Primary care physicians who address multiple problems during acute care visits achieve better clinical scores, comparable patient experience, and lower annual cost.
ABSTRACTObjectives: To assess quality, cost, physician productivity, and patient experience for 2 primary care physician (PCP) practice styles: the focused, who typically address only the patient’s acute problem, versus the max-packers, who typically address additional conditions also.
Study Design: Retrospective observational study using administrative data, electronic health record (EHR) data, and patient surveys. Data represent 285 PCPs (779 PCP-years) in a large, multispecialty group practice during 2011, 2012, and 2013.
Methods: PCPs were ranked each year by their number of additional conditions addressed during acute care visits. The top one-third (max-packers) addressed 25.4% more “other problems” than expected, while focused PCPs (bottom one-third) addressed 20.3% fewer than expected. Outcomes were resource use, clinical quality metrics, patient-reported experience, physician time using the EHR, and physician productivity. All measures were risk-adjusted to account for patient mix. T tests were used to compare measures.
Results: Relative to a focused pattern of care, max-packing was associated with 3.4% lower overall resource use, consistently better scores for the available clinical quality metrics, and comparable patient experience (except for worse wait time ratings). Patients of focused PCPs used 7.3% more specialist services, in terms of costs, than patients of max-packers ($1218 vs $1136; P <.001). Max-packers spent 40 minutes more per clinical day using the EHR. PCPs with less appointment availability and who used a mix of appointment slots were more likely to be max-packers.
Conclusions: Max-packing behavior yields desirable outcomes at lower overall cost but involves more conventionally uncompensated PCP time. Alternatives to compensation just for face-to-face visits and using more flexible scheduling may be needed to support max-packing.
Am J Manag Care. 2020;26(4):e127-e134. https://doi.org/10.37765/ajmc.2020.42840
Relative to primary care physicians who typically focus only on the patient’s main problem during an acute care visit, those also addressing chronic issues (the max-packers) achieve:
Primary care physicians (PCPs) are increasingly working in large groups, often feeling pressured to see more patients while dealing with demanding electronic health record (EHR) systems.1,2 Little is known about PCP responses to increasing patient loads. Some emphasize “throughput,” scheduling as many patients as possible by focusing on the specific reason for the visit. Others do the opposite, using any acute care visit to address additional problems, thereby reducing follow-up visits. The latter approach was termed max-packing by the Institute for Healthcare Improvement.3 In a study unrelated to ours, a very busy PCP spontaneously explained max-packing: “So my [appointment] access is already poor and I already do…max-packing: You come in for a visit, I take care of everything I need to take care of in order to prevent you from coming back too soon. If you come in for a cold and I know you have an appointment next month to follow up on your high blood pressure and diabetes,…I’m also going to look after your high blood pressure and your diabetes at this visit so you don’t have to come back in a month.”4
The value of comprehensive PCP care coordination across visits is demonstrated,5-10 but there has been little exploration of what occurs during visits (ie, is focused care better or worse than max-packing?). We contrast PCPs within the same system who, during acute care visits, focus on the patient’s main problem versus those who also address other conditions—the “max-packers.”3,11-13 We examine resource use, clinical quality, patient experience, physician time, and productivity metrics, along with PCP and practice characteristics associated with max-packing.
This is an exploratory observational study of PCPs treating adult (≥18 years) patients in a large multispecialty group practice (“the Clinic”) with 17 sites spread across 4 urban counties. Nearly all Clinic patients have insurance (14%, health maintenance organization [HMO; Medicare or other]; 61%, commercial preferred provider organization/fee-for-service [FFS]; 10%, Medicare FFS; 2%, Medicaid; 13%, other). PCP compensation reflects their own work relative value units (wRVUs). The Clinic also provides specialty care, ambulatory surgery, and laboratory, imaging, and inpatient physician services. All patient care is recorded in a unified EHR.
Our starting sample is 310 PCPs practicing during 2011 to 2013 (834 PCP-years). To report on “typical PCPs,” we excluded those whose clinical time was less than 20% clinical full-time equivalent (cFTE); who had fewer than 50 active adult Clinic patients, were practicing geriatrics only, were trained in osteopathic medicine, or have a concierge practice; or whose primary service location was inpatient or nursing facilities or was missing appointment scheduling information. This yielded 285 PCPs (779 PCP-years).
