Visit Time & Again $1 Sturtevant

• Journal Listing
• Health Serv Res
• v.42(5); 2007 Oct
• PMC2254573

Health Serv Res. 2007 Oct; 42(v): 1871–1894.

Time Allotment in Primary Care Office Visits

Abstract

Objectives

To utilize an innovative videotape analysis method to examine how dispensary fourth dimension was spent during elderly patients' visits to primary care physicians. Secondary objectives were to identify the factors that influence time allocations.

Data Sources

A convenience sample of 392 videotapes of routine function visits conducted between 1998 and 2000 from multiple principal care practices in the United States, supplemented past patient and medico surveys.

Research Design

Videotaped visits were examined for visit length and time devoted to specific topics—a novel approach to report fourth dimension allocation. A survival analysis model analyzed the furnishings of patient, physician, and physician practice setting on how dispensary time was spent.

Principal Findings

Very limited amount of time was defended to specific topics in office visits. The median visit length was 15.7 minutes covering a median of six topics. Nearly five minutes were spent on the longest topic whereas the remaining topics each received 1.1 minutes. While fourth dimension spent by patient and medico on a topic responded to many factors, length of the visit overall varied little fifty-fifty when contents of visits varied widely. Macro factors associated with each site had more influence on visit and topic length than the nature of the trouble patients presented.

Conclusions

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.

Keywords: Visit length, patient–physician interaction, quality of care, clinical exercise pattern, chief care

Time is a scarce resource in a md's office practice. How physicians use dispensary time has important implications for quality of care, patient trust (Fiscella et al. 2004), malpractice suits (Levinson et al. 1997), and is one of the bases of physician payments (Hsiao et al. 1988). Mechanic, McAlpine, and Rosenthal (2001) reported that the average length of a medico visit had increased from 16.3 to 18.3, between 1989 and 1999, based on survey data from the National Ambulatory Medical Intendance Survey (NAMCS) and the Socioeconomic Monitoring System (SMS). Data from directly observation of primary care office visits by nurse researchers chosen into question these results, all the same. Yawn et al. (2003) found that principal care doctor office visits lasted about 10 minutes. Further, Gilchrist et al. (2004) plant physicians or their office staff over-reported visit length by most four minutes when completing the NAMCS encounter forms.

Patient–medico conversations are circuitous, multidimensional, and multifunctional (Mishler 1984). Visits vary not just in length but also in the division of time among topics. Patients typically nowadays multiple complaints during an role visit requiring physicians to dissever time and resources during a visit to deal with competing demands. A unique and critical part of primary care physicians has been to provide patients with an "advanced medical home" where complex comorbidities are diagnosed and treated. Braddock et al. (1999) analyzed audiotapes of office visits to primary intendance physicians and surgeons and reported a median of three patient concerns per visit. Beasley et al. (2004) reported an average of three.9 concerns discussed with elderly patients by family unit physicians. Studying how physicians utilise clinical time through examining the contents of the visit is also of import to illuminate the procedure of intendance (Donabedian 2005). Our review of the literature (Hsiao et al. 1988; Charon, Greene, and Adelman 1994; Thompson et al. 2003; Heritage and Maynard 2006) and personal communications with other researchers atomic number 82 us to believe that this study is the first to direct measure the actual amount of time spent by patients and physicians on topics occurring during part visits.

In this paper, nosotros took reward of a unique data fix consisting of videotaped elderly patients' visits with their primary care physicians in 3 distinct organizational settings: salaried group do in an academic medical middle, a managed care group (MCG) practice, and fee-for-service inner city solo (ICS) practitioners with an Contained Practice Association contract. We examined not only the length of visits, simply more chiefly, the content of visits in terms of units of clinical conclusion making we refer to every bit "topics," operationalized every bit clinical issues raised by either participant. Our arroyo was in the spirit of the multidimensional interaction analysis (MDIA) system, which codes an interaction directly from an audiotape of the visit based on topics sequentially introduced by patient or physician. The MDIA lists 36 categories subdivided into v major content areas: biomedical, personal habits, psychosocial, patient–doc relationship, and other (Charon, Greene, and Adelman 1994). We partitioned a visit into similar topics, and took a step farther by recording the amount of time spent on each topic by patient and physician. Our arroyo allows us to examine how much time is dedicated to specific topics, and the factors that influenced how clinical fourth dimension is allocated.

This paper addresses a series of questions about visits and topics within visits. First, what was the length of a main care role visit for these elderly patients? 2d, how many topics were discussed, and how much time was devoted to each topic? 3rd, what were the topics of discussion and how did the length of fourth dimension speaking by patient and md vary beyond different types of topics? Lastly, nosotros analyze the influence of patient, md, and physician's practice setting characteristics on how clinic time was spent using duration (or survival) analysis. Our main goal is to characterize md–patient encounters in a new way, in social club to study how physicians and patients classify the scarce resource of doctor time to deal with the circuitous set of problems arising in an part visit.

DATA AND METHODS

This paper conducts analyses of videotapes collected for another report based on a convenience sample of role-based physicians and their patients in iii types of practices (Cook 2002). The practices included a salaried medical group as part of an Bookish Medical Eye (AMC) in the Southwest, a managed care grouping (MCG) in a Midwest suburb, and a number of fee-for-service inner city solo (ICS) practitioners in a Midwestern metropolis. These sites were called to include various practice forms and representation of patients and physicians from racial minority groups.

