It was unthinkable not long ago that a biologist or paleontologist would be at the same conference as an astrophysicist.
Now we have accumulated so much data in each of these branches of science as it relates to origins that we have learned that no one discipline can answer questions of origins alone—Neil deGrasse Tyson
What if I could guide you toward a more robust measure of influence at the point of care? What does the health care provider need at the moment a decision regarding treatment, device recommendation, or benefit/risk probabilities? I will admit that the current landscape does not, at least from the funding perspective, lend itself to innovation in eliminating barriers to systems thinking. But we have to engage beyond multiple choice questions or improbable case studies.
The US healthcare system is not a centralized entity. This is relevant because for example, if social services are provided in the absence of provider or system reimbursement the benefit may not be “realized” within the system that generated the intervention. A patient prescribed a drug requiring refrigeration may lack reliable housing for example. As medical education professionals, the responsibility should be to integrate disparate resources and influencers of health when providing point of care solutions at the individual or community level.
I am a recovering continuing medical education (CME) professional. What does that mean? In CME we have a tendency to admire the problem. We create strategies around weak foundational data frameworks. How many articles do you read about pre- and post-test questions? How many do you read about abandoning the status quo and joining the big kids at the data table? How many of us work industry adjacent outside of our own bubbles or sure thing funding mechanisms?
Are providers using ICD-10-CM Codes that reflect potential health hazards related to socioeconomic and psychosocial circumstances? These Z codes are quite detailed. For example each one of the codes below has a deeper level of granularity:
Z55: Problems related to education and literacy
Z56: Problems related to employment and unemployment
Z57: Occupational exposure to risk factors
Z59: Problems related to housing and economic circumstances
Z60: Problems related to social environment
Z62: Problems related to upbringing
Z63: Other problems related to primary support group, including family circumstances
Z64: Problems related to certain psychosocial circumstances
Z65: Problems related to other psychosocial circumstances
How confident are we in the quality of data if we only capture partial patient information? Are we providing patient centered care if we ignore the patient environment?
The US healthcare system is not a centralized entity. This is relevant because for example, if social services are provided in the absence of provider or system reimbursement the benefit may not be “realized” within the system that generated the intervention. A patient prescribed a drug requiring refrigeration may lack reliable housing for example. As medical education professionals, the responsibility should be to integrate disparate resources and influencers of health when providing point of care solutions at the individual or community level.
I am a recovering continuing medical education (CME) professional. What does that mean? In CME we have a tendency to admire the problem. We create strategies around weak foundational data frameworks. How many articles do you read about pre- and post-test questions? How many do you read about abandoning the status quo and joining the big kids at the data table? How many of us work industry adjacent outside of our own bubbles or sure thing funding mechanisms?
Are providers using ICD-10-CM Codes that reflect potential health hazards related to socioeconomic and psychosocial circumstances? These Z codes are quite detailed. For example each one of the codes below has a deeper level of granularity:
Z55: Problems related to education and literacy
Z56: Problems related to employment and unemployment
Z57: Occupational exposure to risk factors
Z59: Problems related to housing and economic circumstances
Z60: Problems related to social environment
Z62: Problems related to upbringing
Z63: Other problems related to primary support group, including family circumstances
Z64: Problems related to certain psychosocial circumstances
Z65: Problems related to other psychosocial circumstances
How confident are we in the quality of data if we only capture partial patient information? Are we providing patient centered care if we ignore the patient environment?
Systems thinking describes what happens in the gaps between industries. As Neil deGrasse Tyson states in the opening quote, we are data adjacent to many healthcare stakeholders—are we leaving insights behind? Have you heard that physicists and engineers as well as liberal arts folks are deliberating on tangible problems in healthcare and analytics?
Although important, technical skills and statistics are not a substitute for a full breadth of competencies such as health economics, health policy, or subject matter expertise in clinical medicine. As CME professionals, we require sector knowledge in addition to data competency. But if we are unable to communicate effectively to improve numeracy in our varied disciplines—the buck stops with us.
Leyla Acaroglu is a System Thinker (and TedTalk speaker) and provides a wonderful article on Medium, Tools for Systems Thinkers: The 6 Fundamental Concepts of Systems Thinking. I recommend the deeper dive.
Although important, technical skills and statistics are not a substitute for a full breadth of competencies such as health economics, health policy, or subject matter expertise in clinical medicine. As CME professionals, we require sector knowledge in addition to data competency. But if we are unable to communicate effectively to improve numeracy in our varied disciplines—the buck stops with us.
