Association between SARS-CoV-2 infection and select symptoms and conditions 31 to 150 days after testing among children and adults.

BACKGROUND: An increasing number of studies have described new and persistent symptoms and conditions as potential post-acute sequelae of SARS-CoV-2 infection (PASC). However, it remains unclear whether certain symptoms or conditions occur more frequently among persons with SARS-CoV-2 infection compared with those never infected with SARS-CoV-2. We compared the occurrence of specific COVID-associated symptoms and conditions as potential PASC 31- to 150-day following a SARS-CoV-2 test among adults and children with positive and negative test results. METHODS: We conducted a retrospective cohort study using electronic health record (EHR) data from 43 PCORnet sites participating in a national COVID-19 surveillance program. This study included 3,091,580 adults (316,249 SARS-CoV-2 positive; 2,775,331 negative) and 675,643 children (62,131 positive; 613,512 negative) who had a SARS-CoV-2 laboratory test during March 1, 2020-May 31, 2021 documented in their EHR. We used logistic regression to calculate the odds of having a symptom and Cox models to calculate the risk of having a newly diagnosed condition associated with a SARS-CoV-2 positive test. RESULTS: After adjustment for baseline covariates, hospitalized adults and children with a positive test had increased odds of being diagnosed with ≥ 1 symptom (adults: adjusted odds ratio[aOR], 1.17[95% CI, 1.11-1.23]; children: aOR, 1.18[95% CI, 1.08-1.28]) or shortness of breath (adults: aOR, 1.50[95% CI, 1.38-1.63]; children: aOR, 1.40[95% CI, 1.15-1.70]) 31-150 days following a SARS-CoV-2 test compared with hospitalized individuals with a negative test. Hospitalized adults with a positive test also had increased odds of being diagnosed with ≥ 3 symptoms or fatigue compared with those testing negative. The risks of being newly diagnosed with type 1 or type 2 diabetes (adjusted hazard ratio[aHR], 1.25[95% CI, 1.17-1.33]), hematologic disorders (aHR, 1.19[95% CI, 1.11-1.28]), or respiratory disease (aHR, 1.44[95% CI, 1.30-1.60]) were higher among hospitalized adults with a positive test compared with those with a negative test. Non-hospitalized adults with a positive test also had higher odds or increased risk of being diagnosed with certain symptoms or conditions. CONCLUSIONS: Patients with SARS-CoV-2 infection, especially those who were hospitalized, were at higher risk of being diagnosed with certain symptoms and conditions after acute infection.

Patient Experiences with and Preferences for Telemedicine Relative to In-Person Care During the COVID-19 Pandemic.

Introduction: Although telemedicine emerged during the COVID-19 pandemic as a critical mode of health care delivery, there may be differences in the perceived ease of patient-clinician communication and quality of care for telemedicine versus in-person visits, as well as variation in perceptions across patient subgroups. We examined patients' experiences with and preferences for telemedicine relative to in-person care, based on their most recent visit. Methods: We conducted a survey of 2,668 adults in a large academic health care system in November 2021. The survey captured patients' reasons for their most recent visit, perceptions on patient-clinician communication and quality of care, and attitudes toward telemedicine versus in-person care. Results: Among respondents, 552 (21%) had a telemedicine visit. Patients with telemedicine and in-person visits had similar agreement on ease of patient-clinician communication and perceived quality of the visit on average. However, for individuals 65 years of age or older, men, and those not needing urgent care, telemedicine was associated with worse perceptions of patient-clinician communication (65 years of age or older: adjusted odds ratio [aOR], 0.51; 95% confidence interval [CI], 0.31-0.85; men: aOR, 0.50; 95% CI, 0.31-0.81; urgent care: aOR 0.67; 95% CI, 0.49-0.91) and lower perceived quality (65 years of age or older, aOR 0.51; 95% CI, 0.30-0.86; men: 0.51; 95% CI, 0.32-0.83; urgent care: aOR 0.68; 95% CI, 0.49-0.93). Conclusion: Patient-perceived quality of care and patient-clinician communication were similar for telemedicine and in-person visits overall. However, among men, older adults, and those not seeking urgent care, patients using telemedicine had lower perceptions of patient-clinician communication and quality.

