Physician Management of Demand at the Point of Care


Many perspectives can be taken to look at physician practice behaviors. Other articles in this section of the site and in the literature (e.g., by McGuire, Chandra, Cutler, and Song) provide extensive information on multiple approaches to studying physician practice. In general, empirical studies of physician practice behaviors build on administrative data which contain the information necessary for billing but no details on what happened in the encounter. They do not contain detailed information on how patient demand was expressed and how physician responded while they were engaged in an office visit, i.e., at the point of care. Survey data have its own problems, and can be subject to recall, social desirability, or self-perception biases. Physician management of patient demand at this microlevel needs special data to shed light on the exchanges between patient and physician. Video or audio recordings of office visits are two such data sources. This article focuses on interactions between primary care physicians and patients at the point of care, where patient demand is managed through conversations. By analyzing recordings in a detailed way, this methodological approach enables the authors to closely observe what Kenneth Arrow notes as the activities of producing medical care that are unobservable through the lens of administrative data.

Video or audio recordings of the visits allow the authors to examine the length and content of visits. They provide a comprehensive representation of the patient–physician encounter, unlike chart review which can be influenced by physicians’ charting patterns and their tendency to underreport delivery of some services or overreport other services. The following two excerpts from transcripts of two visits illustrate the nuanced information about the demand for bypass surgery (Example 1) and uncertainty about incidence of disease and efficacy of treatment, and the use of heuristics in decision making under uncertainty (Example 2).

Example 1

An elderly female patient seeing her primary care physician for a dry cough, before undergoing bypass surgery that was scheduled to take place in a few weeks.

Patient: (Clasping her hands) and really, why I agreed to the surgery was because I thought that they would be able to fix the damage the heart attack had done to my heart. And they said no.

Physician: No, you can’t fix that. It helps to reestablish the circulation so that you don’t have any further heart attacks. y so I will see you back here after your surgery.

Patient: Yes, if I don’t cancel it again.

It is seen that the patient had expressed some reservations about the operation. The physician did not address her concerns or allowing her to change her mind about undergoing the surgery.

Example 2

An elderly female patient seeing her primary care physician, after having had 3 visits during which multiple new psychotropic medications were started and stopped in the last 3 months. She complained of unsteadiness and off balance, anxiety, and forgetfulness. The following was the exchange at 17 min into the visit.

Physician: The girls are going to set up a follow-up appointment in two weeks and we will see how we’re doing. You’re going to stop the Lorazepam, stop Lorazepam, take Vitamin E, water pill, …

Patient: (Raising her hand as though to signal she has something to say) Now, …

Physician: (Taking her hand, shaking it, and continuing to talk) …. everything else stays the same, including the Wellbutrin and we’re going to see you back in two weeks.

Patient: But now, you said on that Vitamin E, 1000 twice a day, 2000?

Physician: Yes, ma’am. Patient: Ok.

Physician: That’s what the study states. It’s written down here. Ok?

Patient: Yeah, sure.

Physician: (Moving to help patient down from exam table and starts walking towards the door) There you go. We’ll try a little ‘addition by subtraction’ and hope that by stopping the Lorazepam that will stop your coordination difficulties and maybe the Wellbutrin we can continue.

Stopping Lorazepam suddenly instead of tapering it off slowly could exacerbate the patient’s anxiety. The exceedingly high daily dose of vitamin E is also not indicated. From the video data it is noticed that the physician was not willing to hear any more from the patient but wanted to bring her back for another visit 2 weeks later.

Questions That Have Been Informed By Microlevel Interaction Analysis

Direct Observation Analysis

A number of studies that have shed some light on the ‘blackbox’ of patient–physician exchanges at the point of care using microlevel interaction analysis of video and audio recordings of office visits have been undertaken. They encompass three general areas: (1) time allocation in primary care office visits, (2) time management practice in office visits that resemble the use of a behavior rule, and (3) management of diverse demand with heterogeneous level of professional and personal uncertainties.

Time Allocation In Primary Care Office Visits

Time is a scarce resource in a physician’s office practice. How physicians use clinic time has important implications on quality of care, patient trust, and malpractice suits, and is one of the components of physician payments in the resource-based relative value scale. Primary care office visits are essentially communication events between patient (demand) and physician (supply) on which data and research methods from other scientific disciplines have made extensive efforts. Social psychologist Mishler views patient–physician conversations as complex, multidimensional, and multifunctional exchanges. Health services researchers recognize the unique and critical role of primary care physicians in providing patients with an ‘advanced medical home’ where complex comorbidities are diagnosed and treated. Despite previous research efforts on patient–physician interactions, however, the literature was silent on how physicians allocate time within a visit. The point of care exchanges that occur behind the closed door were considered hidden and noncontractible.