Participants

The recruitment effort resulted in a sample of 35 physicians, all of whom had completed their training at the time of the initial study. Patients had to exist at least 65 years of historic period to be eligible for the original written report, identify the participating dr. every bit their usual source of intendance, and provide informed consent. Specifically, patients were identified from their master intendance physicians' patient panels provided by office managers of the participating clinics. When these patients came to the participating dispensary for a visit, regardless of the nature of the visit (east.thou., acute upper respiratory infection, or for routine checkup for diabetes or hypertension), they were invited to participate in the study. If they expressed willingness to participate, informed consent was obtained and their visits were taped. Patient participation rates ranged from 61 to 65 percent at the 3 sites. The final sample independent 392 videotaped visits.¹ Details of participant recruitment take been reported elsewhere (Tai-Seale et al. 2005).

Compared with national information (American Medical Clan 2001), our physician interpretation sample is similar in gender composition merely has fewer physicians in the extremes of the age distribution. African–American physicians were overrepresented in our information (14 percent, compared with 6 pct nationally). Our patient sample is similar to national data on elderly patients in age distribution, and living arrangement (U.S. Census Bureau 2001) but dissimilar in having more educated and fewer married patients (Federal Interagency Forum on Aging Related Statistics 2000).

Videotape Coding

Coding of the videotaped visits consisted of four major components: identifying topics, determining the talk time, coding the dynamics of the talk, and recording additional measures. See Appendix A for details on training of the coders.

Identifying Topics

Coders beginning carefully reviewed the entire video to decide the nature and number of topics raised during the visit. Following the MDIA group (Charon, Greene, and Adelman 1994), a topic was regarded as an result that required a specific response by the physician or patient. Each patient-raised symptom was treated equally a topic unless the patient connected the symptoms inside a "common sense" group, if so, the grouping was treated as a topic. An example could be that a patient talked nigh coughing and headache. He mentioned them ane after another and indicated his worry most having bronchitis. Applying common sense nigh upper respiratory infections, the patient had grouped the symptoms of coughing and headache together. Rather than coding two dissever topics of coughing and headache, we combined them into one topic labeled "worried about bronchitis."

As mentioned before, the MDIA has 36 topics and five major content areas. In recognition of the prevalence of depression, feet, and other mental illnesses treated in primary intendance, we formed a mental wellness content expanse, which is a subset of the psychosocial content area in the MDIA. Nosotros identified 36 topics pertaining to half-dozen major content areas: biomedical, mental health, personal habits, psychosocial problems, patient–md human relationship, or other topics. Each topic was assigned a number from a predetermined list of 36 topics (Charon, Greene, and Adelman 1994).

Effigy i depicts the flow of chat during ane office visit in the information and illustrates how this is grouped into topics for coding. The visit started with the md noting the photographic camera upon his arrival: "They want to see how I talk to my patients." The patient smiled and started to tell the physician about her condition later on the hip fracture. She then told the physician that she had been depressed. The physician empathized past stating that a lot had happened since the death of her hubby. She recounted the days preceding her married man's death and her son's reaction to his death. The doctor brought the topic back to her hip. He reviewed the pain medications prescribed for her hip pain (Propoxyphene), and and so the antidepressants (Paroxetine and Amitriptyline) that she was taking. She expressed business organization over "sleeping too much" and questioned if the medicines were "too strong." The physician told her that he thought that she was doing just fine and he would not alter the medications. He then directed the chat to her backache. During the course of the discussion on backache, they revisited the hip fracture topic. Lastly, they briefly discussed her gum ache and dentures. Subsequently reviewing this encounter, the coders detected five topics: (1) the report, (2) status of hips, (3) low, (iv) backache, and (5) mucilage pain. The give-and-take about her married man'southward death was brought up and addressed within the context of her depression. Therefore, conversations on his death were counted in the depression topic.

Flow of Conversation during a Visit

Talk Time and Topic Length

Patient talk time was measured every bit the length of time the patient spent talking before discussion of the topic concluded. Talk time of a patient'south companion if present at the visit was included in patient'due south talk time. Physician talk fourth dimension was divers as the length of time the physician spent talking before the topic ended. As the two took turns talking, each person'southward talking fourth dimension earlier the other started talking was recorded and then added to get the total length of fourth dimension each person spent talking. Topic length is measured by the sum of the time—in either talking or in silence every bit long every bit both patient and physician are in the room—that elapsed between the beginning and the end of all instances of a topic. The sum of the talk time by the patient and physician may exist less than the total topic length considering at times, both parties sabbatum in silence,^two the doc viewing or writing in the chart. Effigy 1 illustrates how the fourth dimension variables are coded.

Verbal or Nonverbal Cues

The coders were trained to record uncertainty and exact or nonverbal cues of emotional distress during discussions of each topic. Expression of uncertainty was indicated by statements that conveyed the idea that the speakers were not sure about the accuracy of their statements (Gill 1998). Hesitations and words or phrases such as, "we don't know …""may,""might,""is it true that …," and speculative expressions such as "it may be true that …" were taken to propose dubiety. Vocalized pauses, which are the ums, uhs, and ahs, the spoken equivalents of pharynx clearing and false starts, that exist in one'southward speech (Wilson 1993), were besides taken into account in coding doubt. The coders were reminded to pay shut attending to the context in which these expressions were uttered to distinguish expression of doubt from expression of politeness (Gill 1998).

