By Dr. Ashley Sens, CMO Woodland Healthcare

“Teaching is more than imparting knowledge; it is inspiring change. Learning is more than absorbing facts; it is acquiring understanding.”

– William Arthur Ward, Author

In my role as Chief Medical Officer for Woodland Memorial Hospital, I am sometimes asked to meet with patients whom physicians and nurses are concerned may decide to leave the hospital prior to the treating team feeling they are safe to discharge. When I get involved, it is typically because the team is concerned a patient-directed discharge could lead to the patient’s death. The stakes are very high for everyone.

I want to share with you an encounter I had with such a patient who taught me one of the most important lessons of my career.

Typically, the treating team and I would emphasize with these patients the dangers to them if they leave the hospital: “You are making an unsafe choice.” “You could die.”

However, this particular patient (we will call her Rose) helped me hear what those words sound like and feel like from the perspective of someone who has lived a life of severe trauma. The “aha” moment I experienced with Rose came when I realized Rose did not feel she was worthy of the care we were providing to her. All the threats to her safety and wellbeing were actually feeding into her impulse to leave – to run away, because, after all, what we were threatening is what she felt she deserved.

This insight did not happen because Rose told me explicitly she was not worthy of our care. The “aha” happened when I listened to her talk about her life and watched her squirm in the hospital bed. That was when I realized just how uncomfortable she must feel. For those who have lived a life of repeated and ongoing trauma, the trauma itself can feel more comfortable than safety, care, concern, or love – because it is familiar, and because their life experiences tell them they are not worthy of love.

So I stopped telling her she could die. I told her how much we care about her. I told her she was worthy of our care, that she deserved to be safe, and that she deserved to be well.

“We care about you.”

“You are worthy of our care.”

“Please stay with us and allow us to care for you. You deserve this.”

“You are worthy. You deserve to be well.”

Rose stayed.

— Dr. Ashley Sens, CMO, Woodland Memorial Hospital

By Robert L. Moore, MD, MPH, MBA, Chief Medical Officer

“We didn’t stratify the data by American Indian/Alaska Native, because the numbers were too small.”

– DHCS webinar, reviewing quality data stratified by race

Two years ago, Partnership first stratified Quality Outcome data based on the race/ethnicity we received from DHCS. As noted in prior newsletters, this data showed that outcomes were much worse for the self-identified American Indian/Alaska Native (AI/AN) population than for any other racial group. This prompted Partnership to launch a Tribal Engagement Strategy to build relationships with the 21 Tribal Health Centers and their associated 51 individual tribes, so that we can work together on improving health and wellness for our Tribal communities.

Two months ago, while preparing a presentation for the Medi-Cal Managed Care Advisory Group about Partnership’s Tribal Health Liaison Yolanda Latham, I was looking through race/ethnicity data on our members, and comparing it to the official California Census data, and discovered something very concerning: The number of AI/AN members enrolled in Partnership seemed very low. After a little digging (details below), I discovered that the magnitude of the undercounting is somewhere between 213% and 900%, and maybe even higher.

The reason for this is the way DHCS takes the race/ethnicity/tribal affiliation data from the official Medi-Cal application and uses an algorithm to assign a single race. The Medi-Cal application encourages individuals to choose all races that apply, in accordance with federal recommendations going back to 2000.

Page 4 of the Medi-Cal application:Page 20 of the Medi-Cal application:

The mechanism that DHCS uses to convey membership information to Partnership and other Medi[1]Cal managed care plans is a file called the 834 file or membership file. This file lists just one single race-ethnicity category per enrollee. DHCS uses an algorithm to translate the application race and ethnicity responses to this single category.

While the exact algorithm is not publicly posted, it seems likely that if an AI/AN member also identifies as Hispanic or Latino, this trumped their AI/AN status, and they were assigned a Latino ethnicity. Additionally, if an enrollee identified as both AI/AN and any other racial status, they were classified as “other” or “mixed race,” a category with poor outcomes similar to the AI/AN population, but as it is mixed with all other mixed-race individuals is completely non-actionable.

Here are three mechanisms used to estimate the scope of this undercounting:

1. Census Data

One way to estimate the scope of undercounting is to compare the proportion the Medi-Cal enrolled population identified as AI/AN compared to California census data on AI/AN ethnicity.

Official Medi-Cal statistics show a total of 55,302 (only 0.4% of all beneficiaries) AI/AN individuals enrolled in Medi-Cal as of July 2023. (Medi-Cal Fast Facts).

In contrast, in the 2020 census, 1.6% of the California population identified as American Indian and Alaska Native race alone, and an additional 2% of the population identified as American Indian or Alaska Native in combination with some other race, for a total of 3.6% of the population categorized at AI/AN alone or in combination. Even if we assume that the proportion of the AI/AN population of California with Medi-Cal is the same as the non-Medi-Cal population (a highly unlikely assumption), Medi-Cal is undercounting the AI/AN population by as much as nine-fold. Put another way, the true number is 900% higher.

Extrapolating the scope of the undercounting based on census data, as many as 495,000 Medi-Cal beneficiaries would be categorized as AI/AN alone or in combination, instead of just 55,302.

2. American Community Survey

An analysis of the 2018 American Community Survey conducted by the National Indian Health Board estimated the California Medi-Cal population to be 242,813. An updated estimate from 2021 put the number at 330,959, or 600% higher than the official state data.

3. Tribal Health Centers

Confirmatory evidence of racial mis-categorization comes from the subset of Tribal health centers, which only allow enrolled Tribally-affiliated members to be served. Of those Medi-Cal members served at these Tribal health centers, 53% were categorized by Medi-Cal 834 data as not being AI/AN. Meaning that the true number is 213% greater than the identified AI/AN at Native-run health centers.

Extrapolating this underestimate would mean that the actual number of AI/AN members receiving Medi-Cal is about 118,000 individuals.

Why such a broad range?

The range of undercounting (from 213% to 900%) is so large, partly because the U.S. Census groups together indigenous populations from Central America (such as the Maya and Aztec), South America and Canada into its totals. Of these groups, those who identify as indigenous from Central America are large and growing, resulting in a shift from the Latino category to the indigenous/AI/AN category. In contrast, Indigenous persons from outside of the United States are not generally eligible to receive care at Tribal health centers that are limited to Tribal members.

The American Community Survey assesses race and ethnicity differently, in a way that likely does not include Indigenous individuals from Central America in the AI/AN count, which lowers that count relative to the census estimate.

Impact of Undercounting

Official methods of categorizing race have a centuries-long history of being built on racist assumptions and bias. While I would like to think that the algorithm decisions that led to the undercounting of the AI/AN population in Medi-Cal were not intended to harm the AI/AN population, such large-scale undercounting has several important impacts.

First, it reinforces the perception that American Indians are no longer present in California; “erasure” is the term used by American Indian scholars and activists. In fact, in the past century, erasure was an official U.S. government policy, as tribes were “terminated” in the 1950s and 1960s, children kidnapped and taken away to boarding schools to indoctrinate them into American culture. The residual evidence of erasure reflects a lack of acknowledgment and sensitivity of this historical trauma.