To examine how clinic time was actually spent during patients’ visits to primary care physicians and to identify the factors that influence time allocations, a novel approach was developed to analyze video recordings of routine office visits in primary care practices. Currently, audio recordings of period health examinations (PHEs) are being analyzed in an integrated health delivery system which provides supplemental data on service utilization before and after the recorded visits. The findings have been rather thought provoking.

Specifically, not only the length of visits but also, more importantly, the content of visits in terms of units of clinical decision making referred to as ‘topics,’ operationalized as clinical issues raised by either participant was examined. An interaction was coded directly from an audio or video recording of the visit, along with transcripts of the interaction, based on topics sequentially introduced by patient or physician. After partitioning a visit into topics, the amount of time spent on each topic by patient and physician was further recorded. In the PHE study, the quality of communication on each topic was also measured. Figure 1 illustrates the flow of conversation in one visit, from topic to topic, over time. It is evident that the exchange took a rather free flow form, consistent with general conversation patterns in casual conversations, despite training in medical school and residencies on how to structure an office visit.

Physician Management of Demand at the Point of Care Figure 1

This approach of using microlevel data collected at the point of care allows the authors to examine how much time is dedicated to specific topics, the cognitive and emotional efforts invested in the exchanges across topics, and the factors that influenced how clinical time and efforts are allocated. It has been found that primary care office visits vary not only in length but also in the division of time among topics. Patients typically present multiple complaints during an office visit requiring physicians to divide time and resources during a visit to deal with competing demands. Very limited amount of time was dedicated to specific topics. In the video study, it was found that the median visit length was 15.7 min covering a median of six topics. Approximately 5 min were spent on the longest topic, whereas the remaining topics each received 1.1 min. Although time spent by patient and physician on a topic responded to many factors, length of the visit overall varied little even when contents of visits varied widely. Macrofactors associated with each site (e.g., academic medical center where physicians are paid by salary vs. physicians in fee-for-service solo practices or in a managed care group practice) had more influence on visit and topic length than the nature of the problem patients presented.

Time Management Practice Resembling The Use Of A Behavioral Rule

The New York Times published an article about the above study entitled ‘‘The Ticking Clock in the Doctor’s Office’’ along with a cartoon which depicts a clock wedged between a patient and a physician. The ticking clock resembles an upward sloped shadow price of time that rises as time elapses once a visit starts. Although patient and physician both initiate discussions on topics during the visit, it is the physician who decides when the visit needs to end. The authors wanted to examine how physicians decide when to end a visit. They were most interested in the order of topics in terms of ‘seriousness,’ or of the physician’s assessment of the benefits of spending time on the topic.

One hypothesis was that potential topics are ranked by importance and the most important topics are covered first. The efficient allocation of the physician’s time can be described by a threshold value or shadow price, call it l, such that any topic with value greater than l is dealt with by the physician, and any topic with value less than l is not. The value of l is set so as to just use all of the time the physician has available. Another interpretation is that the physician has another activity with a constant value of l. This other activity might, for example, be ‘administrative work,’ that can be done during the day or handled at the end of the day.

An alternative hypothesis was that physicians have a ‘target’ amount of time to spend with each patient, like a tennis coach who gives equal amount of time to each of his students. One way to model a ‘target’ is to regard the shadow price of time to be zero up to the target and infinite after the target. Under the alternative hypothesis, physician admits a new topic if and only if the value of the new topic is greater than or equal to l given how much time has already elapsed in the visit. The target can be set by a norm that is dictated by protocols at the practice regarding the number of patients a physician needs to see each day to reach productivity goals. Under the influence of such productivity goals, the decision to end the visit is determined by a behavioral rule rather than maximization of the expected net benefit of their time with patients.

Empirically, the probability of a topic being the last topic of a visit was modeled. The key right-hand-side variables are four binary indicators of time elapsed when a topic was introduced: within 5 min of the beginning of the visit, between 5–10 min, between 10–15 min, and after 15 min. Multiple measures of the ‘seriousness’ of a topic were incorporated. The empirical findings support the alternative hypothesis: The likelihood of a topic being the last increased successively and significantly with each increment in the block of time for topic introduction. The results are robust to various specifications.