Telemedicine Impact on the Patient-Provider Relationship in Primary Care During the COVID-19 Pandemic.

BACKGROUND: The COVID-19 pandemic has necessitated a rapid uptake of telemedicine in primary care requiring both patients and providers to learn how to navigate care remotely. This change can impact the patient-provider relationship that often defines care, especially in primary care. OBJECTIVE: This study aims to provide insight into the experiences of patients and providers with telemedicine during the pandemic, and the impact it had on their relationship. RESEARCH DESIGN: A qualitative study using thematic analysis of semistructured interviews. SUBJECTS: Primary care providers (n=21) and adult patients (n=65) with chronic disease across primary care practices in 3 National Patient-centered Clinical Research Network sites in New York City, North Carolina, and Florida. MEASURES: Experiences with telemedicine during the COVID-19 pandemic in primary care. Codes related to the patient-provider relationship were analyzed for this study. RESULTS: A recurrent theme was the challenge telemedicine posed on rapport building and alliance. Patients felt that telemedicine affected provider's attentiveness in varying ways, whereas providers appreciated that telemedicine provided unique insight into patients' lives and living situations. Finally, both patients and providers described communication challenges. CONCLUSIONS: Telemedicine has altered structure and process aspects of primary health care such as the physical spaces of encounters, creating a new setting to which both patients and providers must adjust. It is important to recognize the opportunities and limits that this new technology has to help providers maintain the type of one-on-one attention that patients expect and that contributes to relationship building.

Avenues for Strengthening PCORnet's Capacity to Advance Patient-Centered Economic Outcomes in Patient-Centered Outcomes Research (PCOR).

PCORnet, the National Patient-Centered Clinical Research Network, provides the ability to conduct prospective and observational pragmatic research by leveraging standardized, curated electronic health records data together with patient and stakeholder engagement. PCORnet is funded by the Patient-Centered Outcomes Research Institute (PCORI) and is composed of 8 Clinical Research Networks that incorporate at total of 79 health system "sites." As the network developed, linkage to commercial health plans, federal insurance claims, disease registries, and other data resources demonstrated the value in extending the networks infrastructure to provide a more complete representation of patient's health and lived experiences. Initially, PCORnet studies avoided direct economic comparative effectiveness as a topic. However, PCORI's authorizing law was amended in 2019 to allow studies to incorporate patient-centered economic outcomes in primary research aims. With PCORI's expanded scope and PCORnet's phase 3 beginning in January 2022, there are opportunities to strengthen the network's ability to support economic patient-centered outcomes research. This commentary will discuss approaches that have been incorporated to date by the network and point to opportunities for the network to incorporate economic variables for analysis, informed by patient and stakeholder perspectives. Topics addressed include: (1) data linkage infrastructure; (2) commercial health plan partnerships; (3) Medicare and Medicaid linkage; (4) health system billing-based benchmarking; (5) area-level measures; (6) individual-level measures; (7) pharmacy benefits and retail pharmacy data; and (8) the importance of transparency and engagement while addressing the biases inherent in linking real-world data sources.

Racial/Ethnic Disparities in Post-acute Sequelae of SARS-CoV-2 Infection in New York: an EHR-Based Cohort Study from the RECOVER Program.