Second, such profoundly faulty data leads to faulty analysis of health inequities. If the racial data used to calculate rates of quality indicators is biased and faulty, then the inferences drawn by stratifying data by race are hints of the underlying reality, but any sanctions or penalties tied to reducing such inequities by any specified quantity are statistically invalid.

Lastly, such significant undercounting impacts public health prioritization based on population affected, and thus potentially impacts funding allocated proportional to the AI/AN population affected.

What should be done?

Major Tribal organizations representing health and public health policy issues have raised the problematic nature of categorization of AI/AN persons in multiple settings and give input into the newly updated 2024 OMB standards.

National organizations, especially the National Indian Health Board, have raised the issue of data incompleteness and undercounting. Some shorthand terms for the lack of sharing of accurate data about the AI/AN population is “data sovereignty” and the need to “decolonize data systems.” The National Council on Urban Indian Health issued an analysis of undercounting among Urban Indians. Other organizations that have weighed in on undercounting of AI/AN population data include the 12 regional Tribal Epidemiology Centers, and the state Tribal health organizations like the California Rural Indian Health Board.

Major changes in the new U.S. Office of Management and Budget (OMB) Standards

The Updated 2024 OMB Standards for categorizing race/ethnicity move Latino/Hispanic to be a co-equal race/ethnicity category, instead of a carved-out ethnicity category. The Middle-eastern/north African population was carved out of the White category, so there will now be 7 major race/ethnicity categories. One of which is American Indian or Alaska Native, with a box to fill in details with the following language: “Enter, for example, Navajo Nation, Blackfeet Tribe of the Blackfeet Indian Reservation of Montana, Native Village of Barrow Inupiat Traditional Government, Nome Eskimo Community, Aztec, Maya etc.”

The most concerning aspect of the new OMB standard is the list of options for handling individuals who identify more than one race/ethnicity category. The three options identified are (see page 22195):

• The “alone or in combination” approach mentioned earlier related to census data. There is some complexity to using this approach, but it substantially resolves the undercounting of the AI/AN population and should be the starting point of data sharing and equity analysis. A key feature of this approach is that the total of all categories is greater than 100%, as one individual maybe two or more categories; this requires special statistical methods to avoid errors.
• The “most frequent multiple responses” approach, in which the top combined categories are each presented with individual data. For example, in addition to each race ethnicity category alone, each combination is listed with the number of individuals. Some may be simple two-race categories (like Black-Asian), but more complex combinations are possible (like Latino-Black[1]White). This allows the most granular data analysis, and the numbers can be folded into the “alone or in combination” category. The sum of all individuals in all categories will total 100%.
• The “multiracial” approach in which any individual who chooses more than one race/ethnicity category is categorized as either “other” or “mixed.” This grouped category is impossible to analyze, so the “pure” race/ethnicity categories end up being the only way to look for health disparities. This appears to be the method currently used by DHCS, and it should be abandoned as soon as possible.

What Can DHCS Do Now?

First and foremost, DCHS should share the current detailed enrollment race/ethnicity/tribal affiliation data with all Medi-Cal Managed Care plans so they can better analyze and understand the inequities faced by their members. This could be done with a separate monthly report from DHCS and it could also be integrated into the new Medi-Cal Connect platform that DHCS is building to feed assorted supplemental data to health plans. In addition, if DHCS has separate member-level internal flags indicating Tribal affiliation or AI/AN status, from other sources, this should also be conveyed to the plans with the more complete enrollment demographic data.

This granular race/ethnicity/Tribal affiliation data will allow managed care plans to re-run our disparity analyses and release an analysis of our findings. In addition, we can pass on this information to primary care practices to give them the complete and accurate data they need to identify and address health inequities.

As DHCS plans its implementation of the new OMB race-ethnicity standards, they should convene a workgroup with representatives from the California Tribal Epidemiology Center, the California Department of Public Health, the California Rural Indian Board, the California Consortium of Urban Indian Health, and Region IX of US HHS to review the options for categorization of data, strongly considering either the “alone or in combination” approach or the “most frequent multiple responses” approach, which can be combined to create “alone or in combination” groups. These two approaches would stop the undercounting of the AI/AN population.

Finally, to stop presenting incomplete and inaccurate data about the AI/AN population, DHCS should create an internal team to review all presentations of data that is stratified by race/ethnicity to identify, correct and/or put into context the data as it relates to American Indian population. This team should be empowered to raise concerns anonymously to the DHCS Chief Health Equity officer if their concerns are not addressed.

As unintentional as it may be, the DHCS racial categorization algorithm is an example of structural racism that deserves to be addressed. With the increased emphasis on Health Equity at DHCS and CDPH, there should be a heightened sense of urgency to definitively address this issue. DHCS alignment with the OMB’s updated race and ethnicity data standards creates an opportunity to correct an issue that obscures Tribal communities and other small populations from the data.

By Robert L. Moore, MD, MPH, MBA, Chief Medical Officer

“Today we have gathered and see that the cycles of life continue. We have been given the duty to live in balance and harmony with each other and all living things. So now, we bring our minds together as one as we give greetings and thanks to each other as a people.
Now our minds are one.”

– Beginning of the Mohawk Address of Thanks and Greeting to the Natural World.

Much of the health care delivery system in Partnership’s 24-county service area is composed of not-for-profit organizations. Only two of 50 hospitals in our service region are for-profit. Our primary care network is now largely made up of not-for-profit Federally Qualified Health Centers, Tribal Health Centers, and hospital-affiliated Rural Health Centers. Many ambulance providers, hospice and home care agencies, and community-based organizations that we work with are also not-for-profit.

While private physicians, especially specialists, are theoretically for-profit entities, stagnant Medicare and Medi-Cal rates for the last 25-40 years have led those physicians who have not retired or joined a larger group, to not have much profit left. So they are functioning like not-for-profits, staying in business to serve their patients and their community.

Notable exceptions to this trend is ownership of skilled nursing facilities (SNFs) and dialysis centers. Many are privately owned, and a notable number are owned by private equity firms. In the case of small dialysis centers, for-profit entities take over these entities to create a positive cash flow by improving efficiency and leveraging economies of scale. In other cases (SNFs and some specialty groups in our region, hospitals other regions), these firms use their financial strength to gain control of organizations, work to extract value from property/buildings, and then leave town when low quality leads to facility closure, with a net loss of SNF beds in our region. In the Partnership region, private equity owned SNFs have the poorest quality scores.

On June 11, the Corporate Crimes Against Health Care Act bill was introduced in the U.S. Senate. This bill would hold corporate executives personally criminally liable for patient deaths resulting from looting of health resources by private equity firms.

It is hard to know if this punitive approach will have its intended impact on health facility ownership, or behavior of private equity firms. A softer approach in California is to require the state attorney general sign off on any proposed private equity purchase of a health care facility. Any effort in reducing the negative impact of private equity firms on health outcomes is certainly worthwhile.