Verbal cues of emotional distress included expressions such as "I'm such a handbasket case,""What else is there to alive for?" Nonverbal cues of emotional distress include depressed confront, downward gaze, self-touching, drooping posture, and slowed speech (suggesting depression) or fidgety hands, darting eyes, and a however upper body (suggesting feet) (Gattellari et al. 2002; Eide, Graugaard, and Finset 2004; American Psychiatric Association 1994). Two binary variables record whether the patient showed either cue while discussing each topic. Because of their correlation, we only included nonverbal cues in the regression assay.

Survey Data

Surveys of patients and physicians complement the video data. Variables from the surveys were chosen for analysis based on enquiry near how patient–md interaction is influenced by patient health (Bertakis et al. 1993), gender and race (Roter, Hall, and Aoki 2002; Balsa, McGuire, and Meredith 2005), didactics (Waitzkin 1985), and physician gender (Roter, Hall, and Aoki 2002; Roter and Hall 2004). Patient's wellness status was measured by normed SF-36 scores (Ware, Kosinski, and Dewey 2000). The length of the patient–dr. relationship was measured by the number of years the patient had seen the md (Waitzkin 1985).

Empirical Specification

Topic and visit-level analyses were conducted separately to describe the length of time spent and the determinants of time at both levels. At the topic level, our information contain multiple observations (i.eastward., topics) for each patient–physician dyad (visit). We used mixed-level information methods to account for the clustering at the dyad level. The dependent variables were patient talk time, physician talk time, and topic length. At the visit level, the dependent variables were visit length, total patient talk fourth dimension, and total medico talk time.

Nosotros used a survival model to clarify the likelihood that the topic or visit would finish, given how much fourth dimension had already been spent on it. To test duration dependence, we use the Weibull proportional hazard function (Cleves, Gould, and Gutierrez 2004). At the topic level

(1)

where t is time in seconds; β′=(β_one, …, β _j , …, β _J ) is a parameter vector for covariates. x′ _i =(x _i1, …, ten _ij , …, x _iJ ) is a data vector; 10 _i correspond topics, i=1, …, Thousand where K is the total number of topics; j=1, …, J is the alphabetize for explanatory variables which included: topic (biomedical, personal habits, mental health, psychosocial, patient–doctor relationship, and other issues topic; patient initiation; physician showed dubiety, patient showed uncertainty, patient showed mood problem nonverbally), site (AMC, MCG, and ICS), patient (age, gender, health condition, and education), doc (gender), patient and physician dyad (years patient has seen this physician). (African-American patients and physicians were concentrated in the ICS preventing us from conducting an analysis of race of patient or medico dissever from site.)

Therefore, ten _ij represents the explanatory variable's value for topic i and explanatory variable j. In equation (1), h ₀(t) is the baseline adventure charge per unit which can be modeled as

(two)

where s and β₀ are parameters to estimate. southward is known as the shape parameter which represents the presence of duration dependence if information technology is different than 1. β₀ is a scale parameter in the Weibull model. Interpretation of coefficients in the Weibull model equally hazard ratios (HR) is not straightforward. We evaluate the quantitative relationship between a change in covariates and the modify in length of fourth dimension at a abiding survival probability. We can then calculate the percentage modify in talk time as a upshot of an increment in an explanatory variable, (the steps are shown in Appendix B) which is

(3)

equation (3) enables u.s.a. to summate, belongings constant the survival probability, how changes in key explanatory variables would influence the length of time spent on a topic or a visit.

For the topic level analysis, the nature of the topic was captured past five binary variables representing the major content areas, with biomedical topic as the comparison group. Additional variables include binary variables for initiator of the topic, physician showing uncertainty, patient showing uncertainty, patient showing cue of mood problem nonverbally.

For the visit level assay, we examined the effects of visit content complexity on talk times. Complication was measured by the percent of time spent on each of the half dozen groups of topics out of full talk fourth dimension.³ In constructing a mensurate for patient initiation, we created a variable for the share of the topics in all of the topics in the visit that were initiated by the patient. A similar arroyo was used to create variables for the share of topics in which the physician had shown uncertainty, and the share of topics in which nonverbal cues of mood problems were observed in the patient. Analyses were performed in STATA, version nine (STATACorp 2003).

RESULTS

The 392 videotaped visits contained two,557 topics which represented all of MDIA topics with the exception of elderberry abuse which was non present in our sample. Of those topics, 77 pct of the topics (1,977) were discussed with 27 male physicians whereas 23 percent of them (580) were discussed with eight female physicians.

Univariate and Bivariate Analyses

Average length of visits was 17.4 minutes. The median length of visits was 15.7 minutes. The median talk fourth dimension past patient was 5.3 minutes, and doctor, 5.2 minutes. The median time during which neither part spoke was 55 seconds. (Note: unlike the example of the hateful, the sum of the medians is not the median of the sum.) The boilerplate number of topics in a visit was half-dozen.5 (median=half dozen, minimum=ane, maximum=12; Table 1). Owing to the skewness of time variables, we study the medians in descriptive statistics.

