribution to the LHS means that her health information continues to play a major role, even after her untimely death, in improving the chance of survival of newly diagnosed patients.

“In just 15 months, we were able to develop a treatment guideline for inoperable and refractory pancreatic cancer.”

Time: 15 Months Time: 15 Months Time: 24 Months

LHS lowers the barriers to generating, accessing, and implementing knowledge needed for treating inoperable and refractory pancreatic cancer. Thus, the standard of care advances more rapidly.

EPILOGUE

WHAT WAS LEARNED WHEN, AND BY WHOM?Pancreatic cancer is the fourth leading cause of cancer death in the United States. In this story, a Community of Interest forms to learn about the management and treatment of inoperable and refractory pancreatic cancer. Refractory pancreatic cancer is a dire condition associated with very low rates of survival. There is hope that a Learning Health System (LHS) can accelerate learning about how best to manage and treat the disease.

At the outset, the data available to inform treatment and management decisions for refractory pancreatic cancer are sparse. However, as the Community of Interest begins to use the LHS to collect data about past cases of refractory pancreatic cancer, the problem of sparse data starts to subside.

In short order, oncologist Dr. Earl Green is able to retrieve and review relevant clinical decisions and results for several patients from across the United States who are similar to his patient, Clara.

His review of similar cases permits Dr. Green to provide informed advice to Clara regarding follow-up treatments for her case of inoperable and refractory pancreatic cancer.

Dr. Green tells Clara about the work of the Community of Interest and their need for more data from patients like her to be able to create an evidence-based guideline. Clara consents to share her anonymous clinical data for the benefit of other patients with similar conditions. These data will be used by the Community of Interest to further investigate what causes pan-creatic cancer and support the generation of evidence-based guidelines for best practice treat-ment of pancreatic cancer.

When Clara’s case is reviewed at the Tumor Board meeting, Dr. Green is able to share the specific data analyses he had found from the LHS regarding several drug trials and specifically discuss the possibility of Clara’s enrollment in one of the trials.

Based on the data shared amongst the Community of Interest and the guidance received at the Tumor Board meeting, Dr. Green, Clara, and Clara’s sister together decide to enroll Clara in a trial for Drug X.

Thanks to the knowledge shared via the LHS, Clara was able to receive care during which she could maintain mental clarity to reach her goal of completing her poetry anthology. Clara’s patient information contributed to the virtuous learning cycle of the LHS, which resulted in establishing the much-needed guidelines for treating inoperable and refractory pancreatic cancer. The virtual learning cycle around this guideline remains active and drives continuous improvement to the guidelines, improving care and chances of finding a cure.

This story is a stylized one, and it is sad too, but it also represents a new way to share data in a not-so-distant future, and drive better care, faster. The learning made possible by the system-atic sharing of information in a LHS promises to usher in a new era of health care excellence.

This story of health care improvement exempli-fies the emergent and transformative power of a Learning Health System. 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, 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

and School of Information• Task Force Co-Chair - Policy: Julia Adler-Milstein, School of Information

and School of Public Health• Task Force Co-Chair - Policy: Daniel Lee, School of Public Health

AuthorsThis storyboard was authored by Learning Health System initiatives team members Lawrence Chang, Allen Flynn, Anna Vantsevich, Melissa Bruno, and Kathleen Ludewig Omollo, with graphic design by Aimee Andrion.

CopyrightImages 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; tumor board by Dan Hetteix, CC BY. People images from Shutterstock. Other images created by Aimee Andrion.

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