New Clinic patients choose a PCP and are considered that PCP’s patient unless they request a change. Patients are included on their PCP’s active patient list (APL) if they have at least 1 visit with any PCP in the previous or current calendar year or at least 1 nonvisit encounter (eg, pharmacy refill, online message) in the current year.
Depending on the metric, somewhat different relevant patient populations were used. PCPs typically see their own patients along with those of other PCPs. When categorizing PCP practice styles, we used only encounters with their own patients—about 75% of all visits. When assessing quality and resource utilization, we used all the PCP’s own patients, even those seeing other providers. When assessing how PCPs use their time, we included all encounters.
Max-packing is an observed behavior, not an inherent physician characteristic. Thus, we categorized each PCP-year as max-packer, middle, or focused, based on visits with their own patients that year (eAppendix A [eAppendices available at ajmc.com]). We used an episode grouper (Symmetry Suite 9.2.1; Optum; Eden Prairie, Minnesota) to organize all professional, ancillary, and pharmaceutical services, assigning each (ie, a line on a bill) to 1 of 514 clinically homogenous episodes of care.14 For example, if a patient saw her PCP for acute back pain, then within 30 days saw a specialist for the same problem and the specialist ordered back imaging, those downstream services were considered part of the acute back pain episode and extended the 30-day window. Windows for acute problems are typically 30 to 180 days; chronic problems essentially continue indefinitely. If, during the initial visit, the PCP also adjusted her antihypertensive medications and ordered a glycated hemoglobin (A1C) test, the medications would be assigned to a previous hypertension (chronic) episode and the laboratory test to her chronic diabetes care. The PCP evaluation and management (E&M) cost is assigned to the primary reason for the visit (back pain). Two “phantom” records (ie, without physician costs) are generated because during the face-to-face visit, the physician did something for those conditions (hypertension and diabetes). To minimize potential documentation bias, when calculating phantoms, we ignored conditions for which patients received only “counseling.”
We expected that a PCP who primarily sees young, healthy patients would address fewer “other problems” during acute visits than a PCP with mostly elderly patients. We used regression to calculate the expected number of phantom records, given each patient’s age, sex, and mix of acute and chronic conditions (eAppendix A). Based on the ratio of observed-to-expected phantoms per acute episode with ones’ own patients, we ranked PCP-years; the top one-third in each year are labeled “max-packers” and the bottom one-third are labeled “focused.”
Resource Use (costs)
Billing data identify all Clinic services provided. We approximated resource use with standardized costs priced at 150% of Medicare rates, regardless of payer. Prescription drug costs were approximated using medication orders, drug-specific order fill rates, and drug-specific average insurer-based payments. Prescription costs were winsorized to address outlier values. Professional services of Clinic physicians in hospitals were included, but facility and other hospital-based charges were unavailable.
Resource use measures (costs) were mean (1) E&M costs for all acute visits by the PCP’s own patients, (2) total cost per acute episode, and (3) annual total cost per patient on the PCP’s panel. For the second measure, only episodes initially managed by the patient’s own PCP were considered, but resources ordered or provided by other clinicians were included. Following the Clinic’s practice, all resources used by the PCP’s patients were included in the panel measure, even if none were provided by that PCP. At the panel level, we also examined types of services (eg, physician services, laboratory services, imaging, prescriptions) by who provided or ordered them (ie, own PCP, other PCPs or urgent care, and specialists). All resource use measures were risk-adjusted for patient age, sex, and mix of acute and chronic conditions (eAppendix B).15
The Clinic routinely assessed clinical quality with the Integrated Healthcare Association’s 2011-2013 targeted measures16: the average of 6 diabetes management metrics (A1C testing, low-density lipoprotein cholesterol [LDL-C] screening, medical attention for nephropathy, A1C control [<8.0%], LDL-C control [<100 mg/dL], and blood pressure control [<140/90 mm Hg]) and 3 screening metrics (colorectal cancer screening for adults aged 50-75 years, breast cancer screening for women aged 52-69 years, and evidence-based cervical cancer screening for women aged 24-65 years). Patients with a new diagnosis of diabetes are typically closer to being in control and some PCPs have more such patients, so metrics were risk-adjusted for when the patient became eligible (eAppendix C).17
The Clinic routinely questioned its patients about their experiences using the Press Ganey Outpatient Medical Practice Survey.18 We focused on 5 of the survey’s care provider (CP) assessments: likelihood of your recommending your CP, amount of time the CP spent with you, friendliness/courtesy of the CP, explanations the CP gave you about your problem or condition, and concern the CP showed for your questions or worries. We also examined patients’ assessments of wait time at the Clinic. For each, a score indicates the percentage responding that it was very good (5 on 1-5 scale). We have found that some patient responses are associated with race/ethnicity in ways apparently unrelated to objective measures,19 so we adjusted for the race/ethnicity of the PCP’s own patients who responded.