This example resonates with time accounting and targeting heuristics observed in behavioral economics. New York City cab drivers have been documented to quitting earlier on high-wage days and driving longer on low-wage days. Physicians are found to work under a similar behavioral rule, spending time patiently until the target is reached, then quickly closing the visit. The second example presented at the beginning of the article was from a visit in which the physician was by turns patient and inquisitive (at 5 min into the visit) and brisk and dismissive (at 17 min). He transformed the patient’s raised hand for inquiry into a good-bye handshake, and escorted her from the room while telling her that he would bring her back for another visit in 2 weeks. Another approach used to close a visit was referring patients to social workers who would spend more time listening and figuring out how social services might offer support. Giving prescriptions for medications (sometimes even unindicated medications) is yet another approach to close a visit. Although fee-for-service environment clearly rewards such approach to increase demand, they are not necessarily what a perfect agent would have done. It is the opposite of what one would do to maximize the effectiveness of each visit which entails covering multiple issues at one sitting. Managing demand by limiting the number of issues addressed or not addressing some of them effectively – as evidenced in the findings – may actually contribute to backlog in access to office visits because more demand for return visits has been created to address unresolved issues.

Management Of Diverse Demand With Heterogeneous Level Of Professional And Personal Uncertainties

Unlike specialty care visits in which patients usually seek service for one particular condition, for example, rotator cuff injury, carpel tunnel syndrome, that is within the expertise of the specialist, primary care office visits routinely involve multiple patient complaints that could reach beyond the expertise and comfort zone of primary care physicians. Professional and personal uncertainties pose additional layers of complexity in physician’s micromanagement of heterogeneous patient demand. Nevertheless, generalists are expected to address more complex issues. Whether what they do meets this expectation is an empirical question. Direct observation study can facilitate better understanding of how well they perform to the standards and what system modifications or policy changes are needed to complement or substitute some work of the primary care physicians to reduce inefficiencies in the agency relationship.

Whether And How To Respond To Demand For Treatment For Mental Illnesses

Depression is the most common mental illness that is encountered by primary care practitioners who deliver most of its treatment, especially for elderly patients. Depression treatment practice guidelines call for at least four office visits, with counseling on mental health problems lasting for at least 5 min. They also advocate educating patients about treatment options, including medications’ mechanisms of action, costs, risks, and benefits. Although treatment guidelines have been developed based on clinical research and expert opinion, the ‘meaning’ of these standards in terms of routine medical practice is not well understood. Detailed aspects of guidelines are rarely applied in quality assessment studies. The video and audio data enabled the authors to explore in detail how primary care physicians managed patient demand for treatment for depression in routine office visits.

It was found that the median length of time spent addressing depression was only 2 min, during which the patient spoke for 1.2 min and the physician spoke for 0.8 min. Furthermore, it was found that just because a physician has seen a patient with a mood disorder for an appropriate number of visits and prescribed a psychotropic agent or even multiple agents, the appearance of adherence to current guidelines does not necessarily mean that the patient received good mental health treatment. The authors explain in details below with findings from qualitative analyses of video recordings.

Qualitative analysis of the video recordings of visits during which mental health was addressed revealed three themes which characterized how physicians managed patient’s demand for depression treatment at the point of care. The first theme was taking the time to investigate the disease and the patient as a person. The visit with the longest time on mental health discussion (17 min) in the whole study sample was a visit by a 69-year-old white male who broke into tears when his physician asked him how things were. The physician explored carefully and confirmed that the patient was depressed and suicidal, with a plan to use a revolver (already loaded) in the bathtub to end his life. Although the physician was very thorough in his assessment, the treatment plan was inadequate when compared to guidelines on addressing suicidal patients. He asked the patient to give a ‘no suicide contract’ and asked him to call a psychiatrist.