BACKGROUND: Compared to white individuals, Black and Hispanic individuals have higher rates of COVID-19 hospitalization and death. Less is known about racial/ethnic differences in post-acute sequelae of SARS-CoV-2 infection (PASC). OBJECTIVE: Examine racial/ethnic differences in potential PASC symptoms and conditions among hospitalized and non-hospitalized COVID-19 patients. DESIGN: Retrospective cohort study using data from electronic health records. PARTICIPANTS: 62,339 patients with COVID-19 and 247,881 patients without COVID-19 in New York City between March 2020 and October 2021. MAIN MEASURES: New symptoms and conditions 31-180 days after COVID-19 diagnosis. KEY RESULTS: The final study population included 29,331 white patients (47.1%), 12,638 Black patients (20.3%), and 20,370 Hispanic patients (32.7%) diagnosed with COVID-19. After adjusting for confounders, significant racial/ethnic differences in incident symptoms and conditions existed among both hospitalized and non-hospitalized patients. For example, 31-180 days after a positive SARS-CoV-2 test, hospitalized Black patients had higher odds of being diagnosed with diabetes (adjusted odds ratio [OR]: 1.96, 95% confidence interval [CI]: 1.50-2.56, q<0.001) and headaches (OR: 1.52, 95% CI: 1.11-2.08, q=0.02), compared to hospitalized white patients. Hospitalized Hispanic patients had higher odds of headaches (OR: 1.62, 95% CI: 1.21-2.17, q=0.003) and dyspnea (OR: 1.22, 95% CI: 1.05-1.42, q=0.02), compared to hospitalized white patients. Among non-hospitalized patients, Black patients had higher odds of being diagnosed with pulmonary embolism (OR: 1.68, 95% CI: 1.20-2.36, q=0.009) and diabetes (OR: 2.13, 95% CI: 1.75-2.58, q<0.001), but lower odds of encephalopathy (OR: 0.58, 95% CI: 0.45-0.75, q<0.001), compared to white patients. Hispanic patients had higher odds of being diagnosed with headaches (OR: 1.41, 95% CI: 1.24-1.60, q<0.001) and chest pain (OR: 1.50, 95% CI: 1.35-1.67, q < 0.001), but lower odds of encephalopathy (OR: 0.64, 95% CI: 0.51-0.80, q<0.001). CONCLUSIONS: Compared to white patients, patients from racial/ethnic minority groups had significantly different odds of developing potential PASC symptoms and conditions. Future research should examine the reasons for these differences.

The Telemedicine Experience in Primary Care Practices in the United States: Insights From Practice Leaders.

PURPOSE: The need to rapidly implement telemedicine in primary care during the coronavirus disease 2019 (COVID-19) pandemic was addressed differently by various practices. Using qualitative data from semistructured interviews with primary care practice leaders, we aimed to report commonly shared experiences and unique perspectives regarding telemedicine implementation and evolution/maturation since March 2020. METHODS: We administered a semistructured, 25-minute, virtual interview with 25 primary care practice leaders from 2 health systems in 2 states (New York and Florida) included in PCORnet, the Patient-Centered Outcomes Research Institute clinical research network. Questions were guided by 3 frameworks (health information technology evaluation, access to care, and health information technology life cycle) and involved practice leaders' perspectives on the process of telemedicine implementation in their practice, with a specific focus on the process of maturation and facilitators/barriers. Two researchers conducted inductive coding of qualitative data open-ended questions to identify common themes. Transcripts were electronically generated by virtual platform software. RESULTS: Twenty-five interviews were administered for practice leaders representing 87 primary care practices in 2 states. We identified the following 4 major themes: (1) the ease of telemedicine adoption depended on both patients' and clinicians' prior experience using virtual health platforms, (2) regulation of telemedicine varied across states and differentially affected the rollout processes, (3) visit triage rules were unclear, and (4) there were positive and negative effects of telemedicine on clinicians and patients. CONCLUSIONS: Practice leaders identified several challenges to telemedicine implementation and highlighted 2 areas, including telemedicine visit triage guidelines and telemedicine-specific staffing and scheduling protocols, for improvement.

Development of a novel instrument to characterize telemedicine programs in primary care.