Table 1

Descriptive Statistics by Practice Settings

	Full Sample	Academic Medical Middle	Managed Care Group	Inner City Solo Practice
Median time length (minutes)
Visit (Due north=392)
Patient talk time	five.three	8.0	4.0**	1.8**
Physician talk time	5.2	5.ix	iv.7**	ii.6**
Visit length	15.7	23.three	13.iv**	nine.7**
Major topic (Due north=392)
Patient talk time	2.0	3.0	ane.vii**	0.nine**
Physician talk fourth dimension	2.3	2.4	2.3	1.7
Topic length	5.3	6.7	iv.viii**	iii.2**
Minor topics (Northward=2,059)
Patient talk time	0.5	0.7	0.four**	0.2**
Physician talk time	0.4	0.v	0.4**	0.3**
Topic length	ane.1	1.iv	0.9**	0.seven**
Patient characteristics (North=385)
Mean historic period in years	74.3	75.3	73.eight	73.four
% Female person	66.8	67.vii	65.five	71.9
% White	81.3	73.9	96.5**	0**
% African American	10.three	2.3	2.half dozen**	100**
Education, % college or more	44.4	56.2	xl.2**	25.8**
Hateful SF36 physical role functioning	38.5	36.5	39.2	41.5
Hateful SF36 actual pain	41.2	40.six	41.4	42
Mean SF36 social role functioning	34.1	33.eight	34.ii	35.iv
Physician characteristics
% Female person	22.9	20.0	23.viii	25.0
% White	82.nine	80.0	100**	0**
% African American	xiv.3	10.0	0**	100**
Patient–physician dyad characteristics
Years patient seen this md	vi.4	3.2	6.9**	xvi.0**
Time share of topics in visits (N=392)
Biomedical topics (%)	78.6	73.7	81.2**	81.9
Mental wellness topics (%)	4.4	4.7	4.three	1.half-dozen
Personal habit topics (%)	4.2	4.2	iii.9	v.eight
Psychosocial topics (%)	9.ii	13.3	vii.4**	5.vi**
Patient–md relationship topics (%)	i.4	ii.0	1.2	6.8
Other topics (%)	2.two	2.0	two.0	iv.4
Topic characteristics (N=two,448)
Biomedical topics (%)	71.v	69.5	72.8	71.0
Mental health topics (%)	2.9	3.vii	2.43	1.9
Personal addiction topics (%)	6.6	five.5	seven.0	9.7
Psychosocial topics (%)	12.2	13.9	11.6	vii.7
Patient–physician relationship topics (%)	2.7	3.4	2.4	2.6
Other topics (%)	four.1	iv.0	3.9	seven.1
Patient initiated topics (%)	44.3	52.0	40.ii**	38.1**
Doc shown dubiety (%)	4.6	5.6	iii.3**	9.7**
Patient shown uncertainty (%)	xix.4	24.4	sixteen.5**	17.4**
Patient shown nonverbal cue of mood disorder (%)	7.7	10.7	half-dozen.0**	v.2**

Nosotros separated out the longest topic (which will be chosen the "major" topic) from the rest of the topics, which we will call "small" topics. We noticed a significant reduction between the time spent on major (5.25 minutes) and minor topics. During major topics, patients talked for 2.03 minutes and physicians, two.31 minutes. The modest topics received 1.ane minutes during which patients spoke for one-half a minute and physicians, 0.4 minute per topic (Table one).

Bivariate analyses show that, in comparison with 23.3 minutes spent at the AMC, the length of visit were significantly shorter at the MCG practice (xiii.4 minutes, p<.01) and the inner city solo practices (ICS; 9.seven minutes, p<.01). Patients at the MCG (4 minutes, p<.01) and ICS (1.8 minutes, p<.01) spoke significantly less than patients at the AMC (8 minutes). Physicians at the MCG also spoke much less (4.seven minutes, p<.01) than their colleagues at the AMC (5.9 minutes). ICS physicians spoke less than half (2.6 minutes, p<.01) as much as AMC physicians. Among the major topics, patients at the AMC spoke significantly longer (three.0 minutes) than patients at the MCG (ane.vii minutes, p<.01) and ICS (0.9 minutes, p<.01). Similar patterns existed for topic length but not for doc talk fourth dimension. It is possible that the way in which physicians at the MCG and ICS spoke signaled patients to limit their "air fourth dimension." The pattern persisted when time was examined across minor topics (Table i).

The majority of topics (72 percent) pertained to biomedical issues whereas mental health topics composed two.9 percent. Vii percent of the topics were devoted to personal habits. Twelve pct of topics were about psychosocial matters. Discussion of the patient–md relationship accounted for 3 percent of the topics while 4 percent of the topics were other topics including pocket-sized talk. Bivariate statistics on all explanatory variables are presented in Table one past exercise settings.

Survival Analyses of Talk Duration

Tables 2 and ​ iii show the results from the topic-level and visit-level analyses, respectively, expressed in run a risk ratios (HRs). In Table 2, we present findings on the major topics and minor topics for comparison. Because no visits contained a major topic on the patient–dr. relationship, that variable was not included in the analysis of major topics.

