Trends in breast and prostate cancer screening and diagnostic procedures during the COVID-19 pandemic in central Massachusetts.

PURPOSE: We calculated rates of breast and prostate cancer screening and diagnostic procedures performed during the COVID-19 pandemic through December 2021 compared to the same months in 2019 in a large healthcare provider group in central Massachusetts. METHODS: We included active patients of the provider group between January 2019 and December 2021 aged 30-85 years. Monthly rates of screening mammography and digital breast tomosynthesis, breast MRI, total prostate specific antigen (PSA), and breast or prostate biopsy per 1,000 people were compared by year overall, by age, and race/ethnicity. Completed procedures were identified by relevant codes in electronic health record data. RESULTS: Rates of screening mammography, tomosynthesis, and PSA testing reached the lowest levels in April-May 2020. Breast cancer screening rates decreased 43% in March and 99% in April and May 2020, compared to 2019. Breast cancer screening rates increased gradually beginning in June 2020 through 2021, although more slowly in Black and Hispanic women and in women aged 75-85. PSA testing rates decreased 34% in March, 78% in April, and 53% in May 2020, but rebounded to pre-pandemic levels by June 2020; trends were similar across groups defined by age and race/ethnicity. CONCLUSION: The observed decline in two common screening procedures during the COVID-19 pandemic reflects the impact of the pandemic on cancer early detection and signals potential downstream effects on the prognosis of delayed cancer diagnoses. The slower rate of return for breast cancer screening procedures in certain subgroups should be investigated to ensure all women return for routine screenings.

Recruitment Challenges for Low-Risk Health System Intervention Trials in Older Adults: A Case Study.

OBJECTIVE: To assess factors associated with trial participation in the context of a low-risk intervention intended to reduce adverse drug events in recently hospitalized older adults. DESIGN: Mixed methods: analysis of data collected during enrollment efforts and focus groups. SETTING: A large, multispecialty group practice. PARTICIPANTS: Individuals 50 years and older, recently discharged from the hospital and prescribed at least one high-risk medication, were eligible for the trial. Enrollees, decliners, and their caregivers were eligible to participate in focus groups. MEASUREMENTS: Reasons for declining to participate during the initial invitation as well as reasons for not providing consent were recorded. Focus groups were conducted with eligible individuals to explore reasons for enrolling or declining. We conducted multivariable logistic regression to compare characteristics (including sex, age, healthcare proxy, number and type of medications, visiting nurse services, reason for admission, and length of hospital stay) of those who enrolled with those who did not enroll. RESULTS: Of 3,606 individuals determined eligible, 3,147 (87%) declined, 98 (3%) verbally consented to participate but did not complete written consent, and 361 (10%) provided written consent and were considered enrolled. Individuals 80 year and older (odds ratio (OR) = 0.44; 95% confidence interval (CI) = 0.30-0.65) and those with visiting nurse services (OR = 0.64; 95% CI = 0.48-0.85) were least likely to enroll. Among those who provided a reason for declining (2,473), the most common was the belief they did not need additional medication assistance (18%). Another 332 (11%) declined because they were receiving visiting nurse services. CONCLUSION: Recruiting older adults recently discharged from the hospital to participate in trials of low-risk, system-level interventions is challenging and may underenroll the oldest individuals and those potentially at the highest risk for adverse events, limiting generalizability of study findings. Alternative study designs may be more effective than individually randomized trials in assessing low-risk, system-level interventions.

Improving Rates of Influenza Vaccination Through Electronic Health Record Portal Messages, Interactive Voice Recognition Calls and Patient-Enabled Electronic Health Record Updates: Protocol for a Randomized Controlled Trial.

BACKGROUND: Clinical decision support (CDS), including computerized reminders for providers and patients, can improve health outcomes. CDS promoting influenza vaccination, delivered directly to patients via an electronic health record (EHR) patient portal and interactive voice recognition (IVR) calls, offers an innovative approach to improving patient care. OBJECTIVE: To test the effectiveness of an EHR patient portal and IVR outreach to improve rates of influenza vaccination in a large multispecialty group practice in central Massachusetts. METHODS: We describe a nonblinded, randomized controlled trial of EHR patient portal messages and IVR calls designed to promote influenza vaccination. In our preparatory phase, we conducted qualitative interviews with patients, providers, and staff to inform development of EHR portal messages with embedded questionnaires and IVR call scripts. We also provided practice-wide education on influenza vaccines to all physicians and staff members, including information on existing vaccine-specific EHR CDS. Outreach will target adult patients who remain unvaccinated for more than 2 months after the start of the influenza season. Using computer-generated randomization and a factorial design, we will assign 20,000 patients who are active users of electronic patient portals to one of the 4 study arms: (1) receipt of a portal message promoting influenza vaccines and offering online appointment scheduling; (2) receipt of an IVR call with similar content but without appointment facilitation; (3) both (1) and (2); or (4) neither (1) nor (2) (usual care). We will randomize patients without electronic portals (10,000 patients) to (1) receipt of IVR call or (2) usual care. Both portal messages and IVR calls promote influenza vaccine completion. Our primary outcome is percentage of eligible patients with influenza vaccines administered at our group practice during the 2014-15 influenza season. Both outreach methods also solicit patient self-report on influenza vaccinations completed outside the clinic or on barriers to influenza vaccination. Self-reported data from both outreach modes will be uploaded into the EHR to increase accuracy of existing provider-directed EHR CDS (vaccine alerts). RESULTS: With our proposed sample size and using a factorial design, power calculations using baseline vaccination rate estimates indicated that 4286 participants per arm would give 80% power to detect a 3% improvement in influenza vaccination rates between groups (α=.05; 2-sided). Intention-to-treat unadjusted chi-square analyses will be performed to assess the impact of portal messages, either alone or in combination with the IVR call, on influenza vaccination rates. The project was funded in January 2014. Patient enrollment for the project described here completed in December 2014. Data analysis is currently under way and first results are expected to be submitted for publication in 2016. CONCLUSIONS: If successful, this study's intervention may be adapted by other large health care organizations to increase vaccination rates among their eligible patients. CLINICALTRIAL: ClinicalTrials.gov NCT02266277; https://clinicaltrials.gov/ct2/show/NCT02266277 (Archived by WebCite at http://www.webcitation.org/6fbLviHLH).