Physician Time Allocation
In the typical Clinic encounter, the patient checks in and is then escorted from the waiting room by the medical assistant, who takes vital signs and records them in the EHR. The physician enters the exam room, opens the patient’s chart at some point, and logs out before exiting. Access logs facilitate calculating the time each PCP spends with the EHR open in an exam room, in their Clinic office, and remotely.20 An objective lower-bound estimate of time spent with patients is how long the patient’s chart was open with the physician in the exam room. The EHR also records scheduled appointment times.
PCPs are more likely to address multiple issues if there is email or phone communication with the patient prior to or after the visit,21 so we counted such communications within a week of each face-to-face visit. Because physicians may address problems without face-to-face visits, we also counted communications outside of those 1-week windows.
Patient Wait Time
Usual PCP timeliness is the mean difference between appointment time and PCP chart open time for patients checking in before their scheduled appointment time.
At the Clinic, productivity is based on physician wRVUs. This can be assessed as wRVU per cFTE, appointment hours, or total hours. Alternatively, productivity can be measured by the size of the APL per cFTE, either unadjusted (simple head count), adjusted for patient age and sex, or adjusted for patient age, sex, and clinical conditions.15 For family physicians, the panel-based metric includes children and adults.
To compare observed measures of resource use, clinical quality, and patient experience, all relative to the values expected, t tests were used. The expected values reflect patient mix based on age, sex, and clinical conditions (for cost); new versus established patients (for quality metrics); and race/ethnicity (for patient experience). For patient-focused metrics (cost, quality, experience), we compared observed and expected values for all the relevant patients associated with PCPs in each tertile. For PCP-focused metrics (eg, time spent, panel size, factors associated with behaving as a max-packer), the PCP-year is the unit of observation.
Given the exploratory nature of this work, we did not formally adjust P values (most are either highly significant or clearly insignificant). We present results for the middle tertile of PCPs to explore any trend across groups. SAS Enterprise Guide version 7.1 (SAS Institute; Cary, North Carolina) was used for data cleaning and management; statistical analyses were performed using Stata 14.2 (StataCorp; College Station, Texas).
This study was approved by the institutional review boards of the Palo Alto Medical Foundation and Sutter Health.
The number of “other problems” addressed during acute care visits varied markedly. With the exception of the tails, the distribution appeared quite normal, so we split the PCP-years into thirds. The top one-third, max-packers, addressed 25.4% more other problems than expected per acute episode; the bottom one-third, focused PCPs, 20.3% fewer (P <.001). This behavior pattern is quite consistent over time, with 65% to 70% of the PCPs in the same tertile from one year to the next (eAppendix A).
Figure 1 presents summary findings for risk-adjusted resource use, clinical quality, and patient experience. Max-packers had the highest relative (risk-adjusted) per-visit E&M and total per-episode costs but lowest annual total per-patient costs. The opposite pattern holds for focused PCPs (P <.001). For the 4 clinical quality metrics, max-packers scored significantly above expectations for diabetes management, colorectal screening, and breast cancer screening (each P <.001); focused PCP scores were all below expectations (P <.001 for all metrics).
None of the 5 care experience measures differed significantly from expected. For patient assessment of the wait time, however, focused PCPs received markedly better than expected scores (mean [standard error] = 1.06 [0.004]), whereas max-packers scored markedly worse (0.94 [0.004]). Observed EHR-based wait times were approximately 4 minutes longer for max-packers than for focused PCPs (data not shown).