Table 2

Hazard Ratios of Topic-Level Determinants of Patient and Physician Talk, and Topic Durations

	Patient Talk				Physician Talk				Topic Length
	Major†	%	Modest†	%	Major	%	Minor	%	Major	%	Modest	%
Topic
Mental wellness topic‡	0.37**	85	0.67**	42	0.87		1.xl*	−27	0.52**	37	1.07
Personal habit topic‡	0.77		1.31**	−21	1.20		one.70**	−40	0.83		1.58**	−31
Psychosocial problems topic‡	0.48**	57	0.85		1.76**	−28	ane.67**	−39	0.95		1.22*	−15
Patient–Doc relationship topic‡			1.74**	−39			ane.22				i.59**	−31
Other issues topic‡	1.07		one.32		iii.97**	−55	one.91**	−46	2.26		1.67**	−34
Patient initiated topic	0.81		0.72**	34	1.53**	−22	0.96		1.18		0.81**	18
Physician showed uncertainty	0.60**	56	0.84		0.57**	38	0.85	17	0.64**	24	0.86
Patient showed uncertainty	i.00		0.89		0.94		0.79**	26	0.85		0.77**	24
Patient showed mood problem nonverbally	0.69		0.92		one.44*	−19	1.01		0.89		0.xc
Site
Managed care group practice (MCG)§	ane.58**	−26	i.65**	−36	1.15		1.29**	−22	ane.60**	−20	1.59**	−31
Inner metropolis fee-for-service solo (ICS)§	2.93**	−54	2.25**	−51	1.12		1.30		1.98*	−28	1.85**	−39
Patient and doctor
Patient age in years	0.98*	1	1.00		0.99		ane.00		0.99		1.00
Female patient¶	0.97		0.93		0.73**	twenty	0.88		0.78		0.93
Having college or more years of educational activity	0.76*	19	0.98		0.88		0.97		0.83		0.99
Patient physical role functioning	1.01*	−i	one.01**	−1	i.01**	−1	1.00*	−0.4	1.01**	0	1.01**	−ane
Patient actual pain	0.99		1.00		0.98**	1	0.99		0.98*	i	ane.00
Patient social role functioning	0.99		ane.00		0.98		ane.00		0.98		ane.00
Physician is female person¶	1.11		0.92		one.09		1.02		i.07		0.93
Years patient seen this Medico	i.02*	−i	1.01*	−1	1.01		ane.01		1.02**	−ane	1.01*	−1
Shape parameter (S)	ane.62**		1.13**		i.73**		1.04**		2.07**		ane.25**
Number observations	365		1,823		365		i,878		365		i,890

Table iii

Hazard Ratios of Visit-Level Determinants of Patient Talk, Medico Talk, and Visit Duration

	Patient Talk		Medico Talk		Visit Length
	HR	%	60 minutes	%	60 minutes	%
Topic
Personal habit topic (%)†	i.00		1.00		one.00
Psychosocial bug topic (%)†	0.98**	0.8	1.00		0.99
Patient–Md relationship topic (%)†	1.01		0.98		1.00
Other issues topic (%)†	1.01*	−0.7	i.01		one.02
Mental health topic (%)†	0.99**	0.7	one.00		1.00
Patient initiated topic (%)	0.99**	0.4	1.00		1.00
Physician showed dubiousness (%)	0.99*	0.3	i.00		1.00
Patient showed uncertainty (%)	1.00		1.00		1.00
Patient showed mood problem nonverbally (%)	one.00		1.00		one.00
Site
Managed intendance grouping exercise (MCG)‡	ii.47**	−36.8	one.70*	−22.vii	2.89**	−32.5
Inner city fee-for-service solo practices (ICS)‡	vi.58**	−61.6	two.51		3.24**	−35.4
Patient and physician
Age in years	0.98		0.90		0.97**	ane.0
Female patient§	0.95		0.74*	xvi.1	0.91
Having college or more than years of education	0.90		0.90		0.85
SF36: physical office functioning	1.01*	−0.half dozen	i.01**	−0.4	1.01**	−0.3
SF36: bodily pain	0.99		0.97**	1.3	0.98**	0.8
SF36: social function functioning	1.00		1.00		one.00
Female doc§	0.87		1.04		0.74
Years patient seen this MD	1.02**	−one	1.01		i.01
s	1.97**		2.05		ii.69**
Number of observations	366		366		366

A Hour >1.0 ways that talk is more likely to end, i.eastward., the talk length is shorter, in comparing with the reference group for chiselled variables and an increment in continuous variables. For statistically significant variables, we written report a quantitative interpretation of their effects on duration of time co-ordinate to equation (3) presented before.^iv

Nature of Topics

Patient Talk

The topic-level analysis showed that, in comparing with patient talk time on biomedical issues, patients talked 85 per centum longer on a mental wellness outcome (p<.01) if information technology was the major topic, and 42 percent longer otherwise (p<.01). They spoke the aforementioned length on a personal habit topic as on biomedical if it was the major topic, merely 21 percent less otherwise (p<.01). The reverse was true for psychosocial topics: 57 percent longer (p<.01) versus no difference (Table ii). The visit level analysis showed that patients talked 1 percentage longer in response to a i pct increase in the time share of topics on mental health or on psychosocial bug (p<.01; Table 3).

Physician Talk

When mental wellness was the major topic, physicians talked no longer than on biomedical topics. When it was a modest topic, however, physicians talked 27 per centum less (p<.01) than on biomedical topics. Similarly, no difference for a personal habit issue equally the major but talk was 40 percent shorter (p<.01) otherwise. Physicians spent 28 per centum less time on psychosocial topics (p<.01) when they were major topics and 39 percent less time when they were minor topics (Tabular array 2). At the visit level, no significant difference was constitute in doc talk fourth dimension based on the fourth dimension share of topics (Table 3).

