Publications

IMPORTANCE: The dramatic rise in use of telehealth accelerated by COVID-19 created new telehealth-specific challenges as patients and clinicians adapted to technical aspects of video visits.

OBJECTIVE: To evaluate a telehealth patient navigator pilot program to assist patients in overcoming barriers to video visit access.

DESIGN, SETTING, AND PARTICIPANTS: This quality improvement study investigated visit attendance outcomes among those who received navigator outreach (intervention group) compared with those who did not (comparator group) at 2 US academic primary care clinics during a 12-week study period from April to July 2021. Eligible participants had a scheduled video visit without previous successful telehealth visits.

INTERVENTIONS: The navigator contacted patients with next-day scheduled video appointments by phone to offer technical assistance and answer questions on accessing the appointment.

MAIN OUTCOMES AND MEASURES: The primary outcome was appointment attendance following the intervention. Return on investment (ROI) accounting for increased clinic adherence and costs of implementation was examined as a secondary outcome.

RESULTS: A total 4066 patients had video appointments scheduled (2553 [62.8%] women; median [IQR] age: intervention, 55 years [38-66 years] vs comparator, 52 years [36-66 years]; P = .02). Patients who received the navigator intervention had significantly increased odds of attending their appointments (odds ratio, 2.0; 95% CI, 1.6-2.6) when compared with the comparator group, with an absolute increase of 9% in appointment attendance for the navigator group (949 of 1035 patients [91.6%] vs 2511 of 3031 patients [82.8%]). The program's ROI was $11 387 over the 12-week period.

CONCLUSIONS AND RELEVANCE: In this quality improvement study, we found that a telehealth navigator program was associated with significant improvement in video visit adherence with a net financial gain. Our findings have relevance for efforts to reduce barriers to telehealth-based health care and increase equity.

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.

This cohort study analyzes the trends in filled naloxone prescriptions during the COVID-19 pandemic in the United States and compare these to opioid prescriptions and overall prescriptions.

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.

BACKGROUND/OBJECTIVES: Transitional care management (TCM) visits delivered following hospitalization have been associated with reductions in mortality, readmissions, and total costs; however, uptake remains low. We sought to describe trends in TCM visit delivery during the COVID-19 pandemic.

DESIGN: Cross-sectional study of ambulatory electronic health records from December 30, 2019 and January 3, 2021.

SETTING: United States.

PARTICIPANTS: Forty four thousand six hundred and eighty-one patients receiving transitional care management services.

MEASUREMENTS: Weekly rates of in-person and telehealth TCM visits before COVID-19 was declared a national emergency (December 30, 2019 to March 15, 2020), during the initial pandemic period (March 16, 2020 to April 12, 2020) and later period (April 12, 2020 to January 3, 2021). Characteristics of patients receiving in-person and telehealth TCM visits were compared.

RESULTS: A total of 44,681 TCM visits occurred during the study period with the majority of patients receiving TCM visits age 65 years and older (68.0%) and female (55.0%) Prior to the COVID-19 pandemic, nearly all TCM visits were conducted in-person. In the initial pandemic, there was an immediate decline in overall TCM visits and a rise in telehealth TCM visits, accounting for 15.4% of TCM visits during this period. In the later pandemic, the average weekly number of TCM visits was 841 and 14.0% were telehealth. During the initial and later pandemic periods, 73.3% and 33.6% of COVID-19-related TCM visits were conducted by telehealth, respectively. Across periods, patterns of telehealth use for TCM visits were similar for younger and older adults.

CONCLUSION: The study findings highlight a novel and sustained shift to providing TCM services via telehealth during the COVID-19 pandemic, which may reduce barriers to accessing a high-value service for older adults during a vulnerable transition period. Further investigations comparing outcomes of in-person and telehealth TCM visits are needed to inform innovation in ambulatory post-discharge care.

IMPORTANCE: Alternative methods for hospital occupancy forecasting, essential information in hospital crisis planning, are necessary in a novel pandemic when traditional data sources such as disease testing are limited.

OBJECTIVE: To determine whether mandatory daily employee symptom attestation data can be used as syndromic surveillance to estimate COVID-19 hospitalizations in the communities where employees live.

DESIGN, SETTING, AND PARTICIPANTS: This cohort study was conducted from April 2, 2020, to November 4, 2020, at a large academic hospital network of 10 hospitals accounting for a total of 2384 beds and 136 000 discharges in New England. The participants included 6841 employees who worked on-site at hospital 1 and lived in the 10 hospitals' service areas.

EXPOSURE: Daily employee self-reported symptoms were collected using an automated text messaging system from a single hospital.

MAIN OUTCOMES AND MEASURES: Mean absolute error (MAE) and weighted mean absolute percentage error (MAPE) of 7-day forecasts of daily COVID-19 hospital census at each hospital.

RESULTS: Among 6841 employees living within the 10 hospitals' service areas, 5120 (74.8%) were female individuals and 3884 (56.8%) were White individuals; the mean (SD) age was 40.8 (13.6) years, and the mean (SD) time of service was 8.8 (10.4) years. The study model had a MAE of 6.9 patients with COVID-19 and a weighted MAPE of 1.5% for hospitalizations for the entire hospital network. The individual hospitals had an MAE that ranged from 0.9 to 4.5 patients (weighted MAPE ranged from 2.1% to 16.1%). For context, the mean network all-cause occupancy was 1286 during this period, so an error of 6.9 is only 0.5% of the network mean occupancy. Operationally, this level of error was negligible to the incident command center. At hospital 1, a doubling of the number of employees reporting symptoms (which corresponded to 4 additional employees reporting symptoms at the mean for hospital 1) was associated with a 5% increase in COVID-19 hospitalizations at hospital 1 in 7 days (regression coefficient, 0.05; 95% CI, 0.02-0.07; P < .001).

CONCLUSIONS AND RELEVANCE: This cohort study found that a real-time employee health attestation tool used at a single hospital could be used to estimate subsequent hospitalizations in 7 days at hospitals throughout a larger hospital network in New England.