Figure 2 subsets the annual resource use measures by type of service and who ordered/provided the service. Overall, the patient’s own PCP provided or ordered 32.3% of all costs, other PCPs and urgent care accounted for about 9.4%, and specialists accounted for about 57.1%. Given the marked differences in case mix across the 3 groupings of PCPs, we first compare observed versus expected use based on a regression model with patient risk factors (eAppendix B). This allows us to calculate the percentage by which patients assigned each group have expenditures above (or below) the overall average for each type of service. The data in Figure 2 and that follow here are those normalized costs (details are in eAppendix D). With the exception of nearly identical observed and expected own PCP expenditures for patients of the focused PCPs, all other cost measures (observed-to-expected due to case mix) for focused and max-packing PCPs are significantly different (P <.001). Patients of focused and max-packing PCPs had nearly identical amounts of services rendered and ordered by their own PCPs ($664 vs $666, respectively). Patients of focused PCPs had fewer services rendered or ordered from other PCPs/urgent care ($184 vs $199). In contrast, patients of focused PCPs had services rendered or ordered by specialists that were 7.3% higher ($1218 vs $1136). Ancillary services were comparable, but E&M, laboratory, imaging, and prescription costs were 7% to 15% higher (all P <.001) (data not shown).
PCP Time Allocation and Productivity
Max-packing and focused PCPs allocated their time differently (Table 1). Per clinical day, max-packers had 6.8% fewer scheduled appointment hours and 14.5% fewer appointments, yielding 8.2% longer appointments (all P <.001). Max-packers’ average time between EHR open and close was 1.47 minutes (15.6%) longer (P = .005).
Modern clinical care involves more than face-to-face time: Before visits, PCPs may review laboratory results and medical history, while afterward, referrals are made and tests and drugs ordered.20,22,23 All 3 groups of PCPs had comparable EHR time in exam rooms, but max-packers logged substantially more EHR time in their offices or remotely. Overall, max-packers spent 40 minutes more EHR time per clinical day compared with focused PCPs (5.6 vs 4.9 hours; P <.001).
Max-packers averaged 0.273 non—face-to-face patient contacts (19% more than the focused PCPs) in the week before each visit and 0.269 contacts (26% more) in the week after. There was no difference in contacts per patient-year outside those peri-visit windows.
Max-packers generated slightly fewer wRVUs per cFTE (P = .39) but 3.7% more per scheduled appointment hour (P = .03). If, however, clinical time is calculated as appointment time plus EHR time in the office plus EHR time spent remotely, max-packers generated 5.5% fewer wRVUs per clinical hour (P <.01). An alternative productivity metric is panel size. Per cFTE, max-packers (versus focused PCPs) had smaller panels, even if adjusted for patients’ age and sex (1947 vs 2162; P <.001), yet the max-packers’ risk-adjusted panel sizes when accounting for patients’ clinical conditions were larger (2066 vs 2035; P <.01).
PCP and Practice Characteristics
Some characteristics of PCPs and their practices are individually associated with max-packer behavior (Table 2). In a regression model, however, none of those PCP factors—sex, specialty, years since licensure, or cFTE—continued to distinguish max-packing from focused PCPs. Relative to those using only 15/30-minute appointment blocks, however, PCPs using a mix of 15/30-minute and 20/40-minute blocks were more likely to be max-packers. PCPs with open schedules (ie, with a higher percentage of visits booked within a week) were less likely to be max-packers.
Our study is the first to look inside the office visit to assess how what PCPs typically do during and around encounters affects a comprehensive set of outcomes. What occurs during any particular visit obviously depends on the clinical situation, but we focus on the overall practice of these PCPs. All are in the same largely FFS system facing pressures similar to those seen across the United States. Some seem to respond by focusing on the problem at hand and moving on to the next patient; others take extra time, thereby reducing subsequent visits. After risk adjustment, the patients of max-packers have 6.2% fewer PCP visits annually than those of focused PCPs (data not shown).
FFS payment allows higher charges for visits addressing more clinical problems, so max-packing PCPs have greater E&M costs per encounter. The lower annual resource cost per patient in the max-packers’ panels reflects an important difference: Their patients use substantially fewer resources associated with specialists. Max-packers have more communication with their patients shortly before and after visits, thus requiring more traditionally uncompensated EHR time.
