Publications

BACKGROUND: Hospital medicine has grown as a field. However, no study has examined trends in career choices by internists over the past decade.

OBJECTIVE: To measure changes in practice setting for general internists.

DESIGN: Using Medicare fee-for-service claims (2008 to 2018) and data from the American Board of Internal Medicine, practice setting types were measured annually for general internists initially certifying between 1990 and 2017.

SETTING: General internists (non-subspecializing) treating Medicare fee-for-service beneficiaries.

PATIENTS: Medicare fee-for-service beneficiaries aged 65 years and older with at least 20 evaluation and management (E&M) visits annually.

MEASUREMENTS: Practice setting types were defined as hospitalist (>95% inpatient E&M), outpatient only (100% outpatient E&M), or mixed.

RESULTS: 67 902 general internists, comprising 80% of all general internists initially certified from 1990 to 2017 (n = 84 581), were studied. From 2008 to 2018, both hospitalists and outpatient-only physicians increased as percentages of general internists (25% to 40% and 23% to 38%, respectively). This was accompanied by a 56% decline in the percentage of mixed-practice physicians (52% to 23%) as these physicians largely migrated to outpatient-only practice. By 2018, 71% of newly certified general internists practiced as hospitalists compared with only 8% practicing as outpatient-only physicians. Most (86% of hospitalists in 2013) had the same practice type 5 years later. This retention rate was similar across early career and more senior physicians (86% and 85% for the 1999 and 2012 initial certification cohorts, respectively) and for the outpatient-only practice type (95%) but was only 57% for the mixed practice type.

LIMITATION: Practice setting measurement relied only on Medicare fee-for-service claims.

CONCLUSION: Newly certified general internists are largely choosing hospital medicine as their career choice whereas more senior physicians increasingly see patients only in the outpatient setting.

PRIMARY FUNDING SOURCE: This study did not receive direct funding.

As many as one in 10 patients experience dyspnoea at hospital admission but the relationship between dyspnoea and patient outcomes is unknown. We sought to determine whether dyspnoea on admission predicts outcomes.We conducted a retrospective cohort study in a single, academic medical centre. We analysed 67 362 consecutive hospital admissions with available data on dyspnoea, pain and outcomes. As part of the Initial Patient Assessment by nurses, patients rated "breathing discomfort" using a 0 to 10 scale (10="unbearable"). Patients reported dyspnoea at the time of admission and recalled dyspnoea experienced in the 24 h prior to admission. Outcomes included in-hospital mortality, 2-year mortality, length of stay, need for rapid response system activation, transfer to the intensive care unit, discharge to extended care, and 7- and 30-day all-cause readmission to the same institution.Patients who reported any dyspnoea were at an increased risk of death during that hospital stay; the greater the dyspnoea, the greater the risk of death (dyspnoea 0: 0.8% in-hospital mortality; dyspnoea 1-3: 2.5% in-hospital mortality; dyspnoea ≥4: 3.7% in-hospital mortality; p<0.001). After adjustment for patient comorbidities, demographics and severity of illness, increasing dyspnoea remained associated with inpatient mortality (dyspnoea 1-3: adjusted OR 2.1, 95% CI 1.7-2.6; dyspnoea ≥4: adjusted OR 3.1, 95% CI 2.4-3.9). Pain did not predict increased mortality. Patients reporting dyspnoea also used more hospital resources, were more likely to be readmitted and were at increased risk of death within 2 years (dyspnoea 1-3: adjusted hazard ratio 1.5, 95% CI 1.3-1.6; dyspnoea ≥4: adjusted hazard ratio 1.7, 95% CI 1.5-1.8).We found that dyspnoea of any rating was associated with an increased risk of death. Dyspnoea ratings can be rapidly collected by nursing staff, which may allow for better monitoring or interventions that could reduce mortality and morbidity.

AIM: Pancreatic ductal adenocarcinoma (PDAC) is often diagnosed at a late, incurable stage. We sought to determine whether individuals at high risk of developing PDAC could be identified early using routinely collected data.

METHODS: Electronic health record (EHR) databases from two independent hospitals in Boston, Massachusetts, providing inpatient, outpatient, and emergency care, from 1979 through 2017, were used with case-control matching. PDAC cases were selected using International Classification of Diseases 9/10 codes and validated with tumour registries. A data-driven feature selection approach was used to develop neural networks and L2-regularised logistic regression (LR) models on training data (594 cases, 100,787 controls) and compared with a published model based on hand-selected diagnoses ('baseline'). Model performance was validated on an external database (408 cases, 160,185 controls). Three prediction lead times (180, 270 and 365 days) were considered.

RESULTS: The LR model had the best performance, with an area under the curve (AUC) of 0.71 (confidence interval [CI]: 0.67-0.76) for the training set, and AUC 0.68 (CI: 0.65-0.71) for the validation set, 365 days before diagnosis. Data-driven feature selection improved results over 'baseline' (AUC = 0.55; CI: 0.52-0.58). The LR model flags 2692 (CI 2592-2791) of 156,485 as high risk, 365 days in advance, identifying 25 (CI: 16-36) cancer patients. Risk stratification showed that the high-risk group presented a cancer rate 3 to 5 times the prevalence in our data set.

CONCLUSION: A simple EHR model, based on diagnoses, can identify high-risk individuals for PDAC up to one year in advance. This inexpensive, systematic approach may serve as the first sieve for selection of individuals for PDAC screening programs.

The true risk of a COVID-19 resurgence as states reopen businesses is unknown. In this paper, we used anonymized cell-phone data to quantify the potential risk of COVID-19 transmission in business establishments by building a Business Risk Index that measures transmission risk over time. The index was built using two metrics, visits per square foot and the average duration of visits, to account for both density of visits and length of time visitors linger in the business. We analyzed trends in traffic patterns to 1,272,260 businesses across eight states from January 2020 to June 2020. We found that potentially risky traffic behaviors at businesses decreased by 30% by April. Since the end of April, the risk index has been increasing as states reopen. There are some notable differences in trends across states and industries. Finally, we showed that the time series of the average Business Risk Index is useful for forecasting future COVID-19 cases at the county-level (P < 0.001). We found that an increase in a county's average Business Risk Index is associated with an increase in positive COVID-19 cases in 1 week (IRR: 1.16, 95% CI: (1.1-1.26)). Our risk index provides a way for policymakers and hospital decision-makers to monitor the potential risk of COVID-19 transmission from businesses based on the frequency and density of visits to businesses. This can serve as an important metric as states monitor and evaluate their reopening strategies.

BACKGROUND: During the COVID-19 pandemic, many ambulatory clinics transitioned to telehealth, but it remains unknown how this may have exacerbated inequitable access to care.

OBJECTIVE: Given the potential barriers faced by different populations, we investigated whether telehealth use is consistent and equitable across age, race, and gender.

METHODS: Our retrospective cohort study of outpatient visits was conducted between March 2 and June 10, 2020, compared with the same time period in 2019, at a single academic health center in Boston, Massachusetts. Visits were divided into in-person visits and telehealth visits and then compared by racial designation, gender, and age.

RESULTS: At our academic medical center, using a retrospective cohort analysis of ambulatory care delivered between March 2 and June 10, 2020, we found that over half (57.6%) of all visits were telehealth visits, and both Black and White patients accessed telehealth more than Asian patients.

CONCLUSIONS: Our findings indicate that the rapid implementation of telehealth does not follow prior patterns of health care disparities.