The second theme was allocating some time to gathering information, recognizing depression, but giving inadequate treatment. A case in point of this theme was a series of three recorded visits between a female patient and a male physician (Example 2 at the beginning of the article). The time allocated to mental health in each visit was 9, 5, and 11 min, respectively. Over approximately 7 months, this patient was sequentially prescribed paroxetine hydrochloride (10 mg for 6 weeks), fluoxetine hydrochloride (10 mg for 2 weeks), venlafaxine hydrochloride (37.5 mg for 6 weeks), and bupropion hydrochloride (unknown dose for 4 weeks) and taken off of Lorazepam, which she had taken for a long time. In one of the visits, the physician turned her raised hand for inquiry into a good-bye handshake. The management of her depression and anxiety had deviated from guidelines. For instance, low dose and short course of the antidepressants could have rendered these efficacious medications ineffective. Furthermore, stopping Lorazepam abruptly could increase withdrawal symptoms, potentially compounding anxiety. Despite the deficiencies in how her conditions were managed, research or quality improvement efforts based on claims data would have characterized these visits as guideline concordant, because only visit frequency was observed.

The third theme was physician dismissing patient’s cue and indications of emotional distress. Five consecutive visits between a female physician and a female patient were seen, in which perfunctory and dismissive treatment of a patient’s emotional distress was apparent. This patient was hospitalized to receive a stent after percutaneous transluminal coronary angioplasty. 2 min and 40 s were spent on the patient’s emotions:

Physician: What you been up to?

Patient: I have just been crying my eyes out. (Cryingy) Physician: Why?

Patient: I don’t know. I can’t help it. (Cryingy) Physician: Why?

Patient: And then people ask me how I am, I just cry. (Cryingy)

Physician: Oh (pause). Well I am not going to ask you that anymore.

This physician’s paternalistic model of medical practice did not alleviate the patient’s suffering. This is a case in which the 2-min mental health care clearly failed, because the patient left the visits with her depression neither evaluated nor treated.

Such omission could impede her healing from the heart disease. It was somewhat surprising to see that, in a postvisit survey, the patient was satisfied with her visit and continued to return to the same physician for her care.

Whether Responding To Patients’ Clues Would Lengthen Visits

Patients often give clues of distress and invite their physicians to respond. Although some physicians respond immediately, others choose not to respond for fear of sinking too much time if they were to respond. Communication researcher Levinson et al. examined the length of visit and patients’ presentation of clues and found no evidence that responding to clues lengthens visits. Actually, visits in which a physician responded to a patient’s clue were shorter than when the physician missed the opportunity. For primary care, visits without clues were a mean 15.7 min. Those with one clue were 12.7 min. Visits were longer (20.1 min) when there was a missed opportunity, compared to visits where the physician demonstrated at least one positive response to a clue (17.6 min). Visits in which patients repeatedly brought up emotional issues after the physician missed an opportunity to respond to a clue were longer than those with a positive response (18.4 min).

Whether Discussion On A Topic Ends With An Explicit Decision

From the perspective of clinical communication, a decision can be defined as a verbal commitment to an explicit action. A clearly stated decision can facilitate a cognitive closure in the minds of the patient and physician that the discussion on a particular topic has reached an end. Communications research repeatedly documented a deficit in informed decision making in routine office visits and the lack of clear understanding patients have about what they needed to do after they leave their visits. There was a gap in knowledge on how often explicit decisions are actually made when discussion on a topic ends. The proportion of topic discussions in the sample that ended with an explicit decision was examined. The findings suggested, while the majority of topics ended with a decision (77%), there were variations related to the content and dynamics of interactions. Topics in which patients spoke more (67 s) were more likely to end with an explicit decision. Larger number of topics in a visit was associated with lower probability of a topic ending with a clear decision.

In summary, Arrow and colleagues have commented on the challenges in monitoring agents when their actions are unobservable. When the authors studied patient–physician interactions captured in video or audio recordings, they had an invaluable opportunity to observe agent actions. Combined with data on patient behaviors, a more nuanced understanding of how physicians manage patient demand at the point of care was gained. It could be seen that physicians have been observed to be habitual in their management of time during office visits, subject to influence of other demands presented by patients, and their own familiarity with the issues. In observing longitudinal visits between the same dyads of patient and physician, it is also noticed that, in this ‘repeated game’ context, patients return to the same physicians even though their previous clues of their desire to receive mental health services were overlooked or dismissed. It appears that Albert Hirschman’s encouragement to individuals to exercise their abilities to exit or use their voice in order to show their dissatisfaction is challenging for patients to carry out. The assumption of full information rarely holds true in medical decision making. Physician behavior often deviates from profit maximization expected of a firm. Simple extensions of the profit or utility maximization models may not produce satisfactory explanations of principal and agent behaviors. Simon’s ‘Satisficing’ under constraints model offers a more plausible explanation of some of their behaviors. How behavioral economics may offer promising perspectives to study these behaviors are briefly discussed below.

