BACKGROUND: One underexplored driver of inappropriate antibiotic prescribing for acute respiratory illnesses (ARI) is patients' prior care experiences. When patients receive antibiotics for an ARI, patients may attribute their clinical improvement to the antibiotics, regardless of their true benefit. These experiences, and experiences of family members, may drive whether patients seek care or request antibiotics when they have subsequent ARIs. METHODS: Using encounter data from a national United States insurer, we identified patients <65 years old with an index ARI urgent care center (UCC) visit. We categorized clinicians within each UCC into quartiles based on their ARI antibiotic prescribing rate. Exploiting the quasi-random assignment of patients to a clinician within an UCC, we examined the association between the clinician's antibiotic prescribing rate to the patients' rates of ARI antibiotic receipt as well as their spouses' rate of antibiotic receipt in the subsequent year. RESULTS: Across 232,256 visits at 736 UCCs, ARI antibiotic prescribing rates were 42.1% and 80.2% in the lowest and highest quartile of clinicians, respectively. Patient characteristics were similar across the four quartiles. In the year after the index ARI visit, patients seen by the highest-prescribing clinicians received more ARI antibiotics (+3.0 fills/100 patients (a 14.6% difference), 95% CI 2.2-3.8, p<0.001,) versus those seen by the lowest-prescribing clinicians. The increase in antibiotics was also observed among the patients' spouses. The increase in patient ARI antibiotic prescriptions was largely driven by an increased number of ARI visits (+5.6 ARI visits/100 patients, 95% CI 3.6-7.7, p<0.001), rather than a higher antibiotic prescribing rate during those subsequent ARI visits. CONCLUSIONS: Receipt of antibiotics for an ARI increases the likelihood that patients and their family members will receive antibiotics for future ARIs.

Importance: Systematically capturing cancer stage is essential for any serious effort by health systems to monitor outcomes and quality of care in oncology. However, oncologists do not routinely record cancer stage in machine-readable structured fields in electronic health records (EHRs). Objective: To evaluate whether a peer comparison email intervention that communicates an oncologist's performance on documenting cancer stage relative to that of peer physicians was associated with increased likelihood that stage was documented in the EHR. Design, Setting, and Participants: This 12-month, randomized quality improvement pilot study aimed to increase oncologist staging documentation in the EHR. The pilot study was performed at Massachusetts General Hospital Cancer Center from October 1, 2018, to September 30, 2019. Participants included 56 oncologists across 3 practice sites who treated patients in the ambulatory setting and focused on diseases that use standardized staging systems. Data were analyzed from July 2, 2019, to March 5, 2020. Interventions: Peer comparison intervention with as many as 3 emails to oncologists during 6 months that displayed the oncologist's staging documentation rate relative to all oncologists in the study sample. Main Outcomes and Measures: The primary outcome was patient-level documentation of cancer stage, defined as the likelihood that a patient's stage of disease was documented in the EHR after the patient's first (eg, index) ambulatory visit during the pilot period. Results: Among the 56 oncologists participating (32 men [57%]), receipt of emails with peer comparison data was associated with increased likelihood of documentation of cancer stage using the structured field in the EHR (23.2% vs 13.0% of patient index visits). In adjusted analyses, this difference represented an increase of 9.0 (95% CI, 4.4-13.5) percentage points (P = .002) in the probability that a patient's cancer stage was documented, a relative increase of 69% compared with oncologists who did not receive peer comparison emails. The association increased with each email that was sent, ranging from a nonsignificant 4.0 (95% CI, -0.8 to 8.8) percentage points (P = .09) after the first email to a statistically significant 11.2 (95% CI, 4.9-17.4) percentage points (P = .003) after the third email . The association was concentrated among an oncologist's new patients (increase of 11.8 [95% CI, 6.2-17.4] percentage points; P = .001) compared with established patients (increase of 1.6 [95% CI, -2.9 to 6.1] percentage points; P = .44) and persisted for 7 months after the email communications stopped. Conclusions and Relevance: In a quality improvement pilot trial, peer comparison emails were associated with a substantial increase in oncologist use of the structured field in the EHR to document stage of disease.