Leyla Acaroglu is a System Thinker (and TedTalk speaker) and provides a wonderful article on Medium, Tools for Systems Thinkers: The 6 Fundamental Concepts of Systems Thinking. I recommend the deeper dive.
Where is the data?
Data is big. We keep hearing about big data but what we really need is wide data. Curated data thoughtfully prepared to include more variables—otherwise we aggregate into big buckets and lose heterogeneity.
Think of an interconnected adaptive process. Can we learn to access nonproprietary data in order to create a real system thinking initiative? Transdisciplinary thinking—where we are on the edge. It isn’t linear—it is actually cinematic. We need to identify the real needs and gaps in healthcare. Let me remind you of an adage in the analytics space. If all you measure is the easy thing? It is usually the wrong thing.
When information is missing what do we substitute—how do we close the circle? Cognitive biases fill the void. The easy thing or last thing we did directly informs what we do next. This makes sense from an ancestral perspective, when fighting for survival, but in our modern critical thinking environment we are stuck at the limits of our own consciousness.
Data is big. We keep hearing about big data but what we really need is wide data. Curated data thoughtfully prepared to include more variables—otherwise we aggregate into big buckets and lose heterogeneity.
Think of an interconnected adaptive process. Can we learn to access nonproprietary data in order to create a real system thinking initiative? Transdisciplinary thinking—where we are on the edge. It isn’t linear—it is actually cinematic. We need to identify the real needs and gaps in healthcare. Let me remind you of an adage in the analytics space. If all you measure is the easy thing? It is usually the wrong thing.
When information is missing what do we substitute—how do we close the circle? Cognitive biases fill the void. The easy thing or last thing we did directly informs what we do next. This makes sense from an ancestral perspective, when fighting for survival, but in our modern critical thinking environment we are stuck at the limits of our own consciousness.
Opioid Crisis
The opioid epidemic and crisis serves as an illustrative example of why a 360 perspective informs and contextualizes practical challenges in managing the opioid crisis. As CME professionals we are aware of the desire to limit educational interventions to ~ 3 learning objectives. We carefully craft them and cross our fingers that they are truly actionable, meaningful, and measurable. But hold on—are we narrowing the scope?
Maybe we need to be open to discovery. I call this the 4th objective. It is unknowable in the early stages. It is slowly revealed by searching outside of our bubbles. Silent listening in social media, policies with downstream implications on health and economics, industry databases, we need to be vigilant and yes—very curious.
Research from a variety of sources and identify gaps at the intersections or edges. Reporting from only a single source distorts the outcomes and importantly the potential solutions. Deeper research into the opioid crisis also reveals the role of the dark web in propagating the illegal sale of opioids. You can imagine if the focus on potential solutions is too narrow we are just playing wack-a-mole. The dark web is disturbing so I will not share data but one glance and it is obvious that we are bringing a sling shot to a gun fight.
An important tool I rely on for population and provider level insights, Public Insight allows hypothesis generation at a user selected level of granularity.
The opioid epidemic and crisis serves as an illustrative example of why a 360 perspective informs and contextualizes practical challenges in managing the opioid crisis. As CME professionals we are aware of the desire to limit educational interventions to ~ 3 learning objectives. We carefully craft them and cross our fingers that they are truly actionable, meaningful, and measurable. But hold on—are we narrowing the scope?
Maybe we need to be open to discovery. I call this the 4th objective. It is unknowable in the early stages. It is slowly revealed by searching outside of our bubbles. Silent listening in social media, policies with downstream implications on health and economics, industry databases, we need to be vigilant and yes—very curious.
Research from a variety of sources and identify gaps at the intersections or edges. Reporting from only a single source distorts the outcomes and importantly the potential solutions. Deeper research into the opioid crisis also reveals the role of the dark web in propagating the illegal sale of opioids. You can imagine if the focus on potential solutions is too narrow we are just playing wack-a-mole. The dark web is disturbing so I will not share data but one glance and it is obvious that we are bringing a sling shot to a gun fight.
An important tool I rely on for population and provider level insights, Public Insight allows hypothesis generation at a user selected level of granularity.
The opioid crisis serves as an example of how building a data informed approach to generating hypotheses or need assessments is a powerful blueprint for high value education.
Why use an integrative data framework? Data generated for hypothesis generating should come from one database—and confirmatory analyses in others. It isn’t enough to access one relational database for both business needs.
Why are we evaluating educational outcomes in bar charts? Perhaps we have reached the limits of linear improvement models. Instead of building need assessments to be fool-proof perhaps we need a little vulnerability. It might be time to generate a hypothesis requiring a new direction.