Total Length of Topic or Visit

There was only one pregnant determinant of the length of topics: mental health topics were 37 percent longer than biomedical topics (p<.01) when they were major topics and the same when they were modest topics. All other minor topics were shorter than biomedical topics (Table 2). At the visit level, time shares of topics did not bear upon length of visits (Table three). These results advise that intravisit time resource allotment across topics did non influence how long the visit lasted.

Initiator of Topics

Patient Talk

At the topic level, patients' talk length was not significantly influenced by their initiation of major topics. They spoke 34 percent longer, withal, when they initiated pocket-size topics (p<.01). At visit level, the share of topics that were initiated by the patient had a pocket-sized but significant consequence: (0.iv pct longer, p<.01; Table 3).

Physician Talk

Physicians spoke 22 percent less during major topics initiated past patients (p<.01) but no difference during pocket-sized topics. Initiation had no significant upshot at the visit level (Tables 2 and ​ iii).

Total Length of Topic or Visit

Initiation had no significant effects on major topics but patient initiation increased minor topic fourth dimension past eighteen pct. These results imply that, to make up for patients' longer talk, physicians spoke less when patients initiated a major topic. Consequently, neither the length of the topic nor of the visit was affected by patient'due south initiation of major topics. Patients, on the contrary, spoke more on small topics that they had initiated and those topics were longer every bit a event.

Uncertainty and Cues of Mood Problem

Md dubiety was associated with 56 per centum longer patient talk fourth dimension (p<.01) in major topics (Table two), and 0.3 percent longer (p<.05) in the visit (Table 3). When uncertain, physicians talked 38 percent longer (p<.01) on major topics (Tabular array 2), and the major topics lasted 24 percent longer (p<.01; Tabular array two). Patient uncertainty did non influence time on major topics but was acquaintance with 26 percent longer physician talk fourth dimension (p<.01) and 24 pct longer topic length (p<.01) on minor topics. As for cues of mood problem, physicians spoke xix per centum less with patients who showed nonverbal cues of mood problems during the major topics (p<.05; Table 2). Expression of uncertainty did not increase total visit length.

Physician Practice Setting

Patient Talk

In comparing with the AMC, patients talked 26 percent less at the MCG (p<.01), and 54 percent less at the ICS (p<.01) during discussion over major topics. Similar furnishings were plant among modest topics (Table two). Visit-level results were like with the MCG existence 37 percent shorter (p<.01), and ICS being 62 percent shorter than the AMC (p<.01; Table 3).

Physician Talk

Doc talk time did not differ significantly across sites during major topics but was 22 percent shorter amidst pocket-sized topics at the MCG. At the visit level, however, MCG physicians talked 23 percent less than AMC physicians (p<.05).

Total Length of Topic or Visit

Major topics were 20 per centum shorter at the MCG (p<.01) and 28 percent shorter at the ICS (p<.05). Pocket-size topics were even shorter at both MCG and ICS (Table 2). Visits were 33 percent shorter at the MCG (p<.01) and 35 percent shorter at the ICS (p<.01; Table 3).

Patient, Physician Characteristics, and Length of Patient–Md Relationship

Patients with college level or higher education spoke 19 percent longer in major topics (p<.05) but not on pocket-sized topics. Physicians spoke 20 percentage longer to female patients during major topics (p<.01) and 16 percent longer during visits (p<.01). Dr. gender had no effect on talk fourth dimension.

Word

This report offers several new findings with respect to the corporeality of fourth dimension devoted to specific topics in office visits. Nosotros found that a very limited amount of time was allocated to topics. A median of only 5 minutes was spent on even the major topic in a visit. We also found that visit length was insensitive to the contents of a visit. Longer fourth dimension spent on major topics seems to have been compensated past limiting the time allocated to minor topics, therefore leaving the visit length more or less the same. Determinants of topic length differed between major and minor topics. For example, if mental health was the major topic, the topic lasted longer than biomedical topics only was the aforementioned length equally biomedical topics if it was a minor topic. Also, a doc's expression of dubiety was associated with longer topic length if the topic was a major topic, not so if it was a minor topic. Farther, macro factors related to practise settings, east.g., organizational structure and physician payment incentives, appear to have more influence on visit length than micro—inside visit factors. Some implications on these findings are discussed below.

Topics and Time Allocation

Master intendance visits indeed incorporate a large number of topics covering diverse subjects. With only about 2 minutes of talk time on even the major topic from each speaker, we could not assist but wonder how much is accomplished during such a brief exchange. Future research should assess whether the corporeality of time for major topics or a particular type of topic was "sufficient" to facilitate effective data exchange and patient-centered care.

While there are typically three to four biomedical topics raised in most visits (Braddock et al. 1999; Beasley et al. 2004), a broader definition of topics finds more subjects of word. All compete for visit time. Our most intriguing finding is that while time spent past the patient and dr. on a topic responds to many factors, time of the visit overall is much less malleable. A physician could adjust to a patient's presentation of a fourth dimension-consuming problem past either extending the patient'southward visit and taking a niggling time away from the many other patients seen in the grade of a mean solar day, or, keeping this patient's visit most the same by restricting time on other problems the patient might have. It appears from our data that the second strategy predominates. Therefore, if visit lengths are rigidly fix, patients with more health concerns that would have required more time for history-taking and counseling could end upwards receiving less fourth dimension than they demand. Competing time demands during office visits may contribute to lower overall quality for vulnerable older people (Min et al. 2005) if physicians are less inclined to spend the fourth dimension and cognitive energy to engage in these time-consuming processes.

