Results 736 537 admissions managed by 18 854 hospitalist physicians (median age 41) were included. Patients’ characteristics were similar across physician ages. After adjustment for characteristics of patients and physicians and hospital fixed effects (effectively comparing physicians within the same hospital), patients’ adjusted 30 day mortality rates were 10.8% for physicians aged <40 (95% confidence interval 10.7% to 10.9%), 11.1% for physicians aged 40-49 (11.0% to 11.3%), 11.3% for physicians aged 50-59 (11.1% to 11.5%), and 12.1% for physicians aged ≥60 (11.6% to 12.5%). Among physicians with a high volume of patients, however, there was no association between physician age and patient mortality. Readmissions did not vary with physician age, while costs of care were slightly higher among older physicians. Similar patterns were observed among general internists and in several sensitivity analyses.

Participants 20% random sample of Medicare fee-for-service beneficiaries aged ≥65 admitted to hospital with a medical condition in 2011-14 and treated by hospitalist physicians to whom they were assigned based on scheduled work shifts. To assess the generalizability of findings, analyses also included patients treated by general internists including both hospitalists and non-hospitalists.

Using nationally representative data on Medicare beneficiaries admitted to hospital with a medical condition during 2011-14, we sought answers to three questions. First, what is the association between age of the treating physician and patient mortality after admission? Second, does this association vary with the volume of patients a physician treats? Finally, given national efforts to improve the efficiency of healthcare, is physician age associated with readmissions and costs of care?

A systematic review of the relation between physician experience and quality of care found that older physicians might perform worse—older physicians have decreased clinical knowledge, adhere less often to standards of appropriate treatment, and perform worse on process measures of quality with respect to diagnosis, screening, and preventive care. 4 Data on patient outcomes, which arguably are most important, have been scarce. 4 Existing studies have also been limited in size or disease scope and have not been nationally representative. 5 6 7 As a result, whether physician age is associated with patient outcomes remains largely unknown.

The relation between physician age and performance remains largely unknown, particularly with respect to patient outcomes. Clinical skills and knowledge accumulated by more experienced physicians can lead to improved quality of care. Physicians’ skills, however, can also become outdated as scientific knowledge, technology, and clinical guidelines change. Incorporating these changes into clinical practice is time consuming and can at times be overwhelming. 1 2 3 Interest in how quality of care evolves over a physician’s career has revived in recent years, with debates over how best to structure programs for continuing medical education, including recent controversy in the US regarding maintenance of certification programs.

Methods

Data We linked multiple data sources: the 20% Medicare Inpatient Carrier and Medicare Beneficiary Summary Files (2011-14); physician data collected by Doximity (an online professional network for physicians); and the American Hospital Association (AHA) annual survey of hospital characteristics (2012). Doximity has assembled data on all US physicians (both those who are registered members of the service as well as those who are not) from multiple sources and data partnerships, including the national plan and provider enumeration system national provider identifier registry, state medical boards, specialty societies such as the American Board of Medical Specialties, and collaborating hospitals and medical schools. The database includes information on physician age, sex, year of completion and name of medical school, residency, and board certification.89101112 Previous studies have validated data for a random sample of physicians in the Doximity database by using manual audits.89 We were able to match about 95% of physicians in the Medicare database to the Doximity database.

Patients We identified beneficiaries of Medicare fee-for-service aged ≥65 who were admitted to hospital with a medical condition (as defined by the presence of a medical diagnosis related group on admission) from 1 January 2011 to 31 December 2014. We restricted our sample to patients treated in acute care hospitals and excluded elective admissions and those in which a patient left against medical advice. To allow sufficient follow-up, we excluded patients admitted in December 2014 from 30 day mortality analyses and patients discharged in December 2014 from readmission analyses.