Unsurprisingly, patients with diabetes whose PCPs are max-packers have significantly above-average disease management scores, but max-packers’ patients also have better screening scores. Longer waits are generally negatively associated with perceptions of care, overall patient satisfaction, and the likelihood of recommending the CP,24 but although patients of max-packers express substantially less satisfaction with their wait time (and indeed wait 4 minutes longer), they nonetheless give their PCPs overall scores comparable to those of the focused PCPs.
Max-packing, however, may be less beneficial for PCPs. Max-packers generate more wRVUs per appointment hour but devote more EHR time outside the exam room, effectively working 18 minutes more per day. If compensation depends on wRVUs from face-to-face visits, this means 5.5% lower pay per total clinical hour.
In a fully capitated model, savings from reduced specialty, inpatient, and postacute care can be used to reward more effective primary care.25 Although our data do not include the latter 2 categories of cost, the lower use of specialist-related services alone could more than make up for the 5.5% lower hourly compensation of max-packers. Medicare is exploring alternatives to traditional FFS reimbursement,26 but appropriately risk-adjusting payments to large groups is challenging.27 Compensation of individual PCPs based on panel management has many conceptual advantages,6,28-31 but the panels of max-packers include fewer patients than those of focused PCPs, and simple age/sex adjustment does not change this much. Max-packers’ patients are sicker than average, given their age, necessitating more complex adjustments.15 Incorporating such adjustments in a new internal compensation method may be organizationally challenging. Adding a small payment for substantive communications with patients occurring in the week before a visit might be more feasible.
In regressions using these data, max-packing behavior was not associated with PCP sex, specialty, years of experience, or cFTE. However, having a full appointment schedule may lead PCPs to want to reduce the need for return visits—consistent with the Institute for Healthcare Improvement notion of max-packing.3 Flexibly using a mixture of appointment blocks may allow those PCPs to most effectively use their time, but this may conflict with standardized workflows. Physician burnout has been at least partly blamed on the increasing time demanded by EHR work. A recent study’s findings showed that time spent doing process notes was not associated with burnout measures, but having an above-average number of EHR system—generated reminders in the inbox per week was associated with burnout. The authors report anecdotal evidence that physicians “considered…progress notes to be work they perform with more autonomy. They derived professional satisfaction from writing good notes.”32 As the max-packers actually see fewer patients per clinic hour, they probably have fewer in-basket messages per day.
Simply addressing economic incentives and organizational expectations may be insufficient. Precisely how a PCP asks questions can elicit more problems from patients, and relatively simple interventions may improve question asking.33 Additional work is needed to assess how better communication skills, usage of time, and appropriate compensation may facilitate max-packing.
Our study reflects practice in a single, quite large, multispecialty group. Every organization has its own culture, and large group practices are uncommon in some parts of the nation. Unlike the HMOs upon which some studies are based, however, the Clinic has a broad range of payer sources and, similar to the United States as a whole, physician compensation is based largely on wRVUs. PCPs differ in the clinical issues of their patients and in the race/ethnicity composition of their panels, so we included extensive risk adjustments in these internal comparisons. The routinely reported clinical quality metrics, however, capture just a small part of what PCPs do, so it is impossible to assess a style of practice as of truly higher quality. Cost results might differ if we had inpatient data.
We assessed quality, cost, physician productivity, and patient experience for 2 PCP practice styles using data from a large multispecialty group practice. Our results suggest that relative to a focused pattern of care, max-packing was associated with lower overall resource use (costs), consistently better scores of clinical quality metrics, and comparable patient experience (except for worse wait time ratings). Compared with focused PCPs, max-packers saw fewer patients with longer visits and spent more time messaging and using the EHR per clinical day. Max-packers are likely to be undercompensated by traditional FFS models due to extra EHR time and disadvantaged in simple capitation models due to managing sicker panels.Author Affiliations: Palo Alto Medical Foundation Research Institute (HSL, SYL, LJE, SC), Palo Alto, CA.
Source of Funding: This research was supported by the National Institute of Diabetes and Digestive and Kidney Diseases (R21DK094387; principal investigator [PI]: Dr Luft), the Robert Wood Johnson Foundation (grant number 70045; PI: Dr Luft), and the Palo Alto Medical Foundation.