Perspectives From Behavioral Economics

Health care exchanges, physician practice in particular, are fertile grounds for behavioral economics research. Yet the bulk of the application of behavioral economics to issues in health economics has been on patient behaviors, particularly addictive behavior around cigarettes, drugs and alcohol, and unhealthy lifestyles. Physician behavior has just begun to be subject to investigations guided by behavior economics perspectives. Some examples of physician behaviors that can be explained by behavior economics perspectives are elucidated here.

Use Of Heuristics

An important finding from behavioral economics is the use of heuristics by decision makers that works reasonably well over a broad array of circumstances, but can be far off the mark in others. Following a norm or what Frank and Zeckhauser refer to as a ‘ready-to-wear’ treatment would be one such heuristic. The choice of heuristics implies less attention to purposeful optimization which is consistent with Simon’s satisficing behavior. Humans, as opposed to Econs, have frailties. Often, the cognitive resources to maximize is lacking: the relevant probabilities of outcomes is usually not known, all outcomes with sufficient precision can rarely be evaluated, and the memories are weak and unreliable. Wennberg told the story of physicians recommending tonsillectomy for certain percentage or number of recently seen patients. Approximately 40% of children previously deemed not needing surgery were recommended for surgery at each subsequent waves of examination by additional physicians. The findings from direct observation data are consistent with this notion.

Attribution Bias

Context under which decisions are made needs to be taken into consideration. Loewenstein argues against ‘context free’ thinking because visceral and emotional factors can affect decisions in unexpected ways. For example, people may overattribute other people’s behavior to personal dispositions whereas overlooking situational causes or transient environmental influences on behaviors. In doing so, the decision maker falls prey to attribution bias. Case study findings suggest that some physicians overlook the effects of inaccessibility of healthy food choices and walking paths in low-income neighborhoods, or other social determinants of health and overattribute the obesity problem to obese individuals being lazy.

Groopman told a gut-wrenching story of an elderly African-American patient being labeled as noncompliant who suffered from congestive heart failure, diabetes, hypertension, coronary artery disease, and advanced rheumatoid arthritis. She had been repeatedly admitted to a major academic medical center. None of her previous physicians knew that she was unable to read the labels on the medicine bottles until an African-American internist recognized what the other physicians had overlooked. This physician paid attention to the social context of severe disadvantages of being a black woman in the rural Mississippi of the 1930s and was able to arrange for the patient’s daughter to be present at discharge and be informed of plans for care at home. The patient’s recovery was remarkable afterwards.

Such attribution bias appears to be fairly common. Patients’ weights significantly affect how physicians view and treat them. Patients with higher body mass index are also less likely to be perceived by physicians as medication adherent. Physicians ordered more tests for obese patients, spent less time with them, and viewed them with more negativity than nonobese patients. In a study of patient–physician communication over management of chronic pain, a physician was observed to be telling an elderly female African-American patient with disabling knee pain: ‘‘all you need to do is to lose 50 pounds. So you won’t be hobbling around with all the extra weight on your knees’’.

Anchoring And Availability Bias

Frank and Zeckhauser termed the ‘My Way Hypothesis’ for situations in which physicians would regularly prescribe a therapy that was quite different from the choice that would be made by their peers. Although it is possible that the physician chose that therapy because she had differential expertise, the My Way Hypothesis may also apply when a physician has had personal ‘good luck’ with it, a plausible heuristic, but one that falls prey to the availability heuristic, namely overweighting evidence that one can bring easily to mind. Groopman recounted an emergency department (ED) physician using a ‘studied calm’ approach to avoid anchoring and availability biases when a patient presented with symptoms suggestive of a kidney stone. Rather than going along with the kidney stone diagnosis made by the triage nurse, the ED physician asked what might be the worst-case scenario thereby avoiding these cognitive biases and correctly diagnosed a dissecting abdominal aortic aneurysm, a far more life-threatening emergency.

Therefore, there is systematic evidence that heuristics can frequently lead decision makers astray, particularly when probabilistic outcomes are involved, as is almost always the case with medicine. The power of the field of behavioral economics has developed from the broad insight that heuristics can lead to significantly suboptimal behavior. Application of behavior economics perspectives can help advance the understanding of micromanagement of patient demand.

What Is The Value Of This Research In Improving The Functioning Of Patient–Physician Interaction?