Quality measurement of medication monitoring in the "meaningful use" era.

OBJECTIVES: While the 2011 implementation of "meaningful use" legislation for certified electronic health records (EHRs) promises to change quality reporting by overcoming data capture issues affecting quality measurement, the magnitude of this effect is unclear. We compared the measured quality of laboratory monitoring of Healthcare Effectiveness Data and Information Set (HEDIS) medications based on specifications that (1) include and exclude patients hospitalized in the measurement year and (2) use physician test orders and patient test completion. STUDY DESIGN: Cross-sectional study. METHODS: Among patients 18 years and older in a large multispecialty group practice utilizing a fully implemented EHR between January 1, 2008, and July 31, 2008, we measured the prevalence of ordering and completion of laboratory tests monitoring HEDIS medications (cardiovascular drugs [angiotensin-converting enzyme inhibitors or angiotensin receptor blockers, digoxin, and diuretics] and anticonvulsants [carbamazepine, phenobarbital, phenytoin, and valproic acid]). RESULTS: Measures excluding hospitalized patients were not statistically significantly different from measures including hospitalized patients, except for digoxin, but this difference was not clinically significant. The prevalence of appropriate monitoring based on test orders typically captured in the EHR was statistically significantly higher than the prevalence based on claims-based test completions for cardiovascular drugs. CONCLUSIONS: HEDIS quality metrics based on data typically collected from claims undermeasured quality of medication monitoring compared to EHR data. The HEDIS optional specification excluding hospitalized patients from the monitoring measure does not have a significant impact on reported quality. Integration of EHR data into quality measurement may significantly change some organizations' reported quality of care.

Development and pilot testing of guidelines to monitor high-risk medications in the ambulatory setting.

OBJECTIVES: To develop guidelines to monitor high-risk medications and to assess the prevalence of laboratory testing for these medications among a multispecialty group practice. STUDY DESIGN: Safety intervention trial. METHODS: We developed guidelines for the laboratory monitoring of high-risk medications as part of a patient safety intervention trial. An advisory committee of national experts and local leaders used a 2-round Internet-based Delphi process to select guideline medications based on the importance of monitoring for efficacy, safety, and drug-drug interactions. Test frequency recommendations were developed by academic pharmacists based on a literature review and local interdisciplinary consensus. To estimate the potential effect of the planned intervention, we determined the prevalence of high-risk drug dispensings and laboratory testing for guideline medications between January 1, 2008, and July 31, 2008. RESULTS: Consensus on medications to include in the guidelines was achieved in 2 rounds. Final guidelines included 35 drugs or drug classes and 61 laboratory tests. The prevalence of monitoring ranged from less than 50.0% to greater than 90.0%, with infrequently prescribed drugs having a lower prevalence of recommended testing (P <.001 for new dispensings and P <.01 for chronic dispensings, nonparametric test for trend). When more than 1 test was recommended for a selected medication, monitoring within a medication sometimes differed by greater than 50.0%. CONCLUSIONS: Even among drugs for which there is general consensus that laboratory monitoring is important, the prevalence of monitoring is highly variable. Furthermore, infrequently prescribed medications are at higher risk for poor monitoring.

Risk factors for adverse drug events among older adults in the ambulatory setting.

OBJECTIVES: To gather information on patient-level factors associated with risk of adverse drug events (ADEs) that may allow focus of prevention efforts on patients at high risk. DESIGN: Nested case-control study. SETTING: Large multispecialty group practice in New England. PARTICIPANTS: All Medicare enrollees cared for by a multispecialty group practice during 1 year (N=30,397 person-years from July 1, 1999, through June 30, 2000). For each patient with an ADE, a control was randomly selected. MEASUREMENTS: Data were abstracted from medical records on age, sex, comorbidities, and medication use at the time of the event. RESULTS: ADEs were identified in 1,299 older adults. Independent risk factors included being female and aged 80 and older. There were dose-response associations with the Charlson Comorbidity Index and number of scheduled medications. Patients taking anticoagulants, antidepressants, antibiotics, cardiovascular drugs, diuretics, hormones, and corticosteroids were at increased risk. In the analysis of preventable ADEs, the dose-response relationship with comorbidity and number of medications remained. Patients taking nonopioid analgesics (predominantly nonsteroidal antiinflammatory drugs and acetaminophen), anticoagulants, diuretics, and anti-seizure medications were at increased risk. CONCLUSION: Prevention efforts to reduce ADEs should be targeted toward older adults with multiple medical conditions or taking multiple medications, nonopioid analgesics, anticoagulants, diuretics, and antiseizure medications.