Importance: Medicare recently concluded a national voluntary payment demonstration, Bundled Payments for Care Improvement (BPCI) model 3, in which skilled nursing facilities (SNFs) assumed accountability for patients' Medicare spending for 90 days from initial SNF admission. There is little evidence on outcomes associated with this novel payment model. Objective: To evaluate the association of BPCI model 3 with spending, health care utilization, and patient outcomes for Medicare beneficiaries undergoing lower extremity joint replacement (LEJR). Design, Setting, and Participants: Observational difference-in-difference analysis using Medicare claims from 2013-2017 to evaluate the association of BPCI model 3 with outcomes for 80 648 patients undergoing LEJR. The preintervention period was from January 2013 through September 2013, which was 9 months prior to enrollment of the first BPCI cohort. The postintervention period extended from 3 months post-BPCI enrollment for each SNF through December 2017. BPCI SNFs were matched with control SNFs using propensity score matching on 2013 SNF characteristics. Exposures: Admission to a BPCI model 3-participating SNF. Main Outcomes and Measures: The primary outcome was institutional spending, a combination of postacute care and hospital Medicare-allowed payments. Additional outcomes included other categories of spending, changes in case mix, admission volume, home health use, length of stay, and hospital use within 90 days of SNF admission. Results: There were 448 BPCI SNFs with 18 870 LEJR episodes among 16 837 patients (mean [SD] age, 77.5 [9.4] years; 12 173 [72.3%] women) matched with 1958 control SNFs with 72 005 LEJR episodes among 63 811 patients (mean [SD] age, 77.6 [9.4] years; 46 072 [72.2%] women) in the preintervention and postintervention periods. Seventy-nine percent of matched BPCI SNFs were for-profit facilities, 85% were located in an urban area, and 85% were part of a larger corporate chain. There were no systematic changes in patient case mix or episode volume between BPCI-participating SNFs and controls during the program. Institutional spending decreased from $17 956 to $15 746 in BPCI SNFs and from $17 765 to $16 563 in matched controls, a differential decrease of 5.6% (-$1008 [95% CI, -$1603 to -$414]; P < .001). This decrease was related to a decline in SNF days per beneficiary (from 26.2 to 21.3 days in BPCI SNFs and from 26.3 to 23.4 days in matched controls; differential change, -2.0 days [95% CI, -2.9 to -1.1]). There was no significant change in mortality or 90-day readmissions. Conclusions and Relevance: Among Medicare patients undergoing lower extremity joint replacement from 2013-2017, the BPCI model 3 was significantly associated with a decrease in mean institutional spending on episodes initiated by admission to SNFs. Further research is needed to assess bundled payments in other clinical contexts.

Electronic consult (eConsult) systems allow specialists more flexibility to respond to referrals more efficiently, thereby increasing access in under-resourced healthcare settings like safety net systems. Understanding the usage patterns of eConsult system is an important part of improving specialist efficiency. In this work, we develop and apply classifiers to a dataset of eConsult questions from primary care providers to specialists, classifying the messages for how they were triaged by the specialist office, and the underlying type of clinical question posed by the primary care provider. We show that pre-trained transformer models are strong baselines, with improving performance from domain-specific training and shared representations.