Incentives in prevailing physician payments favor process-based patient care over time-intensive evaluation and management care. Much of what physicians do to assist their patients during an office visit would be nearly impossible to be captured in a fee schedule or a pay-for-performance organisation. Further, psychosocial aspects of health and health care accept fourth dimension to address. For example, issues such as a awaiting motion to nursing home, or a physician's retirement and the handing over of the patient to some other doc, tend to exist nether-represented in medical records. Furthermore, some parts of the conversation aim at building rapport or easing tension, eastward.g., telling jokes. While nigh of these subjects do not require medical expertize, they occur ofttimes during office visits, and probably influence the effectiveness of advice. Overlooking their influence on how dispensary time (hence doctor effort and resources) is spent, yet, may distort incentives for quality effort because they may well represent the emotional labor a md is performing. As long as physicians are expected to relate to their patients in a personal and compassionate style (Suchman 2006), they demand to be given resources and incentives to develop and sustain such relationships. A payment system that offers doc flexibility in interaction content and fourth dimension can be very desirable for providing patient-centered care. This could exist a fruitful topic for future research, mayhap by sequential analysis of the topics. Information technology could take implications on medical education and continuation of training in effective clinical fourth dimension management.

Length of Visit and Practice Setting Effects

The average visit length in our sample of elderly patients was 17.4 minutes (median, 16 minutes)—quite close to the visit lengths reported in previous studies (Braddock et al. 1999; Mechanic, McAlpine, and Rosenthal 2001), which included both elderly and younger patients. The variations in visit length across practise sites suggest that dissimilar patients go dissimilar treatments. Besides influences of financial incentives and organizational cultures (Wolinsky and Corry 1981; Hillman, Pauly, and Kerstein 1989), educational mission at the AMC could accept caused visits at the AMC to be longer visits. Fortunately, nosotros were able to examine the influence of medical students on the length of visits. A medical student was present in merely 12 out of the 147 visits at the AMC and none were at the other practices. The median lengths of these 12 visits (24.viii minutes) were compared with the other 135 visits (23.0 minutes). While they differed by nearly ii minutes, the difference was nonsignificant.

Owing to the modest number of sites in the study, however, we view the across site comparisons as descriptive just. Furthermore, findings about the inner city practices are more complex to interpret, due to the unique combination of exclusively African American patient–physician pairs.

In clinical practice today, information technology appears, visit lengths may be prescribed by physicians' practice settings. Physicians are often held to daily patient volume targets that can also limit the amount of fourth dimension they spend with each patient. Hence, examining the length of visit may not provide much new information. This ascertainment is supported past the results from our visit-level analyses, which show that the site indicator is the dominant determinant of time at the visit level. Future studies aimed at better understanding of patient–physician interaction can benefit from a more in-depth analysis as was done in this study.

Influence of Patients

Patients who initiated major topics were met with significant reduction in md talk fourth dimension during the major topic in the visit. This may suggest that physicians did not view those topics as important every bit patients did. Similarly, doc talked much less with patients when patients showed mood bug nonverbally. It raises a question near whether physicians feel disinclined to appoint patients who announced depressed or anxious. Additional research should be pursued to examine the content and rapport of interaction in these topics.

Similar to previous findings (Waitzkin 1985), patients with at to the lowest degree some college instruction spoke significantly longer during the major topic in a visit. This suggested that better-educated patients might have prioritized how they would apply the visit time so that they could spend sufficient time on the topic that was most important to them. Other patients can be encouraged to program alee to brand sure that they are heard on pertinent issues. Alternatively, physicians might be less inclined to cut off the talk of a patient with a college socioeconomic status.

Probably the best-known piece of work related to ours is Roter's Interaction Analysis Organization (RIAS; Roter 1977) which analyzes visits by coding "utterances," defined as complete thoughts expressed by the patient or md. As ane of the almost widely used systems for analyzing patient–md communication, RIAS provides a wealth of information based on aggregate utterance counts, on communication behaviors pertaining to data gathering, patient education and counseling, rapport building, and partnership edifice (Levinson and Roter 1995; Roter et al. 1997; Roter, Hall, and Aoki 2002). The RIAS is oriented to measuring and evaluating communication process (Wasserman and Inui 1983), rather than time or topics discussed, and thus serves a dissimilar purpose than our investigation. Our approach direct observes time pressure and competing demands within the visit coming from dissimilar issues facing the physician and patient, thereby offering new insights on patient–dr. interaction.

This study has some limitations. For case, we do non have information from the medical records which could provide additional data on patient's history, nor do we have information on previous or subsequent visits. Farther, the convenience sample limits the external validities of the findings. These limitations are oft shared with other research on patient–dr. interactions. Additional studies are needed to replicate the arroyo on other patient age groups and practice settings. The impact of such findings on clinical practice and policy will be stronger if consistent patterns are identified, using this innovative approach to examine how clinical time—a critical resource in wellness intendance—is used. Grounding research in the direct assay of the conduct of patient and physician in the actual of units of clinical conclusion making, as done in this study, may be a promising approach for future studies.

Acknowledgments

We give thanks Mary Ann Cook and Marcia Ory for the data, NIMH (MH01935) and NIA (AG15737) for funding the research, and Margarita Alegria, John Z. Ayanian, Howard Beckman, Richard Frankel, Rachel M. Henke, Richard Kravitz, Joseph Newhouse, Richard Street Jr., Suojin Wang, and ii anonymous reviewers for helpful comments on an earlier version of the newspaper.