The not-for-profit sector is not uniform in its focus on improving outcomes for the community. “Not-for-profit” is a tax category, not a reflection of mission or corporate culture. As a result, some corporate not-for-profits are deeply connected to their communities in a way that promotes interdependent and synergistic activities to improve health outcomes. Others may have a mission statement related to health status, but their leadership is more focused on financial returns and growth than on community engagement.

What sets community-based and community-focused organizations apart from other not-for-profits? In her book, Braiding Sweatgrass, Indigenous Wisdom, Scientific Knowledge, and the Teachings of Plants, Robin Wall Kimmerer, a botany/ecology Professor and citizen of the Potawatomi Nation, gives a series of essays on the reciprocity that’s inherent in the cultural frameworks of Tribal communities. People support the Earth/nature, and nature and the Earth supports us.

People living in communities support each other, and the community supports each of us. Community-based health plans support the clinicians, hospitals, county health departments, and other health care providers, and those community-based health centers, specialists, hospitals etc. support the health of the health plan.

When we are in tune with our mutual interdependence, then our relationship is not one of trying to extract concessions in a zero-sum-game frame-of-mind, but that of how we can help each other grow and thrive while the patients we serve also grow and thrive.

In many schools in Tribal communities, each day starts with an address expressing gratitude for our fellow human beings, all living beings, the earth around us and the sun and moon that make life possible on Earth. Although not related to the American Thanksgiving holiday, this address is often referred to as the Thanksgiving Address, and includes the repeated phrase, “Now our minds are one.”

I thank all of you for your commitment to your community and to the interdependent health care delivery system we are all nurturing together.

By Robert L. Moore, MD, MPH, MBA, Chief Medical Officer

“It is clear to me that AI will never replace physicians — but physicians who use AI will replace those who don’t.”

– Jesse Ehrenfeld, President of the American Medical Association

In the last two years, due to a big improvement in silicon chip technology (NVIDIA) and neural network design, artificial intelligence (AI) systems are able to learn from the entire knowledge of the public internet and generate new knowledge. The process of creating a new Large Language Model (LLM) AI system is akin to a parent guiding the mental and moral development of a child. Like infants absorbing information from the world around them, new LLMs absorb unstructured and unlabeled text, images or sound from the internet. After this unstructured phase, like older children who attend school, the LLMs are fine-tuned, trained for specific tasks, and boundaries of right and wrong are defined, through programming if not trial and error.

It requires human beings to ensure ethical guardrails are built in and logical, and to program additional overarching rules to make the output more accurate, useful, and innovative. The final product is analogous to an overconfident high school student with a photographic memory.

For an understandable and entertaining introduction to AI, I recommend the 3-episode series on Freakonomics Radio.

When thinking of the myriad potential of generative AI in health care, it is helpful to divide up the possibilities into two buckets:

1. Ways that AI can increase efficiency, taking tasks that take some amount of human brain processing that is fairly repetitive, teaching the generative AI how to do this quickly and accurately.
2. Improving the quality of an activity, by recognizing patterns quickly or drawing on a deeper fund of knowledge than humans can achieve.

One example of efficiency improvement is using a programmed LLM to be a computer scribe for a clinician visit. Several clinicians in the Partnership service area are starting to use these programs. Early adopters of this technology say that it reduces their time to document on patients by about 15%.

There are many digital scribes on the market; each Electronic Health Record (EHR) seems to be building one that is programmed to work best with their particular EHR. Free standing programs are more like computer dictation systems, not integrated with the electronic health record.

A deeper EHR integration of these computer scribes has begun, with many of the larger EHR systems. Psychiatrist and technology consultant Bill Weeks thinks that this will progress rapidly, such that the EHR database will still exist in the background, but an AI assistant will take the place of the typing and clicking that currently is so disheartening to clinicians.

Image recognition is a second early application of AI in medicine. The main medical example for using AI to improve quality is in the areas of radiology and pathology, where training on huge volumes of images is used to assist radiologists and pathologists with identifying concerning patterns that they may have missed if using their own eyes. An example more relevant to primary care clinicians (and approved by the FDA and covered by Partnership) is the use of AI to interpret screening retinopathy imaging done in the primary care office.

A third example of using AI for improving efficiency is to leverage AI to create a virtual “you” to educate your patients. For conditions or education that you end up repeating over and over again in your day, an AI capable of copying your voice and likeness can give high quality personalized education to your patients while they are either in the office or at home. Early pilots suggest that this will both save time and improve the chances that the advice is followed. When patients are fully aware that an avatar is speaking for you with information you approved of, your AI generated avatar can deliver more personalized and detailed education than you would have time to do in your clinical visit. Notably, many are concerned about this particular application of AI, as it can be used to mimic politicians in political campaigns.

A Generative AI (that is a LLM that is able to generate new knowledge) trained on a variety of high quality medical references could help improve the quality of preventive and curative care. If integrated with your EHR and a part of your computer scribe, such a system can remind you of preventive health care items the patient is due for and order them for you with your vocal approval.

In the area of acute care and chronic disease care, in the future LLMs will vocally and seamlessly point out allergies when a medication is ordered, note new studies showing better drug treatments, give a differential diagnosis, summarize abnormal lab results with interpretation etc. It is sort of like having a very smart third year resident in the room with you, taking notes and looking things up for you as you see your patient. It will take some training to make this happen seamlessly and accurately, but it is likely to be standard in a few short years.

Improved efficiency will help address clinician burnout. Artfully using generative AI to progressively improve quality of care may do the same; only time will tell. Either way, some training is needed, both of the generative AI itself and the humans that use it!

By Robert L. Moore, MD, MPH, MBA, Chief Medical Officer

“Yes, and…”

– A fundamental tenet of improvisation: “Yes” means to accept or affirm what is offered to you by others; “and” means to contribute something new that builds upon that offer.

 

Busy primary care clinicians talk and listen to patients for many hours a day.

Over the years, we learn how to efficiently gather the information we need to make a diagnosis, use language to convey this to the patient along with a suggested treatment plan, all while showing professionalism and empathy, and adapting our communication based on the verbal and non-verbal cues we pick up from our patients.

On our own, human beings who gain skill and proficiency in an activity find a comfortable pattern that works pretty well, and then we lock in habits related to that activity, looking for growth and challenge in other areas (or maybe not). Think about your proficiency with a second language, or with driving, or with your favorite weekend sport.

Continuing to hone any skill requires a desire to improve, a way of evaluating our current performance against a higher standard, and a willingness to test out new methods. This doesn’t need to be an all-consuming activity, even a few minutes or a few hours can make new habits that make clinical communication better. We only must be humble enough to realize that further improvement is always possible and desirable, no matter how strong our communication is to begin with.

If you are in that frame of mind, take a moment to pause and think about a different way to think about clinician communication.