Medicare hospital spending and method of assigning physicians to patients In the US, Medicare spending on patients in hospital mainly consists of two components: parts A and B. Part A spending is a fixed payment to a hospital per patient that is determined by the final diagnosis or diagnoses of the patient (categorized into diagnosis related groups) and broadly reflects hospital costs other than professional services. Within each hospital the part A payment does not vary for patients within the same diagnosis related group (with a few exceptions). Part B uses fee-for-service payment, and spending varies with the intensity of services delivered, including visits, procedures, and interpretation of tests and images. Based on previous studies,101112 we defined the responsible physician for a given admission as the physician who billed the largest share of part B costs during that admission.13 In a sensitivity analysis, we used alternative assignment methods to assess the robustness of our findings to this attribution rule. We restricted our analyses to admissions for which the highest spending physicians were hospitalists (described below) or general internists. For patients transferred to other acute care hospitals (1.2% of admissions), we attributed the multi-hospital episode of care and associated outcomes to the assigned physician of the initial admissions.1415 On average, 51%, 22%, and 11% of total part B spending was accounted for by the first, second, and third highest spending physicians, respectively. Our primary analysis focused on patients treated by hospitalists to examine the possibility that older physicians might treat patients with greater or lesser unmeasured severity of illness. Hospitalists are physicians whose clinical focus is caring for patients admitted to hospital.1617 They are typically trained in internal or family medicine. Some complete subspecialty training as well (such as infectious disease or nephrology) but decide to practice general inpatient medicine. The hospitalist specialty began in the 1990s in the US and is the most rapidly growing medical specialty there. Before the introduction of hospitalists, a patient admitted for a general medical condition was cared for by that patient’s primary care physician (equivalent to general practitioner in the UK), who, on any given day, would typically visit his/her inpatients when time permitted in the outpatient schedule. In 2016, it was estimated that more than 50 000 hospitalists were practicing in the US, and about 75% of US hospitals now have hospitalists.18 Hospitalists typically work in scheduled shifts or blocks (such as one week on and one week off) and do not treat patients in the outpatient setting. Therefore, within the same hospital, patients treated by hospitalists are plausibly quasi-randomized to a particular hospitalist based only on the time of the patient’s admission and the hospitalist’s work schedule.101119 We assessed the validity of this assumption by testing the balance of a broad range of patient characteristics across categories of age of hospitalist. We defined hospitalists as general internists who filed at least 90% of their total evaluation and management billings in an inpatient setting, a claims based approach that a previous study validated by calling physicians to confirm that they were indeed hospitalists (sensitivity of 84.2%, specificity of 96.5%, and a positive predictive value of 88.9%).20

Physician age Physician age was defined as the age on the date of admission of patients. Data on physician age were available for 93.5% of physicians. Physician age was modeled both as a continuous linear variable and as a categorical variable (in categories of <40, 40-49, 50-59, and ≥60) to allow for a potential non-linear relation with patient outcomes. We also used linear spline models.

Patient outcomes The primary outcome was the 30 day mortality rate in patients (death within 30 days of admission); secondary outcomes were 30 day readmission rates (readmission within 30 days of discharge) and costs of care. Information on dates of death, including deaths out of hospital, was available in the Medicare Beneficiary summary files. Over 99% of dates of death in these files have been verified by death certificate.21 For mortality analyses, we excluded patients whose death dates were not validated. We defined costs of care as total part B spending per admission.

Adjustment variables We adjusted for patient characteristics, physician characteristics, and hospital fixed effects. Patient characteristics included age in five year increments, sex, race or ethnic group (non-Hispanic white, non-Hispanic black, Hispanic, other), primary diagnosis (diagnosis related group), 27 comorbidities (Elixhauser comorbidity index22), median household income of zip code (in 10ths), an indicator for dual Medicare-Medicaid coverage, day of the week of the admission date (to account for the possibility that severity of illness of patients could be higher on specific days of the week), and year indicators. Physician characteristics (other than age) included sex, indicator variables for medical school from which a physician graduated (all foreign schools were grouped into a single category), and whether they graduated from allopathic (MD) or osteopathic (DO) medical schools (allopathic and osteopathic schools both teach the same basic curriculums necessary to become a qualified physician, but osteopathic schools emphasize prevention and other techniques as well). We included indicator variables for each hospital, which allowed each hospital to have its own intercept in the regression analyses, a statistical method known as hospital fixed effects. Hospital fixed effects account for both measured and unmeasured characteristics of hospitals that do not vary over time, including unmeasured differences in patient populations, thereby effectively comparing patient outcomes among hospitalists of varying age within the same hospital.232425