Author Disclosures: Drs Luft and Liang are employed by the organization providing the data, Palo Alto Medical Foundation Research Institute (PAMFRI), but no prior review or approval was required. Dr Chung was employed by PAMFRI and Dr Eaton was a consulting investigator at PAMFRI during the time this work was performed.
Authorship Information: Concept and design (HSL, SYL, LJE, SC); acquisition of data (HSL, SYL, SC); analysis and interpretation of data (HSL, SYL, LJE, SC); drafting of the manuscript (HSL, SYL, LJE, SC); critical revision of the manuscript for important intellectual content (HSL, SYL, LJE, SC); statistical analysis (HSL, SYL, LJE); obtaining funding (HSL); and supervision (HSL).
Address Correspondence to: Harold S. Luft, PhD, Palo Alto Medical Foundation Research Institute, 795 El Camino Real, Ames Bldg, Palo Alto, CA 94301. Email: firstname.lastname@example.org.REFERENCES
1. Kane CK. Updated data on physician practice arrangements: physician ownership drops below 50 percent. American Medical Association website. ama-assn.org/sites/ama-assn.org/files/corp/media-browser/public/health-policy/PRP-2016-physician-benchmark-survey.pdf. Published 2017. Accessed January 18, 2019.
2. Goitein L, James B. Standardized best practices and individual craft-based medicine: a conversation about quality. JAMA Intern Med. 2016;176(6):835-838. doi: 10.1001/jamainternmed.2016.1641.
3. Changes: decrease demand for appointments. Institute for Healthcare Improvement website. ihi.org/resources/Pages/Changes/DecreaseDemandforAppointments.aspx. Accessed May 1, 2018.
4. Uy V, May SG, Tietbohl C, Frosch DL. Barriers and facilitators to routine distribution of patient decision support interventions: a preliminary study in community-based primary care settings. Health Expect. 2014;17(3):353-364. doi: 10.1111/j.1369-7625.2011.00760.x.
5. Bodenheimer T, Pham HH. Primary care: current problems and proposed solutions. Health Aff (Millwood). 2010;29(5):799-805. doi: 10.1377/hlthaff.2010.0026.
6. Lochner J, Trowbridge E, Kamnetz S, Pandhi N. Family physician clinical compensation in an academic environment: moving away from the relative value unit. Fam Med. 2016;48(6):459-466.
7. O’Malley AS, Cunningham PJ. Patient experiences with coordination of care: the benefit of continuity and primary care physician as referral source. J Gen Intern Med. 2009;24(2):170-177. doi: 10.1007/s11606-008-0885-5.
8. Pham HH, O’Malley AS, Bach PB, Saiontz-Martinez C, Schrag D. Primary care physicians’ links to other physicians through Medicare patients: the scope of care coordination. Ann Intern Med. 2009;150(4):236-242. doi: 10.7326/0003-4819-150-4-200902170-00004.
9. Rittenhouse DR, Shortell SM, Fisher ES. Primary care and accountable care—two essential elements of delivery-system reform. N Engl J Med. 2009;361(24):2301-2303. doi: 10.1056/NEJMp0909327.
10. Rosenthal TC. The medical home: growing evidence to support a new approach to primary care. J Am Board Fam Med. 2008;21(5):427-440. doi: 10.3122/jabfm.2008.05.070287.
11. Payne E. Re: interventions to improving osteoporosis screening: an Iowa Research Network (IRENE) study. J Am Board Fam Med. 2010;23(1):132; author reply 132-133. doi: 10.3122/jabfm.2010.01.090224.
12. Abou Malham S, Touati N, Maillet L, Gaboury I, Loignon C, Breton M. What are the factors influencing implementation of advanced access in family medicine units? a cross-case comparison of four early adopters in Quebec. Int J Family Med. 2017;2017:1595406. doi: 10.1155/2017/1595406.
13. Noffsinger EB. The ABCs of Group Visits: An Implementation Manual for Your Practice. New York, NY: Springer-Verlag New York; 2013.
14. OptumInsight. Symmetry Episode Treatment Groups: measuring healthcare with meaningful episodes of care. Optum website. optum.com/content/dam/optum/resources/whitePapers/symmetry_episode_treatment_groups_wp_06_2012.pdf. Published 2012. Accessed January 29, 2017.