Rather than accepting the notion that agent behaviors are unobservable and noncontractible, research efforts using direct observation data available in other fields has shed some light on agent behaviors at the point of care were applied. Building on the insights from behavior economics, coupled with empirical evidence from direct observation of physician–patient interaction, a more informed understanding of how physicians manage demand at the point of care, more like Humans with frailties, rather than Econs who have full information and can do probabilistic decision analysis on the go can be attained. Better point-of-care clinical decision support systems, redesign staffing structure to provide effective support, implement incentives that are conducive for escaping the lull of the norm may be designed. Shared decision making has been shown to lead to better honoring of patient’s wishes and lower procedure-based service use. This line of research might continue to contribute to improving patient–physician interaction.

Coding communications within visits at the topic level is time consuming. Establishing inter- and intrarater coding reliability takes much effort and time. Some may question if the effort is worthwhile. It has been asserted that the findings have value in improving the functioning of patient–physician interaction. For example, it is noted that many topics compete for visit time, resulting in small amount of time being spent on each topic. A highly regimented schedule might interfere with having sufficient time for patients with complex or multiple problems. Efforts to improve the quality of care need to recognize the time pressure on both patients and physicians, the effects of financial incentives, and the time costs of improving patient–physician interactions.

Where This Research Area Should Go?

To understand how physicians manage patient demand, Simon and Fuchs’ admonishment about using good data must be adhered to. Direct observation using video or audio data has offered unique insights and can continue to do so. Multidisciplinary collaboration with researchers in other fields (e.g., health communication and medical education) for data and communication analysis empirical approaches can continue to be a fruitful endeavor. It would be important to have large enough sample size to enable the examination of causal relationship between communication characteristics and downstream patient-reported outcomes.

Increasingly, patients are communicating with physicians asynchronously via secure messaging through the electronic health records (EHR) and personal health record. The method for studying patient–physician communication must evolve accordingly to take advantage of the EHR as an additional source of data for data mining. Some EHR, for example, EpicCare(EPIC) EHR, offers an unobtrusive portal to study time use through analysis of EPIC access log, a feature in EPIC EHR designed for monitoring access to patient’s EHR for security and privacy concerns. The EPIC access log tracks the user of the EHR, time of access, device from which the access was made, and EHR functional location of the access, for example, progress note, medication list, phone encounter, and secure messaging. EHR enables the authors to leverage existing ‘behind-the-scenes’ data to study how much time clinicians spend on performing tasks. Natural language processing software is making progress in harvesting useful information from this data source to inform research on physician behaviors. Continued effort can bring promises to the field.

Sensitivity to institutional changes taking place in health care is essential. The health care delivery system is undergoing fundamental changes. To be relevant, one must be mindful of the institutional context and delivery system characteristics – dynamic rather than static – in which physicians manage patient’s demand at the point of care. The redesign of clinical care processes and payment incentives can be informed by this type of research. For instance, it has been observed that quite a bit of time is being spent on listening to the lung, doing the traditional litany of system review where old information was rehashed with no apparent value. For example, if a patient’s grandparent died of cancer 20 years ago, repeating this information at every periodic health exam offers no new information. How much value do these clinical routines offer? Should reimbursement continue to be triggered by following these routines? Substituting tradition-based medicine with evidence-based medicine may free up some time for physicians to do more shared decision making on more important issues in patient’s view at the point of care to maximize the benefit of time.

Even the definition of point of care needs to be expanded to accommodate new models of care delivery. For example, the emergence of team care makes the function of care managers and other providers on the team important forces that might affect how patient demand is managed in the world of team care. The literature is silent on how team communicates among its members and with patients, let alone the impact on demand.

Tools designed to reduce information asymmetry and uncertainty in decision making are being developed, tested, and prescribed for patients. These decision aids – for example, for prostate cancer, breast cancer, depression treatment, and end-of-life care preference: – are making significant changes in patients’ understanding of options and assisting them to alter their demand for health care services. How physicians use these decision aids and how they respond to patient’s modified preference can be important areas of research. As the field pays more attention to patients with multiple chronic conditions, more refined clinical decision support systems that can be tuned to accommodate demands from multiple morbidities will be a welcome addition to practicing physicians. Direct observation data can also provide unique insight on how demand from each condition is managed at the point of care with multimorbidity patients. For those interested in accessing the video recordings that have been used in the research, they may access them if they are medical educators or researchers working to improve the doctor–older patient relationship.


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Physician-Induced Demand
Price Elasticity of Demand for Medical Care