Disclosures: None.

Disclaimers: None.

APPENDIX A. TRAINING OF CODERS

Training of coders involved over 8 hours of initial didactic instruction, and independent coding of 10 training visits past each coder. Intercoder reliability was calculated later data on the 10 preparation visits were collected. An additional five preparation visits were chosen and coded by each coder in a 2nd round of training to amend reliability.

To measure agreement among coders, we calculated intraclass correlation (ICC) for numerical variables. In light of debates on properties of Cohen's κ and its susceptibility to showing depression values for uncommon behaviors (Cicchetti and Feinstein 1990; Feinstien and Cicchetti 1990; Bakeman et al. 1997; Ickes, Marangoni, and Garcia 1997), we used both Cohen's κ and per centum agreement for categorical variables (Eide, Graugaard, and Finset 2004).

After the 2nd round of training coding, consistency was satisfactory The ICC for visit length was 0.98; total talk time, 0.89; patient talk fourth dimension, 0.84; physician talk time, 0.86; and number of topics, 0.95. The Cohen's κ and percent agreement for patient showing verbal cue for mood disorder was 0.31 and 92 percent; for nonverbal cue for mood disorder was 0.10 and 90 percent, and for showing uncertainty, 0.06/82 and i.00/98 percent on patient and doctor'due south expression of uncertainty, respectively. Intrarater ICC ranged from 0.84 to 1 on number of topics, from 0.98 to 0.99 on all other numerical variables.

Following Braddock et al. (1999), nosotros ensured interrater reliability by randomly selecting 10 percent of the visits to be recoded by a second coder. To ensure intrarater reliability 5 percent of the visits coded by each coder were selected for repeated coding by the same coder. Coding-related questions were resolved through weekly team consultations.

APPENDIX B. DERIVATION OF QUANTITATIVE INTERPRETATIONS OF SURVIVAL ANALYSIS RESULTS

To test elapsing dependence, we use the Weibull proportional gamble part (Cleves et al. 2004), at the topic level

(B1)

where t is fourth dimension in seconds; β′=(β₁, …, β _j , …, β _J ) is a parameter vector for covariates. x′ _i =(ten _i1, …, x _ij , …, x _iJ ) is a data vector; x _i represent topics, i=1, …, K where K is the full number of topics; j=1, …, J is the index for explanatory variables. In equation (B1), h ₀(t) is the baseline hazard rate which can be modeled equally

(B2)

where s and β₀ are parameters to estimate. s is known equally the shape parameter which represents the presence of duration dependence if information technology is dissimilar than i. β₀ is a calibration parameter in Weibull model. The survival probability, i.e., the probability for a visit or topic lasting longer than time t, has the form

(B3)

The exponential of β _j has an interpretation every bit a hazard ratio (HR) which is the alter of hazard rate with i unit change in the value of the jth covariate.

Suppose at time t ₀ the survival probability is S(t ₀|10 ₀), we want to find a time point t ₁ such that the survival probability Due south(t ₁|x₁ ) is the aforementioned as S(t ₀|x ₀), afterwards ane unit increase in the value of the jth covariate. That is

Solving for t ₁, we have

where β′x ₀−β′ten ₁=[(β₁ x ₀₁+β₂ ten ₀₂+…+β _j 10 _0j +…)−(β₁ x ₀₁+β₂ x ₀₂+…+β _j (ten _0j +1)+… ]=−β _j .

Hence, we become

Because exp(β _j )=Hr _j , where HR _j stands for gamble ratio for the jth covariate, nosotros take

(B4)

Interpretation of coefficients in the Weibull model equally HRs is not straightforward. We evaluate the quantitative human relationship between a change in covariates and The modify in length of fourth dimension at a constant survival probability. We tin then calculate the percent modify in talk fourth dimension equally a upshot of an increase in an explanatory variable, which is

(B5)

Equation (B5) enables united states of america to summate, property abiding the survival probability, how changes in key explanatory variables would influence the length of time spent on a topic or a visit.

Note: equation (B1) here corresponds to equation (i) in the text, equations (B2)–(2), and (B5)–(3) in text.

NOTES

^oneNineteen of the visits were multiple visits between a few patient–physician dyads. Sensitivity analyses excluding these visits obtained similar results as the total sample. Results reported hither are from all 392 visits.

ⁱⁱSilences are meaningful social interaction activities that can convey multiple letters. For example, when diagnostic news is bad, silence may be a patient's exhibition of stoicism (Maynard 2003).

³For case, in a visit covering five topics, two of them focused on biomedical issues. Their combined talk time was 450 seconds, out of a total of ane,020 seconds talk time in the visit. The fourth dimension share of the biomedical content area would be 44 percent. If one topic was on mental health issues and it took 120 seconds, the share of mental health content time would be 12 percentage, and then on.

⁴For case, from Tabular array 2, we see that, amongst the major topics, the hazard ratio of changing the topic from biomedical to mental health is 0.37, and the estimate of shape parameter due south is 1.62. We tin can calculate percent alter in patient talk fourth dimension by applying equation, HR^−1/south−one=(0.37)^−1/i.62−i=85 percent. Therefore, patient'due south talk time on mental health topics is 85 percent longer than on biomedical topics provisional on a given survival probability.

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