15. Chung S, Eaton LJ, Luft HS. Standardizing primary care physician panels: is age and sex good enough? Am J Manag Care. 2012;18(7):e262-e268.
16. AMP commercial HMO. Integrated Healthcare Association website. iha.org/our-work/accountability/value-based-p4p. Accessed January 30, 2017.
17. Liang SY, Luft H. Pay-for-performance quality measures: why do new physicians score lower? J Patient Cent Res Rev. 2017;4(3):154.
18. Presson AP, Zhang C, Abtahi AM, Kean J, Hung M, Tyser AR. Psychometric properties of the Press Ganey Outpatient Medical Practice Survey. Health Qual Life Outcomes. 2017;15(1):32. doi: 10.1186/s12955-017-0610-3.
19. Chung S, Johns N, Zhao B, et al. Clocks moving at different speeds: cultural variation in the satisfaction with wait time for outpatient care. Med Care. 2016;54(3):269-276. doi: 10.1097/MLR.0000000000000473.
20. Tai-Seale M, Olson CW, Li J, et al. Electronic health record logs indicate that physicians split time evenly between seeing patients and desktop medicine. Health Aff (Millwood). 2017;36(4):655-662. doi: 10.1377/hlthaff.2016.0811.
21. Chung S, Luft H. Characterizing primary care visits: explaining variation in the evaluation and management visit level. Clin Med Res. 2013;11(3):160. doi: 10.3121/cmr.2013.1176.ps2-19.
22. Baron RJ. What’s keeping us so busy in primary care? a snapshot from one practice. N Engl J Med. 2010;362(17):1632-1636. doi: 10.1056/NEJMon0910793.
23. Farber J, Siu A, Bloom P. How much time do physicians spend providing care outside of office visits? Ann Intern Med. 2007;147(10):693-698. doi: 10.7326/0003-4819-147-10-200711200-00005.
24. Bleustein C, Rothschild DB, Valen A, Valatis E, Schweitzer L, Jones R. Wait times, patient satisfaction scores, and the perception of care. Am J Manag Care. 2014;20(5):393-400.
25. Mandal AK, Tagomori GK, Felix RV, Howell SC. Value-based contracting innovated Medicare Advantage healthcare delivery and improved survival. Am J Manag Care. 2017;23(2):e41-e49.
26. CPC+ payment and attribution methodologies for program year 2019 (version 2). CMS website. innovation.cms.gov/Files/x/cpcplus-methodology-py19.pdf. Published February 21, 2019. Accessed July 18, 2019.
27. Schulte F, Weber L. Medicare Advantage plans overbill taxpayers by billions annually, records show. NPR website. npr.org/sections/health-shots/2019/07/16/740964958/records-show-medicare-advantage-plans-overbill-taxpayers-by-billions-annually. Published July 16, 2019. Accessed July 16, 2019.
28. Davis K. Paying for care episodes and care coordination. N Engl J Med. 2007;356(11):1166-1168. doi: 10.1056/NEJMe078007.
29. Goroll AH, Berenson RA, Schoenbaum SC, Gardner LB. Fundamental reform of payment for adult primary care: comprehensive payment for comprehensive care. J Gen Intern Med. 2007;22(3):410-415. doi: 10.1007/s11606-006-0083-2.
30. Institute of Medicine. Rewarding Provider Performance: Aligning Incentives in Medicare. Washington, DC: National Academies Press; 2007.
31. Trowbridge E, Bartels CM, Koslov S, Kamnetz S, Pandhi N. Development and impact of a novel academic primary care compensation model. J Gen Intern Med. 2015;30(12):1865-1870. doi: 10.1007/s11606-015-3410-7.
32. Tai-Seale M, Dillon EC, Yang Y, et al. Physicians’ well-being linked to in-basket messages generated by algorithms in electronic health records. Health Aff (Millwood). 2019;38(7):1073-1078. doi: 10.1377/hlthaff.2018.05509.
33. Heritage J, Robinson JD, Elliott MN, Beckett M, Wilkes M. Reducing patients’ unmet concerns in primary care: the difference one word can make. J Gen Intern Med. 2007;22(10):1429-1433. doi: 10.1007/s11606-007-0279-0.