BACKGROUND: The mechanisms driving the recent decline in outpatient antibiotic prescribing are unknown. We estimated the extent to which reductions in the number of antibiotic prescriptions filled per outpatient visit (stewardship) and reductions in the monthly rate of outpatient visits (observed disease) for infectious disease conditions each contributed to the decline in outpatient antibiotic prescribing in Massachusetts between 2011 and 2015. METHODS: Outpatient medical and pharmacy claims from the Massachusetts All-Payer Claims Database were used to estimate rates of antibiotic prescribing and outpatient visits for 20 medical conditions and their contributions to the overall decline in antibiotic prescribing. Trends were compared to those in the National Ambulatory Medical Care Survey (NAMCS). RESULTS: Between 2011 and 2015, the January and July antibiotic prescribing rates per 1,000 individuals in Massachusetts declined by 18.9% and 13.6%, respectively. The monthly rate of outpatient visits per 1,000 individuals in Massachusetts declined (p < 0.05) for respiratory infections and urinary tract infections. Nationally, outpatient visits for antibiotic-meriting medical conditions also declined between 2010 and 2015. Of the estimated 358 antibiotic prescriptions per 1,000 individuals averted over the study period in Massachusetts, 59% (95% CI 54%, 63%) were attributable to reduced observed disease and 41% (95% CI 37%, 46%) to improved stewardship. CONCLUSIONS: The decline in antibiotic prescribing in Massachusetts was driven both by a decline in observed disease and improved antibiotic stewardship, in agreement with national trends. A focus on infectious disease prevention should be considered alongside antibiotic stewardship as a means to reduce antibiotic prescribing.

To enhance compensation for primary care activities that occur outside of face-to-face visits, the Centers for Medicare and Medicaid Services recently introduced new billing codes for transitional care management (TCM) and chronic care management (CCM) services. Overall, rates of adoption of these codes have been low. To understand the patterns of adoption, we compared characteristics of the practices that billed for these services to those of the practices that did not and determined the extent to which a practice other than the beneficiary's usual primary care practice billed for the services. Larger practices and those using other novel billing codes were more likely to adopt TCM or CCM. Over a fifth of all TCM claims and nearly a quarter of all CCM claims were billed by a practice that was not the beneficiary's assigned primary care practice. Our results raise concerns about whether these codes are supporting primary care as originally expected.

OBJECTIVE: To offer pragmatic, evidence-informed advice on nonsteroidal anti-inflammatory drugs (NSAIDs) as first-line therapy after surgery. This companion to the American Academy of Otolaryngology-Head & Neck Surgery (AAO-HNS) clinical practice guideline (CPG), "Opioid Prescribing for Analgesia After Common Otolaryngology Operations," presents data on potency, bleeding risk, and adverse effects for ibuprofen, naproxen, ketorolac, meloxicam, and celecoxib. DATA SOURCES: National Guidelines Clearinghouse, CMA Infobase, National Library of Guidelines, NICE, SIGN, New Zealand Guidelines Group, Australian National Health and Medical, Research Council, TRIP database, PubMed, Guidelines International Network, Cochrane Library, EMBASE, CINAHL, BIOSIS Previews, ISI Web of Science, AHRQ, and HSTAT. REVIEW METHODS: AAO-HNS opioid CPG literature search strategy, supplemented by PubMed/MEDLINE searches on NSAIDs, emphasizing systematic reviews and randomized controlled trials. CONCLUSION: NSAIDs provide highly effective analgesia for postoperative pain, particularly when combined with acetaminophen. Inconsistent use of nonopioid regimens arises from common misconceptions that NSAIDs are less potent analgesics than opioids and have an unacceptable risk of bleeding. To the contrary, multimodal analgesia (combining 500 mg acetaminophen and 200 mg ibuprofen) is significantly more effective analgesia than opioid regimens (15 mg oxycodone with acetaminophen). Furthermore, selective cyclooxygenase-2 inhibition reliably circumvents antiplatelet effects. IMPLICATIONS FOR PRACTICE: The combination of NSAIDs and acetaminophen provides more effective postoperative pain control with greater safety than opioid-based regimens. The AAO-HNS opioid prescribing CPG therefore prioritizes multimodal, nonopioid analgesia as first-line therapy, recommending that opioids be reserved for severe or refractory pain. This state-of-the-art review provides strategies for safely incorporating NSAIDs into acute postoperative pain regimens.