It builds on an understanding of the role of the mirror neuron system as the root of human empathy, covered in 2022. In 1963, long before the mirror neuron system was first described, theatre teacher Viola Spolin summarized her life’s work in the influential book: Improvisation for the Theater. In it, she describes a series of exercises for aspiring actors to help them gain skill in improvisation (commonly shortened to just “Improv”), where actors instantaneously absorb the mood, power structure, intent of other actors and respond quickly and appropriately. This use of the term Improv is more expansive than Improv Comedy, one application that is commercially popular (The Second City was founded in 1959 by Viola Spolin’s son, Paul Sills).

Decades later, in his third book, If I Understood You, Would I Have This Look on My Face?, Actor Alan Alda made the connection between improvisation and the mirror neuron system. Alda promotes the use of Viola Spolin’s exercises as a way for humans in all fields, from Engineering to Medicine to Economics and the Humanities to deeply listen and understand those we interact with, promoting deeper, more bilateral relationships as we become better communicators. Think of Improv exercises as training to hone the capability of our mirror neuron systems.

A description of one of the theater games will illustrate. In this activity, presciently called “Mirror,” two students face each other standing or sitting. First, one student is the “initiator” and the other is the “reflector.” The initiator makes movements and the reflector attempts to be a mirror, making the same motions as the initiator as close to being a mirror as they can. The “initiator” bears some responsibility for making the movements slow enough and smooth enough to make it easier for the “reflector” to match. After they gain proficiency in this, they switch roles, perhaps going back and forth a few times so they become more and more attuned to what movements and speeds each other uses. Then comes the Ouija-board moment, when the students are both “initiators” AND “reflectors” at the same time. Their mirror neuron systems are linked and result is amazing to watch.

In the last decade, several studies of health professions students suggests that a curriculum adapted from the traditional theater training can improve communications skills. A three year study at the University of Michigan Medical School used trainers from the Alan Alda Center for Communicating Science to train all students at the beginning of their third year, with a 2.5 hour Improv-based curriculum, using exercises specifically chosen for applicability to patient-clinician interactions.

One improvisation exercise selected for the training is called “The Rant.” Working in pairs, students take turns ranting for two minutes each about a subject of their choice, and then their rants are translated by the listening students to focus on the underlying values and needs of the ranter.

University of Michigan medical students reported substantial increases in insights regarding their role as a physician, ability to demonstrate effective communication, and teamwork. Most students reported applying the skills they learned to their clinical interactions with patients.

An Empathy and Clarity Rating Scale was developed and validated by four medical schools in the Mid-west and Eastern United States, which showed that a 6-hour Improv training for first year medical students (named with a new term: Medical Improv) made medical communication more empathic and clear. A news article from the American Association of Medical Colleges describes some of the exercises: “talking to a banana, muttering gibberish, and tossing balls at colleagues” summarizing, “Medical Improv transforms goofy theater games into serious skills like empathy, teamwork, and super-quick thinking.”

These theater exercises are meant to be experienced with other people. Local actors may be available to do in-person trainings, and on-line trainings are a low-cost option for individuals and those further away from cities. Each summer (except during the depths of COVID in 2020 and 2021), Northwestern University hosts a 5 day train-the-trainer workshop for Medical Improv. The next one is scheduled for July 20 through July 24.

Formal studies of the effect of Improv training in primary care residencies and for practicing physicians have not yet been published. However, many existing trainings in communication skills are more intellectual, not improving the actual default neural pathways our brains use. For example, many articles on medical communication focuses on using acronyms to remember how to organize communication content. For example, see this article for a summary of SBAR, the GREAT technique, the LAURS technique, the VALUE framework, and the SPIKES technique.

The changes brought about through the COVID pandemic and by the rise of the smart phone are associated with some major changes in how we communicate empathy and understanding with each other. Offering practicing clinicians training in Medical Improv offers a fun and possibly more effective path towards better communication skills.

By Robert L. Moore, MD, MPH, MBA, Chief Medical Officer

“Focused Action Beats Brilliance”

– Mark Sanborn, U.S. Author

Coming out of the COVID pandemic, the shortage of primary care and specialty clinicians that has been building over the prior 2 decades has become noticeably more acute.  Early retirements, some deaths and disabilities related to COVID contributed to this, as well as a changing expectation of a better work-life balance which means that the average clinician coming out of training is working less hours per week and expects less-intense on-call responsibilities.  Graduate Medical Education training slots funded by Medicare have been stagnant since the late 1990s, so the supply is getting progressively constrained relative to a growing population, and the average age of physicians is higher than ever as a result.

For many specialists, the most common payer is Medicare, so the stagnant Medicare physician reimbursement rates of the past 25 years (actually a 26% decline when adjusted for inflation) have led to a trend of less specialists in private practice, and more working for medical groups or hospitals.  In rural areas, where there are less groups and hospital employment options, some specialties have been especially impacted.  When the number of specialists drops in a community, the remaining specialists end up taking more call for ED and hospital consultation, making their practice more exhausting and causing a feedback loop of additional retirements.

In the past decade, some factors have alleviated the specialist shortages somewhat, but they do not resolve the underlying issues.

1. Wider use of video telemedicine
2. Primary care clinician using eConsult services to avoid referrals or make the referrals more streamlined.
3. Primary Care Clinicians use UpToDate and other resources to care for their patients without a referral or make referrals more targeted and efficient.
4. Some specialist physicians have hired Nurse Practitioners or Physician Assistants to be Specialist Extenders for initial consultations and ongoing care.
5. Artificial Intelligence-assisted transcription services make documentation and communication more efficient when deployed by specialists.
6. Changing documentation requirements in 2022 has alleviated the burden of inefficient, low-quality clinical documentation.
7. Partnership’s Transportation Benefit allows access to specialists who are further away, when no specialists are available locally.
8. Some tribal health centers, rural health centers and tribal health centers have begun contracting with specialists directly.

What can primary care clinicians do to help?

1. Good workups before referral. Primary care clinicians, especially the growing number of primary care Nurse Practitioners and Physician Assistants, can alleviate specialty demand further by developing systems to ensure that all referrals are warranted and well worked up.  Reserving specialty referral for patients that cannot be cared for by primary care clinicians frees up specialty visits for those patients who need to see specialists.  Send over good documentation with the referral to make the specialist’s workup more efficient.
2. Use telemedicine where possible. Sign up for, use, and embrace Partnership’s specialty telemedicine program.
3. Target specialty referral to the specialist most appropriate to the patient’s needs. Don’t send many referrals to several specialists for the same patient, and clog up the referral system.
4. Have conversations with impacted specialists and their specialist coordinators in your community. These help set expectations and prioritization, as well as standard procedures.  This makes the referral process more efficient for everyone: primary care, specialists, and patients.   Partnership hosts “referral roundtables” in each community: be sure to send your referral staff to these events.

What policy interventions are needed?