Statistical analysis First, we examined the association between physician age and 30 day mortality using a multivariable logistic regression model treating age as both a continuous variable and a categorical variable to allow for a non-linear relation, adjusting for patient and physician characteristics and hospital fixed effects. We also used linear age splines. To evaluate whether splines improve goodness of fit compared with modeling a linear relation between physician age and patient mortality, we performed a Wald test adjusted for clustering (to approximate a likelihood ratio test because standard likelihood based tests are unavailable with clustered data). To account for potential correlations of patient outcomes within the same physicians, we clustered standard errors at the physician level.26 To overcome complete or quasi-complete separation problems (perfect or nearly perfect prediction of the outcome by the model), we combined diagnosis related group codes with no outcome event (30 day mortality or readmission) into clinically similar categories.27 We calculated adjusted 30 day mortality rates using margins of responses (also known as predictive margins); for each admission we calculated predicted probabilities of outcome with physician age group fixed at each level and then averaged over the distribution of covariates in our national sample.28 Second, because physicians with high volumes of patients might better maintain clinical knowledge and skills,29303132 we examined whether the association between physician age and patient mortality was modified by volume. We classified physicians into thirds of patient volume: low (estimated number of total admissions <90 per year), medium (91-200 admissions), and high (>201 admissions). Within each group, we examined the association between physician age and patient mortality, adjusting for patient and physician characteristics and hospital fixed effects. We used a Wald test to formally test the interaction between physician age and patient volume. Finally, we evaluated the association between physician age and 30 day readmissions and costs of care. We used multivariable logistic regression models for readmission analyses. Because cost data were right skewed, we used a generalized linear model (GLM) with a log link and gamma distribution.33

Secondary analyses We conducted several secondary analyses. First, to test the generalizability of our findings, we repeated our analyses among general internists overall, including both hospitalists and non-hospitalists. Second, to evaluate whether our findings were sensitive to how we attributed patients to physicians, we tested two alternative attribution rules: attributing patients to physicians with the largest number of evaluation and management claims and attributing patients to physicians who billed the first claim for a given admission (“admitting physician”). Third, because the association between physician age and mortality could be confounded by unobserved care preferences of patients, such as do-not-resuscitate directives, we excluded patients with cancer and those discharged to a hospice. Fourth, to assess the relation between physician age and patient outcomes in a relatively young population whose probability of death is lower, we restricted our analysis to patients aged 65-75. Fifth, an increasing number of young subspecialists in specialties like nephrology and infectious disease work as hospitalists but were excluded from our primary analyses. To investigate this, we reanalyzed the data including hospitalists with medical subspecialties and adjusted for their specialty. Sixth, patients who are admitted multiple times might not be randomly assigned to a given hospitalist but instead to the hospitalist who treated the patient previously. To deal with this, we reanalyzed the data after restricting our sample to the first admission. Seventh, we also evaluated in hospital, 60 day, and 90 day mortality rates to assess if any survival gains were short lived. Eighth, we used generalized estimating equations (GEE) with an independent covariance matrix to account for the hierarchical structure of the data because of the grouping of patients within hospitals, adjusting for patient and physician characteristics and hospital fixed effects.34 Ninth, to focus on more homogenous patient populations, we separately analyzed the four most common conditions treated by hospitalists in our data (sepsis, pneumonia, congestive heart failure, and chronic obstructive pulmonary disease) (see table A in the appendix for diagnosis codes). Tenth, we used years since completion of residency, instead of physician age, as a measure of physician experience. We did not use this variable for our primary analyses because data on year of residency completion were missing for 35.5% of physicians, and we were concerned that missingness might not be at random. Eleventh, we conducted a formal sensitivity analysis to assess the extent to which an unmeasured confounder might explain our results.35 Twelfth, we conducted cost analysis using different model specifications: a GLM model with a log link and a negative binomial distribution, a GLM model with a log link and a Poisson distribution, and an ordinary least squares model after winsorizing the top 1% of observations with largest residuals (replacing outlier costs by the most extreme retained values). Finally, we conducted analyses among subgroups including Medicare beneficiaries aged ≥65 who were admitted to hospital with an emergency medical condition (as opposed to our baseline analysis of “non-elective” conditions, which included both emergency and urgent admissions), Medicare beneficiaries aged ≥65 who were admitted with an elective medical condition, and Medicare beneficiaries aged 20-64. The latter group qualified for Medicare through disability and has generally worse health status than the general US population aged below 65, but nonetheless the generalizability of our findings to populations of younger patients is of interest. Data preparation was conducted with SAS, version 9.4 (SAS Institute), and analyses were performed with Stata, version 14 (Stata-Corp, College Station, TX).