The coronavirus disease 2019 (COVID-19) pandemic continues to devastate US nursing homes. Adequate personal protective equipment (PPE) and staffing levels are critical to protect nursing home residents and staff. Despite the importance of these basic measures, few national data are available concerning the state of nursing homes with respect to these resources. This article presents results from a new national database containing data from 98 percent of US nursing homes. We find that more than one in five nursing homes reports a severe shortage of PPE and any shortage of staff. Rates of both staff and PPE shortages did not meaningfully improve from May to July 2020. Facilities with COVID-19 cases among residents and staff, as well as those serving more Medicaid recipients and those with lower quality scores, were more likely to report shortages. Policies aimed at providing resources to obtain additional direct care staff and PPE for these vulnerable nursing homes, particularly in areas with rising community COVID-19 case rates, are needed to reduce the national COVID-19 death toll.

OBJECTIVE: In response to the COVID-19 pandemic, many psychiatrists have rapidly transitioned to telemedicine. This qualitative study sought to understand how this dramatic change in delivery has affected mental health care, including modes of telemedicine psychiatrists used, barriers encountered, and future plans. The aim was to inform the ongoing COVID-19 response and pass on lessons learned to psychiatrists who are starting to offer telemedicine. METHODS: From March 31 to April 9, 2020, semistructured interviews were conducted with 20 outpatient psychiatrists practicing in five U.S. states with significant early COVID-19 activity. Inductive and deductive approaches were used to develop interview summaries, and a matrix analysis was conducted to identify and refine themes. RESULTS: At the time of the interviews, all 20 psychiatrists had been using telemedicine for 2-4 weeks. Telemedicine encompassed video visits, phone visits, or both. Although many continued to prefer in-person care and planned to return to it after the pandemic, psychiatrists largely perceived the transition positively. However, several noted challenges affecting the quality of provider-patient interactions, such as decreased clinical data for assessment, diminished patient privacy, and increased distractions in the patient's home setting. Several psychiatrists noted that their disadvantaged patients lacked reliable access to a smartphone, computer, or the Internet. Participants identified several strategies that helped them improve telemedicine visit quality. CONCLUSIONS: The COVID-19 pandemic has driven a dramatic shift in how psychiatrists deliver care. Findings highlight that although psychiatrists expressed some concerns about the quality of these encounters, the transition has been largely positive for both patients and physicians.

Background: Improving access to treatment for opioid use disorder is a national priority, but little is known about the barriers encountered by patients seeking buprenorphine-naloxone ("buprenorphine") treatment. Objective: To assess real-world access to buprenorphine treatment for uninsured or Medicaid-covered patients reporting current heroin use. Design: Audit survey ("secret shopper" study). Setting: 6 U.S. jurisdictions with a high burden of opioid-related mortality (Massachusetts, Maryland, New Hampshire, West Virginia, Ohio, and the District of Columbia). Participants: From July to November 2018, callers contacted 546 publicly listed buprenorphine prescribers twice, posing as uninsured or Medicaid-covered patients seeking buprenorphine treatment. Measurements: Rates of new appointments offered, whether buprenorphine prescription was possible at the first visit, and wait times. Results: Among 1092 contacts with 546 clinicians, schedulers were reached for 849 calls (78% response rate). Clinicians offered new appointments to 54% of Medicaid contacts and 62% of uninsured-self-pay contacts, whereas 27% of Medicaid and 41% of uninsured-self-pay contacts were offered an appointment with the possibility of buprenorphine prescription at the first visit. The median wait time to the first appointment was 6 days (interquartile range [IQR], 2 to 10 days) for Medicaid contacts and 5 days (IQR, 1 to 9 days) for uninsured-self-pay contacts. These wait times were similar regardless of clinician type or payer status. The median wait time from first contact to possible buprenorphine induction was 8 days (IQR, 4 to 15 days) for Medicaid and 7 days (IQR, 3 to 14 days) for uninsured-self-pay contacts. Limitation: The survey sample included only publicly listed buprenorphine prescribers. Conclusion: Many buprenorphine prescribers did not offer new appointments or rapid buprenorphine access to callers reporting active heroin use, particularly those with Medicaid coverage. Nevertheless, wait times were not long, implying that opportunities may exist to increase access by using the existing prescriber workforce. Primary Funding Source: National Institute on Drug Abuse.