1. Medicare Rates: First and foremost, having the U.S. Congress change the Medicare reimbursement system from automatically generating cuts to physician rates to one that inflation-indexes future rates (H.R. 2474).  Related to this, short-term relief by actually generating Medicare rate increases is also important (H.R. 6683). Physicians should coordinate communication with their elected representatives with the American Medical Association, being sensitive to how legislative staff respond to arguments that paying physicians more is key for specialty access.
2. Graduate Medical Education: A dramatic increase in funded GME slots nationally is needed in specialties with the greatest shortages (gastroenterology, cardiology, rheumatology, endocrinology, ENT, neurology) as well as for primary care (H.R. 2389 and S. 1302).
3. Ballot Initiative in California to Lock in MCO Tax link to Medi-Cal Rates. While DHCS plans to increase specialist rates for Medi-Cal starting in 2025, a planned ballot initiative would lock these into place for the future.

What more can be done?

Although there is a broad shortage of specialists, the shortage is more acute for some specialties in some communities.  In these cases, the local hospital may be willing to subsidize recruitment to ensure hospital coverage of the specialist.  A local Health Center may be willing to bring in a specialist under their umbrella.  Partnership is working to track the shortages in each geographic area and to work with community partners to focus energy on these.  Please reach out to your local Partnership regional leadership if you want to help with this effort.

By Robert L. Moore, MD, MPH, MBA, Chief Medical Officer

“Effectively, change is almost impossible without industry-wide collaboration, cooperation and consensus.”

– Simon Mainwaring, Author

On January 1, when some of us were thinking about New Year’s resolutions or football games, 10 California counties joined Partnership HealthPlan’s County Organized Health System. Partnership grew overnight from about 600,000 members to 920,000 members. On the same date, 81,000 Kaiser-assigned members transitioned to direct coverage by Kaiser Health Plan, and many thousands of individuals with unclear documentation status, aged 27-49, gained Medi-Cal coverage.

This was the largest increase in membership since the combination of an eight-county expansion in 2023 and the massive expansion of coverage associated with the implementation of the Affordable Care Act. For the first time ever in California’s history, every resident of California qualifies for some sort of affordable or costless health care coverage.

The degree of growth was larger than expected. While DHCS asked Partnership to Expand in early 2022, the final approval did not come until two months before, so staffing for the increased needs of this larger membership is ongoing. Retrospectively, it took most of 2014 to stabilize staffing after the 2013-2014 expansion. Similarly, we expect 2024 to be a year of steady staff growth, stabilizing by the end of the year. Given the trust our 24 counties have granted us, the Partnership team feels a profound sense of responsibility to build an excellent support framework for our members and for you, our clinical network.

However, we will not be able to catch our breath in 2025. DHCS has directed us to begin a Dual-Special Needs Plan (D-SNP) for our members who have both Medi-Cal and Medicare, going live in January 2026. This will require two large IT software changes, a number of smaller IT programs, as well as a team focused on the new operational compliance network, and quality requirements for a D-SNP. On average, a Medicare member has about five times the needs of a typical adult Medi-Cal-only member, so the new D-SNP plan is another phase in growth in the responsibilities of Partnership to provider greater depth of services to these members, compared to when their primary payer was Medicare

Medi-Cal and the D-SNP Medicare Advantage program have a very important similarity: the resources coming from California and the federal government, respectively, are both adjusted significantly based on an acuity adjustment, which depends largely on the coding done by providers in our network. As announced last year, our current Primary Care Provider network will have the PCP QIP adjusted by acuity starting in measurement year 2023. It is likely that an even larger alignment of incentives for robust coding of annual visits will be implemented out of the gate for the Medicare population in 2026.

The upshot? Ensuring your clinicians are well trained on the importance of addressing all major medical issues each year and capturing this in the coding for that visit is key to the resources CMS and DHCS will send to Partnership, which in turn impacts the resources Partnership can devote to rates, quality incentives, and financial supports for the community. This year, we ask you to please prioritize provider and staff training on optimal visit coding, as well as the auditing processes to support improvement.

By Robert L. Moore, MD, MPH, MBA, Chief Medical Officer

“It is better to light the candle than to curse the darkness.”

– William L. Watkinson

All clinicians should have a mastery of the placebo effect, not for nefarious purposes such as pushing sham treatments on patients, but to understand the natural history of disease and the role of the placebo effect in mainstream treatments that sometimes provide benefit.

The strength of the placebo effect is related to the strength of belief in the treatment and conditioning.  In October, this newsletter highlighted some key studies on the power of belief, showing that the belief that a treatment works is stronger when the placebo treatment hurts, when it has brand name recognition, when it costs more, and when the prescribing physician expresses the conviction that the treatment will work.

This month I will review the power of conditioning.

The placebo effect for a placebo pain pill study is stronger in patients with a history of chronic pain for which they had taken pain medication in the past. This illustrates the effect of conditioning. It is well documented that in patients with chronic pain, their pain begins to subside when they know their pain medication is coming, before they even receive the medication. The end result is to reinforce the benefit of the pain medication (or placebo).

Conditioning can also affect prescribers! When multiple patients say, with great conviction, that the expensive brand name medication works better than the inexpensive generic equivalent, the clinician may start to believe this also and discount FDA studies showing bio-equivalence. The clinician’s belief can affect their prescribing pattern, leading them to initiate treatment with more expensive medications. It can also influence the degree to which they reassure their patients that the medication they prescribe will help them.

The over-use of expensive medications by prescribers because of conditioning leads to the important and disturbing conclusion that the placebo effect impacts not just the patient being treated, but also affects the clinician recommending the treatment. Here are some examples of the placebo effect on prescribers:

1. Generic citalopram contains both the active levo-isomer and the inactive dextro-isomer of the medication. Brand name Lexapro contains only the l-isomer. While there is a small possibility of an unfavorable side effect profile from the d-isomer, the presence of the d-isomer should have no effect on the efficacy of the l-isomer. If the l-isomer dose is the same, the efficacy should be equivalent. Only the price and the brand designation could explain increased perceived efficacy.
2. Patients with chronic, non-malignant pain taking greater than 120 mg of morphine per day will often, with time, develop more pain and request ever-higher doses of narcotics. It is a vicious cycle, with higher doses temporarily alleviating pain. But with time, the pain becomes more severe and disabling. Counseling patients on this vicious cycle is difficult because of their belief in the power of narcotic medications and the conditioning that changes the physiology of patients taking these medications chronically. This belief is reinforced by withdrawal symptoms when a dose is missed or a clinician begins reducing the dose. Any drug with withdrawal symptoms will be harder for a patient to stop, because they often deeply believe that only that medication is actually controlling the disease. This is true of narcotic medications, benzodiazepine anxiolytics, many antidepressants, muscle relaxants, and even NSAIDS. In all these cases, prolonged use of the medication produces long-term changes in the synapses. These changes cause patients to feel symptoms when the medication is withdrawn. Thus, withdrawal symptoms strengthen the placebo effect! In fact, if the patient can get through the withdrawal, they may feel the same or better than they did on chronic therapy, but it is hard to get the patient there. Trust between the clinician and patient and detailed education are keys to success, but may not be enough in some cases. For this reason we should avoid initiating prolonged use of medications which can cause withdrawal symptoms until all other options are exhausted.
3. When reviewing evidence for effectiveness of a given treatment, journal articles often compare only the effectiveness relative to placebo because this is the standard for FDA approval. I recommend always digging into the article to look at the treatment’s effect on the control/placebo group; it gives valuable information. For example, early studies comparing amoxicillin to placebo for treatment of acute otitis media (AOM) in children showed the control group improved in 82% of cases (presumably most cases were viral and did not need antibiotics), while about 90% of the amoxicillin group improved. In this case, the mechanism of the placebo effect is not related to belief or conditioning, but on the natural course of the disease. Nonetheless, use of antibiotics in pediatric otalgia conditions parents to expect antibiotics for treatment of all ear pain, since their child improved with treatment the last time antibiotics were prescribed. Looking closely at the placebo effect in studies helps clinicians interpret marketing for new products.