BACKGROUND: Given the rapid deployment of telemedicine at the onset of the COVID - 19 pandemic, updated assessment methods are needed to study and characterize telemedicine programs. We developed a novel semi - structured survey instrument to systematically describe the characteristics and implementation processes of telemedicine programs in primary care. METHODS: In the context of a larger study aiming to describe telemedicine programs in primary care, a survey was developed in 3 iterative steps: 1) literature review to obtain a list of telemedicine features, facilitators, and barriers; 2) application of three evaluation frameworks; and 3) stakeholder engagement through a 2-stage feedback process. During survey refinement, items were tested against the evaluation frameworks while ensuring it could be completed within 20-25 min. Data reduction techniques were applied to explore opportunity for condensed variables/items. RESULTS: Sixty initially identified telemedicine features were reduced to 32 items / questions after stakeholder feedback. Per the life cycle framework, respondents are asked to report a month in which their telemedicine program reached a steady state, i.e., "maturation". Subsequent questions on telemedicine features are then stratified by telemedicine services offered at the pandemic onset and the reported point of maturation. Several open - ended questions allow for additional telemedicine experiences to be captured. Data reduction techniques revealed no indication for data reduction. CONCLUSION: This 32-item semi-structured survey standardizes the description of primary care telemedicine programs in terms of features as well as maturation process. This tool will facilitate evaluation of and comparisons between telemedicine programs across the United States, particularly those that were deployed at the pandemic onset.

Impact of the Early Phase of the COVID-19 Pandemic on US Healthcare Workers: Results from the HERO Registry.

BACKGROUND: The HERO registry was established to support research on the impact of the COVID-19 pandemic on US healthcare workers. OBJECTIVE: Describe the COVID-19 pandemic experiences of and effects on individuals participating in the HERO registry. DESIGN: Cross-sectional, self-administered registry enrollment survey conducted from April 10 to July 31, 2020. SETTING: Participants worked in hospitals (74.4%), outpatient clinics (7.4%), and other settings (18.2%) located throughout the nation. PARTICIPANTS: A total of 14,600 healthcare workers. MAIN MEASURES: COVID-19 exposure, viral and antibody testing, diagnosis of COVID-19, job burnout, and physical and emotional distress. KEY RESULTS: Mean age was 42.0 years, 76.4% were female, 78.9% were White, 33.2% were nurses, 18.4% were physicians, and 30.3% worked in settings at high risk for COVID-19 exposure (e.g., ICUs, EDs, COVID-19 units). Overall, 43.7% reported a COVID-19 exposure and 91.3% were exposed at work. Just 3.8% in both high- and low-risk settings experienced COVID-19 illness. In regression analyses controlling for demographics, professional role, and work setting, the risk of COVID-19 illness was higher for Black/African-Americans (aOR 2.32, 99% CI 1.45, 3.70, p < 0.01) and Hispanic/Latinos (aOR 2.19, 99% CI 1.55, 3.08, p < 0.01) compared with Whites. Overall, 41% responded that they were experiencing job burnout. Responding about the day before they completed the survey, 53% of participants reported feeling tired a lot of the day, 51% stress, 41% trouble sleeping, 38% worry, 21% sadness, 19% physical pain, and 15% anger. On average, healthcare workers reported experiencing 2.4 of these 7 distress feelings a lot of the day. CONCLUSIONS: Healthcare workers are at high risk for COVID-19 exposure, but rates of COVID-19 illness were low. The greater risk of COVID-19 infection among race/ethnicity minorities reported in the general population is also seen in healthcare workers. The HERO registry will continue to monitor changes in healthcare worker well-being during the pandemic. TRIAL REGISTRATION: ClinicalTrials.gov identifier NCT04342806.

A predictive model of clinical deterioration among hospitalized COVID-19 patients by harnessing hospital course trajectories.