Importance: Temporary disruptions in health care access are common, but their associations with chronic disease control remain unknown. Objective: To evaluate whether long-term changes in chronic disease control were associated with a temporary 6-month decrease in access to health care services. Design, Setting, and Participants: This cohort study examined the long-term changes in chronic disease control associated with the 6-month closure of the Manhattan facility of the Veterans Affairs (VA) New York Harbor Healthcare System after superstorm Sandy, which caused a significant disruption in health care access for veterans in the region. Electronic health records from the VA Healthcare System between October 29, 2010, and October 29, 2014, were used to identify a total of 81 544 veterans who were and were not exposed to the 6-month closure of the VA Manhattan Medical Center after superstorm Sandy. Of those, 19 207 veterans were included in the exposed cohort and 62 337 were included in the nonexposed control cohort, which included veterans who were equally exposed to the storm but who retained regular access to health care from 3 VA medical centers (Brooklyn and the Bronx in New York and New Haven in Connecticut) during and after the storm. A difference-in-differences analysis was used to assess within-patient changes in chronic disease control over time between a cohort that was exposed to decreased health care access compared with a similar cohort that was not exposed to decreased access. All analyses adjusted for individual demographic and socioeconomic characteristics, between-zip code differences, and common time trends. Data analyses were conducted between February 1, 2016, and September 30, 2019. Exposure: The 6-month closure of the VA Manhattan Medical Center after superstorm Sandy on October 29, 2012. Main Outcomes and Measures: The outcomes measured were uncontrolled blood pressure (defined as mean blood pressure per patient per quarter >140/90 mm Hg), uncontrolled diabetes (defined as mean hemoglobin A1c per patient per quarter >8%), uncontrolled cholesterol (defined as mean low density lipoprotein per patient per quarter >140 mg/dL), and patient weight. Results: Among the 81 544 veterans included in the study, the mean (SD) age was 62.1 (17.6) years, and 93.6% were men, 62.7% were white, and 31.8% were black. At the 3-month midpoint of the 6-month facility closure of the VA Manhattan Medical Center, an absolute decrease of 24.8% (95% CI, -26.5% to -23.0%; P < .001) was observed in the percentage of veterans who had any VA primary care visit per quarter compared with a baseline of 47.8% before the closure (relative decrease, 51.9%; 95% CI, -55.4% to -48.1%; P < .001). One year after the facility reopened, no differential change was observed in the percentage of patients with a primary care visit between the exposed vs nonexposed cohorts (absolute decrease, -0.1%; 95% CI, -1.5% to 1.4%; P = .94); however, patients in the exposed cohort were 25.9% more likely to have uncontrolled blood pressure than patients in the nonexposed cohort (unadjusted increase, 5.5% in the exposed cohort vs 1.3% in the nonexposed cohort; adjusted absolute increase, 5.0%; 95% CI, 3.5%-6.0%; P < .001). Two years after superstorm Sandy, patients in the exposed cohort were 10.9% more likely to experience uncontrolled blood pressure than those in the nonexposed cohort (unadjusted increase, 5.2% in the exposed cohort vs 3.5% in the nonexposed cohort; adjusted absolute increase, 2.1%; 95% CI, 0.5%-3.6%; P < .001). Compared with the nonexposed cohort, the exposed cohort also experienced a decrease in filled medication prescriptions per patient per quarter of 6.9% during the facility closure (absolute decrease, -0.7 prescriptions filled per patient per quarter; 95% CI, -0.9 to -0.5; P < .001) and of 2.2% a year after the facility reopened (absolute decrease, -0.2 prescriptions filled per patient per quarter; 95% CI, -0.4 to -0.1; P = .04). No differential changes were observed in uncontrolled diabetes, uncontrolled cholesterol, or patient weight. Conclusions and Relevance: In this study, a temporary period of decreased access to health care services was associated with increased rates of uncontrolled hypertension, but not with increased rates of uncontrolled diabetes or hyperlipidemia, more than 1 year after the Manhattan VA facility reopened. Temporary gaps in access to health care may be associated with long-term increases in uncontrolled blood pressure among patients with hypertension.