For example, should we preferentially prescribe a more expensive, more powerful antibiotic that is reported to improve 50% more cases of AOM than amoxicillin (i.e. a 12% benefit over placebo compared to 8%)? Perhaps we should just recommend ibuprofen for a few days (which would work 82% of the time) and reserve antibacterial treatment for cases where this conservative approach fails. This approach has been recommended by the American Academy of Pediatrics as an option since 2004, but clinicians in the U.S. have been slow to adopt it due to clinician conditioning.

Adapting our clinical practice and communication with patients to account for the placebo effect may be the most important skill we develop. We must use our scientific training to inform the art of medicine. As clinicians, we owe it to our patients and society to account for the placebo effect and use only low cost, relatively safe treatments when the major effect is likely to be placebo.

By Robert L. Moore, MD, MPH, MBA, Chief Medical Officer

“On National Rural Health Day, we recommit to investing in rural communities and delivering affordable, quality health care so that generations of rural Americans can thrive.”

– Joe Biden
President of the United States of America

November 15 was National Rural Health Day, a time to reflect how health policy affects rural communities.

Much of the legislation and policy in California is written with an urban or suburban point of view. This is not surprising, as nearly 95% of all Californians live in an urban or suburban setting – this includes those who develop the regulations in various state departments.

From a health policy perspective, the Enhanced Care Management (ECM) program is a current example of an urban focus that creates challenges for rural communities. The Department of Health Care Services (DHCS) is directing Medi-Cal Health Plans to contract with non-traditional Medi-Cal providers and organizations for ECM Services, directing plans to contract with community- based organizations (CBOs). In urban communities, with hundreds or thousands of CBOs, this represents an attempt to direct resources to organizations who are working directly with communities in nimble ways, getting at the underlying social drivers of health status – this work is often more challenging for mega-Primary Care Physician (PCP) sites.

In rural areas, Health Centers are smaller and more deeply connected with the special needs of their communities, and sometimes, the only provider of social and medical services. Unfortunately, DHCS has shared that community health centers were not the provider types they had envisioned for this new benefit – although they may be the only ones in their community able to perform the work. The number of local, rural, CBOs interested in developing a business infrastructure to deliver Medi-Cal regulated services is small, certainly not enough to meet the need for care management in the first few years of the program.

A few other examples:

1. Medicare’s policy of paying rural providers less than urban providers.
2. Medi-Cal’s Pediatric Palliative Care Benefit, whose service requirements are not possible in rural areas.
3. Medi-Cal’s non-medical transportation benefit (NMT) which does not account for limited public transportation options, limited internet availability, limited public infrastructure such as passable roads, highways, etc. and challenges with time and distance for rural and remote communities.
4. Medi-Cal’s new Community Health Worker benefit, whose service delivery relies heavily on in-person outreach and engagement that will not have a quick update in rural communities due to rural and remote communities, along with a lack of readily available workforce.

Density accounts for the difference in the implementation of health policy in urban and rural settings: the density of clinicians, the density of patients, and the density of available support services.  Poverty exists in cities and rural areas, but higher-density provides larger urban areas with more governmental resources and economies to help address underlying economic inequities.

With advocacy, Partnership and other organizations representing rural health care providers, can request exceptions and workarounds for health policies that are not feasible in rural communities.  The energy needed to go through the exemption process takes away from other activities, like innovation, community engagement, and solidifying core management and operations.

It would be more efficient and equitable to have proposed health policies and regulations undergo a rural health analysis in the drafting stages.

Here are a few opportunities that can be implemented immediately:

1. In the policy development process, add a rural analysis that identifies any challenges in applying the policy equally and equitably in rural communities. This analysis should include direct feedback from key advisors and associations that represent rural communities.
2. If a difference is identified, the policy is amended to equitably affect rural areas. This may mean that a policy that is hard to operationalize in rural areas needs a higher level of funding than in urban areas, so that it can be applied equitably.
3. Department attests that this process has been followed.

A policy that is promulgated without accommodations for rural areas is inequitable and in fact creates risk for greater health disparities in rural communities.  Rural Native Americans face the largest health inequities in the state (and in the Partnership service area), any policy that is inequitable from a rural perspective, is also inequitable from a Native American perspective, with an effect that multiplies their historic trauma and inequities.

Health Policy that systematically, if unintentionally, disadvantages residents and health care providers in rural areas is a reflection of “Structural Urbanism.”  Just as intentionality is needed to address Structural Racism, so too is intentional policy analysis needed to ensure that health policy and regulations are not perpetuating inequities for rural Californians, including Native Americans.

By Robert L. Moore, MD, MPH, MBA, Chief Medical Officer

“From Asclepius through Hippocrates to Galen, and until very recently [about 100 years ago], the history of medical treatment was largely the history of the placebo effect, because all medical treatments, with rare exceptions, were at best placebos, at worst unknowingly deadly.”

– Arthur Shapiro and Elaine Shapiro
Professors of Medicine, Singapore National University Hospital

“Just about everyone on this blog mailing list has been in a leadership role at their PCP practice for fewer than 10 years.  For the next few months, as new physicians from Partnership’s 10 new counties join this mailing list, I will be reprinting some of my favorite lead articles from past blogs, with updates as appropriate.  This post was first included in the phcprimarycare.org blog in 2012 and was republished in Marin Medicine afterwards. It is part one of a series of three articles on the placebo effect.”   Enjoy! –Bob Moore

Case Study: A primary care clinician recently diagnosed a patient with major depression. She prescribed citalopram 20 mg per day. One month later the patient felt less depressed and her PHQ-9 had dropped from 16 (moderate depression) to 12. Since the patient was not in remission, the clinician increased the dose to 40 mg per day. One month later the PHQ-9 score was 8 (mild depression) and two months later, the score was 3 (not depressed). Should the citalopram be continued? Which one of the following answers is most correct?

1. Yes. The citalopram worked and the patient is at risk of recurrence if the treatment is stopped.
2. Yes. Although the citalopram helped no better than placebo in this patient, stopping it will remove the placebo effect and increase the risk of recurrence.
3. No. The citalopram worked and remission is likely to continue without continued treatment.
4. No. The citalopram had no effect and the patient became better on her own, so continued treatment is not indicated.