We can only connect the dots we collect. (Economics, policy, clinical medicine)
As a community, CME professionals need to join discussions of value in healthcare with a bigger toolbox. The term value for instance will always need context. Value for whom? The patient? The provider? The health system? The payer? It should be no surprise that a collection of multiple choice questions or Likert questions are not going to render the important data or curated insights.
Discussions in health economics include robust analytic tools and methodologies that include discrete choice experiments allowing preferences to be measured based on relative importance of a variety of variables or attributes.
Patient participation in their healthcare decision making is touted as the new blockbuster drug. Preference-based measures require inclusion of utility measures such as quality of life, rating scales, standard gamble, time tradeoffs and willingness to pay need to be integrated into tools we develop with patients—not solely for patients.
The healthcare system is complex. Efforts to provide education or insights into quality improvement or accessibility to care require industry adjacent best practices. As a frequent traveler and conference attendee/speaker it is not uncommon to engage design thinkers, engineers, statisticians, economists, as well as health law professionals in discussions that only a few years ago were siloed and “solved” from within.
“It is difficult to get a man to understand something, when his salary depends upon his not understanding it!”—Upton Sinclair
Isolating healthcare from the interconnectedness of a community, society, or population happens to our own detriment. A system approach requires intervention at the societal, industrial, and ecological level. Dynamic problems require dynamic solutions…
Why use an integrative data framework? Data generated for hypothesis generating should come from one database—and confirmatory analyses in others. It isn’t enough to access one relational database for both business needs.
Why are we evaluating educational outcomes in bar charts? Perhaps we have reached the limits of linear improvement models. Instead of building need assessments to be fool-proof perhaps we need a little vulnerability. It might be time to generate a hypothesis requiring a new direction.
We can only connect the dots we collect. (Economics, policy, clinical medicine)
As a community, CME professionals need to join discussions of value in healthcare with a bigger toolbox. The term value for instance will always need context. Value for whom? The patient? The provider? The health system? The payer? It should be no surprise that a collection of multiple choice questions or Likert questions are not going to render the important data or curated insights.
Discussions in health economics include robust analytic tools and methodologies that include discrete choice experiments allowing preferences to be measured based on relative importance of a variety of variables or attributes.
Patient participation in their healthcare decision making is touted as the new blockbuster drug. Preference-based measures require inclusion of utility measures such as quality of life, rating scales, standard gamble, time tradeoffs and willingness to pay need to be integrated into tools we develop with patients—not solely for patients.
The healthcare system is complex. Efforts to provide education or insights into quality improvement or accessibility to care require industry adjacent best practices. As a frequent traveler and conference attendee/speaker it is not uncommon to engage design thinkers, engineers, statisticians, economists, as well as health law professionals in discussions that only a few years ago were siloed and “solved” from within.
“It is difficult to get a man to understand something, when his salary depends upon his not understanding it!”—Upton Sinclair
Isolating healthcare from the interconnectedness of a community, society, or population happens to our own detriment. A system approach requires intervention at the societal, industrial, and ecological level. Dynamic problems require dynamic solutions…
Further reading:
Systems Thinking and Modeling for Public HealthAm J Public Health. 2006 March; 96(3): 403–405. Practice Scott J. Leischow, PhD and Bobby Milstein, MPH
Obstacles to the Prescription and Use of Opioids
Rachel S. Wallwork, BA, Fallon E. Chipidza, BA, and Theodore A. Stern, MD
Prim Care Companion CNS Disord. 2016; 18(1): 10.4088/PCC.15f01900.
Published online 2016 Feb 18. doi: 10.4088/PCC.15f01900
A list of non-proprietary data resources:
Systems Thinking and Modeling for Public HealthAm J Public Health. 2006 March; 96(3): 403–405. Practice Scott J. Leischow, PhD and Bobby Milstein, MPH
Obstacles to the Prescription and Use of Opioids
Rachel S. Wallwork, BA, Fallon E. Chipidza, BA, and Theodore A. Stern, MD
Prim Care Companion CNS Disord. 2016; 18(1): 10.4088/PCC.15f01900.
Published online 2016 Feb 18. doi: 10.4088/PCC.15f01900
A list of non-proprietary data resources:
- Social Vulnerability Index
- CDC Wonder
- FDA Adverse Events Reporting System (FAERS)
- Substance Abuse & Mental Health Data Archive (SAMHDA)
- Substance Abuse & Mental Health Services Administration (SAMHSA)
- National Survey on Drug Use and Health
- Prescriber Check-up
- International Narcotics Control Board
- Public Insight
- HCUP (state and nationwide databases)
- National Center for Health Statistics
- US Census Bureau
- American Community Survey