I’ll make it easier for you – 1 and 3 are not correct. A 2008 meta-analysis demonstrated that anti-depressants work well in severe depression (PHQ 9 score greater than 20) but are no better than placebo for mild to moderate depression, as was the case in this patient. Her improvement was not due to the pharmacology of citalopram. She might have improved without taking any pill, or she may have improved taking any pill she believed to be helpful – the placebo effect.

We will return to this case and find which answer is most correct at the end of this post. First, though, please explore with me the implications of some new studies and thoughts on the placebo effect.

All clinicians must have a mastery of the placebo effect, not for nefarious purposes such as pushing sham treatments on patients, but to understand the natural history of disease and the role of the placebo effect in mainstream treatments that sometimes provide benefit.

History of Placebo

The word placebo comes from the Latin “I shall please.” It was used in the 14th Century to refer to sham mourners hired to sob and wail for the deceased at funerals. By 1785 it appeared in the New Medical Dictionary, referring to what were considered (at the time) marginal practices of medicine. In retrospect, of course, we now know that the effect of many treatments available at that time were due to the placebo effect. These included worm secretions for toothache (worked 68% of the time) and powdered Egyptian mummy (formerly available from Merck), which was used by President Lincoln’s physician on the bullet wounds inflicted by John Wilkes Booth. (For more, see the reference by behavioral economist Daniel Ariely.)

The use of placebo in controlled clinical trials became common only in the last 50 years. In controlled trials, the “placebo effect” actually includes two effects: the effect of the natural history of the condition being studied (is it improved even if there is no treatment at all?) and the physiologic placebo effect (requires the patient’s cognitive ability to understand what the treatment is attempting to do). The physiologic placebo effect is influenced by two factors: the expectation or belief that the intervention will work, and classical conditioning.

The Power of Belief

The strength of the placebo effect depends on the strength of the patient’s belief that it will help. The placebo response in studies ranges from 0% to almost 100%, depending on the circumstances. Children tend to have greater responses than adults and patients with Alzheimer’s dementia progressively lose placebo responsiveness.

I would like to highlight five factors that increase the strength of the belief of effectiveness: the invasiveness of the intervention, the confidence with which the prescriber associates the treatment with improvement, advertising/marketing, the cultural background of the patient, and the price of the treatment.

1. No pain, no gain, part I. Cutting the skin has a powerful effect. From 1930 to 1955, internal mammary artery ligation was used to treat angina. Doctors opened the chest wall, tied off the internal mammary arteries, and closed the chest wall. Patients noted immediate relief which gradually decreased over time. In 1955, this treatment was compared to placebo: a sham surgery, with the patient put to sleep, the skin was cut and sutured, but the arteries were not ligated. The result rocked the medical world: there was no benefit of internal mammary artery ligation over sham surgery. Tens of thousands of individuals had had open thoracic surgery for an ineffective treatment. The placebo effect was strong because of the invasiveness of the surgery, compounded by the certainty conveyed by the physicians (who really believed in it), and the high price for the surgery.
2. No pain, no gain, part II. Have we learned our lesson from the internal mammary artery example? Apparently not. How effective is viscosupplementation injection (using hyaluronic acid) of the knee for painful osteoarthritis? A 2012 meta-analysis of high quality trials of intra-articular hyaluronic acid showed a clinically insignificant benefit over saline injection, and increased side effects. Piercing a knee with a large needle and injecting a liquid works particularly well as a placebo. What is more interesting is the size of the placebo effect: 30-50% improvement in pain and 10-30% improvement in function. Finding the size of the placebo effect can sometimes be a challenge in reading scientific studies; they tend to focus on the “true” effect of the treatment, after adjusting out the placebo effect, even when the placebo effect is far greater in magnitude.
3. Is brand name really better? Many patients and even many clinicians believe brand name drugs work better than generic medications. Again, the belief of the patient impacts the strength of the placebo response. In a recent appeal process, a PHC patient stated with great conviction that brand name Concerta helped his symptoms while the generic version did not work. Investigation showed that the same manufacturer produced both the brand name and the generic pills in the same factory, in the same way. The only difference: the brand name pills are labeled Concerta. Thinking through the implications: this patient should receive neither the brand name drug nor the placebo, because the entire benefit of the brand name appears due to its placebo effect, since the exact same medication, when generic, did not work. Please consider this every time a patient says the brand name works better: it is likely placebo effect instead of a true difference in efficacy.
4. The effect of culture. Many studies have shown the placebo effect can be stronger in some cultures than others, depending on the condition. This is related to the meaning through which people of different cultures experience illness and treatment. For example: the placebo effect in treating gastric ulcers is low in Brazil, higher in northern Europe, and extremely high in Germany. However, the placebo effect in treating hypertension is lower in Germany then elsewhere.
5. The effect of price: higher cost medications work better. Most of us subconsciously use price as a surrogate indicator of quality. Taking this a step further, a higher price increases the belief a treatment will work and increases the strength of the placebo effect. This was best shown in a brilliant little study performed by four economists published in 2008. Volunteers were recruited to study a “new” pain medication. Their baseline pain threshold was established with a series of electric shocks to the wrist. They were then divided into two groups. Each was given one of two identical placebo pills, with one group told this new treatment for pain would cost 10 cents per pill and the other told the treatment would cost $2.50 per pill. Pain was reduced by 55% in the low-price pill group and by 80% in the high-price pill group. Another key finding in this study was related to conditioning from prior use of pain medication, and will be described below. Before considering this, though, think through the implications of this study. What does it mean for a patient who requests a brand name medication, perhaps because of an advertisement seen on television? How should we clinicians interpret our patients’ lack of response to low-cost generics? More on this below.

The Power of Conditioning

The placebo effect in the placebo pain pill study was stronger in patients with a history of chronic pain for which they had taken pain medication in the past. This illustrates the effect of conditioning, the second factor contributing to the effectiveness of the placebo effect. It is well documented that in patients with chronic pain, their pain begins to subside when they know their pain medication is coming, before they even receive the medication. The end result is to reinforce the benefit of the pain medication (or placebo).

Conditioning can also affect prescribers! When multiple patients state with great conviction that the expensive brand name medication works better than the inexpensive generic equivalent, the clinician may start to believe this also and discount FDA studies showing bio-equivalence. Even worse, the clinician’s belief can affect their prescribing pattern, leading them to initiate treatment with more expensive medications. It can also influence the degree to which they reassure their patients that the medication they prescribe will help them.

The over-use of expensive medications by prescribers because of conditioning leads to the important and disturbing conclusion that the placebo effect impacts not just the patient being treated, but also affects the clinician recommending the treatment. Here are some examples of the placebo effect on prescribers:

1. Generic citalopram contains both the active levo-isomer and the inactive dextro-isomer of the medication. Brand name Lexapro contains only the l-isomer. While there is a small possibility of an unfavorable side effect profile from the d-isomer, the presence of the d-isomer should have no effect on the efficacy of the l-isomer. If the l-isomer dose is the same, the efficacy should be equivalent. Only the price and the brand designation could explain increased perceived efficacy.
2. This same principle applies to proton pump inhibitors. When prescribers select a more expensive agent like Nexium, they are not helping their patients, but only contributing to the high cost of health care. Brand name medications are on average about 15 times more expensive than equivalent generic medications.
3. Patients with chronic, non-malignant pain on greater than 120 mg of morphine per day will often, with time, develop more pain and request ever higher doses of narcotics. It is a vicious cycle, with higher doses temporarily alleviating pain. But with time, the pain becomes more severe and disabling. As narcotic overdoses have surpassed auto accidents as a cause of mortality in California in the decade prior to 2010, the medical community is now aware of the danger high dose narcotics pose for patients and the community, without really alleviating pain or improving function of the patients taking this therapy. Counseling patients on this vicious cycle is difficult because of their belief in the power of narcotic medications and the conditioning that changes the physiology of patients taking these medications chronically. This belief is reinforced by withdrawal symptoms when a dose is missed or a clinician begins reducing the dose. Any drug with withdrawal symptoms will be harder for a patient to stop, because they often deeply believe that only that medication is actually controlling the disease. This is true of narcotic medications, benzodiazepine anxiolytics, many antidepressants, muscle relaxants, and even NSAIDS. In all these cases, prolonged use of the medication produces long-term changes in the synapses. These changes cause patients to feel symptoms when the medication is withdrawn. Thus, withdrawal symptoms strengthen the placebo effect! In fact, if the patient can get through the withdrawal, they may feel the same or better than they did on chronic therapy, but it is hard to get the patient there. Trust between the clinician and patient and detailed education are keys to success, but may not be enough in some cases. For this reason, we should avoid initiating prolonged use of medications which can cause withdrawal symptoms until all other options are exhausted.
4. When reviewing evidence for effectiveness of a given treatment, journal articles often compare only the effectiveness relative to placebo because this is the standard for FDA approval. I recommend always digging into the article to look at the treatment’s effect on the control/placebo group; it gives valuable information. For example, early studies comparing amoxicillin to placebo for treatment of acute otitis media (AOM) in children showed the control group improved in 82% of cases (presumably most cases were viral and did not need antibiotics), while about 90% of the amoxicillin group improved. In this case, the mechanism of the placebo effect is not related to belief or conditioning, but on the natural course of the disease. Nonetheless, use of antibiotics in pediatric otalgia conditions parents to expect antibiotics for treatment of all ear pain, since their child improved with treatment the last time antibiotics were prescribed. Looking closely at the placebo effect in studies helps clinicians interpret marketing for new products. For example, should we preferentially prescribe a more expensive, more powerful antibiotic that is reported to improve 50% more cases of AOM than amoxicillin (i.e. a 12% benefit over placebo compared to 8%)? Perhaps we should just recommend ibuprofen for a few days (which would work 82% of the time) and reserve antibacterial treatment for cases where this conservative approach fails. This approach has been recommended by the American Academy of Pediatrics as an option since 2004, but clinicians in the U.S. have been slow to adopt it due to clinician conditioning.

Adapting our clinical practice and communication with patients to account for the placebo effect may be the most important skill we develop. We must use our scientific training to inform the art of medicine. As clinicians, we owe it to our patients and society to account for the placebo effect and use only low cost, relatively safe treatments when the major effect is likely to be placebo.

If the patient really wants a pill – when education about the nature of the condition doesn’t seem to work – what should we do? Because it is ethically problematic to give a treatment the provider knows is placebo, clinicians tend to resort to medically acceptable treatments (especially pharmaceuticals), even if they know or strongly believe their benefit is most likely due to the placebo effect. One way around this conundrum is to consider lifestyle interventions. Examples include: recommending gentle physical activity for low back strain, headaches, depression, etc.; working with the patient to decrease fat intake or make other beneficial dietary changes for gastrointestinal symptoms, depression, anxiety, etc.; or recommending a low cost multivitamin-with-mineral supplement to boost the immune system’s ability to heal. If there is not time or resources for the more in-depth support required to encourage dietary and physical activity changes, the vitamin option may be more attractive.

An important word of caution: the placebo effect does not prove that a serious condition is not present. Patients with life threatening conditions can get some temporary relief from a placebo treatment.

Going back to our original case involving citalopram for moderate depression: There is likely some effect of placebo, in which case the relapse may occur no matter what choice is selected. If a relapse of moderate depression happens after citalopram is stopped, it may be attributed to stopping the medication. If it happens while taking citalopram, it may be attributed to the medication not working as well. In the first case the clinician perhaps “stopped the medication too early” and will restart it. If a recurrence occurs on citalopram, however, the clinician may then move to another medication that might “work” better, perhaps a nice expensive brand name product. The clinician gives four weeks of Lexapro samples, which the patient reports works very well (the placebo effect enhanced by the use of expensive, brand name samples). The clinician then requests that Medi-Cal cover the medication. Suppose Medi-Cal denies the request and the patient appeals (Actually, since the pharmacy benefit has been carved out since 2022, MediCal prefers brand name medications due to rebates, but set that consideration aside for the moment to think about the principle). This puts the PCP in the position of supporting the selection of an expensive brand name medication whose effectiveness is entirely due to the placebo effect.

An alternative: use your influence with the patient to increase their belief that a particular low-cost treatment will work and is the safest medication for them. Use your word choice to increase the patient’s perception that the treatment is effective and lessen their belief that high cost or brand name equates to more effective treatment.

The correct answer (in 2012)? Option 4 (stop the medication) is the most cost-effective option. It avoids escalation of treatments with no proven efficacy. For the best option of all, based on an understanding of the placebo effect, consider trying non-pharmacological treatment: brief intervention counseling or referral for cognitive behavioral therapy if you have these available in your health center or in your community.

References:

Ariely, Dan. The Power of Price: Why a 50-Cent Aspirin Can Do What a Penny Aspirin Can’t. Predictably Irrational: The Hidden Forces that Shape our Decisions. HarperCollins Publishers, 2008.

Lieberthal et al. Clinical Practice Guideline: The Diagnosis and Management of Acute Otitis Media. Pediatrics. 2013; 131:e964-e999. American Academy of Pediatrics.

Moerman DE. Cultural variations in the Placebo Effect: ulcers, anxiety, and blood pressure. Med Anthropology Quarterly 14:51-72.

Rutjes, et al. Viscosupplementation for Osteoarthritis of the Knee: A Systematic Review and Meta[1]analysis. Ann Intern Med 2012;157(3):180-191.

Venekamp et al. Antibiotics for acute otitis media in children (Cochrane Review).The Cochrane Library 2013 Issue 1.

Waber, et al. Commercial Features of Placebo and Therapeutic Efficacy. JAMA March 5, 2008, 299(9), p. 1016-1017.