From early March through mid-May 2020, the COVID-19 pandemic overwhelmed hospitals in New York City. In anticipation of ventilator shortages and limited ICU bed capacity, hospital operations prioritized the development of prognostic tools to predict clinical deterioration. However, early experience from frontline physicians observed that some patients developed unanticipated deterioration after having relatively stable periods, attesting to the uncertainty of clinical trajectories among hospitalized patients with COVID-19. Prediction tools that incorporate clinical variables at one time-point, usually on hospital presentation, are suboptimal for patients with dynamic changes and evolving clinical trajectories. Therefore, our study team developed a machine-learning algorithm to predict clinical deterioration among hospitalized COVID-19 patients by extracting clinically meaningful features from complex longitudinal laboratory and vital sign values during the early period of hospitalization with an emphasis on informative missing-ness. To incorporate the evolution of the disease and clinical practice over the course of the pandemic, we utilized a time-dependent cross-validation strategy for model development. Finally, we validated our prediction model on an external validation cohort of COVID-19 patients served in a demographically distinct population from the training cohort. The main finding of our study is the identification of risk profiles of early, late and no clinical deterioration during the course of hospitalization. While risk prediction models that include simple predictors at ED presentation and clinical judgement are able to identify any deterioration vs. no deterioration, our methodology is able to isolate a particular risk group that remain stable initially but deteriorate at a later stage of the course of hospitalization. We demonstrate the superior predictive performance with the utilization of laboratory and vital sign data during the early period of hospitalization compared to the utilization of data at presentation alone. Our results will allow efficient hospital resource allocation and will motivate research in understanding the late deterioration risk group.

Routine Laboratory Blood Tests Predict SARS-CoV-2 Infection Using Machine Learning.

BACKGROUND: Accurate diagnostic strategies to identify SARS-CoV-2 positive individuals rapidly for management of patient care and protection of health care personnel are urgently needed. The predominant diagnostic test is viral RNA detection by RT-PCR from nasopharyngeal swabs specimens, however the results are not promptly obtainable in all patient care locations. Routine laboratory testing, in contrast, is readily available with a turn-around time (TAT) usually within 1-2 hours. METHOD: We developed a machine learning model incorporating patient demographic features (age, sex, race) with 27 routine laboratory tests to predict an individual's SARS-CoV-2 infection status. Laboratory testing results obtained within 2 days before the release of SARS-CoV-2 RT-PCR result were used to train a gradient boosting decision tree (GBDT) model from 3,356 SARS-CoV-2 RT-PCR tested patients (1,402 positive and 1,954 negative) evaluated at a metropolitan hospital. RESULTS: The model achieved an area under the receiver operating characteristic curve (AUC) of 0.854 (95% CI: 0.829-0.878). Application of this model to an independent patient dataset from a separate hospital resulted in a comparable AUC (0.838), validating the generalization of its use. Moreover, our model predicted initial SARS-CoV-2 RT-PCR positivity in 66% individuals whose RT-PCR result changed from negative to positive within 2 days. CONCLUSION: This model employing routine laboratory test results offers opportunities for early and rapid identification of high-risk SARS-CoV-2 infected patients before their RT-PCR results are available. It may play an important role in assisting the identification of SARS-CoV-2 infected patients in areas where RT-PCR testing is not accessible due to financial or supply constraints.

Association Between Residential Neighborhood Social Conditions and Health Care Utilization and Costs.

BACKGROUND: Social factors are important drivers of health. However, it is unclear to what extent neighborhood social conditions are associated with total and preventable health care utilization and costs. OBJECTIVES: To examine the association of neighborhood social conditions with total annual and potentially preventable Medicare costs. RESEARCH DESIGN AND SUBJECTS: Retrospective cohort study. Medicare claims data from 2013 to 2014 linked with neighborhood social conditions at the US census block group level of 2013 for 93,429 Medicare fee-for-service and dually eligible patients. MEASURES: Neighborhood social conditions were measured by Area Deprivation Index at the census block group level, categorized into quintiles. Outcomes included total annual and potentially preventable utilization and costs. RESULTS: After adjustment for demographics and comorbidities, patients with the least disadvantaged social conditions had higher total annual Medicare costs [$427; 95% confidence interval (CI), $200-$655] and similar potentially preventable costs (-$23; 95% CI, -$56 to $10) as compared with patients with the intermediate level social conditions. Patients with the most disadvantaged social conditions had similar total Medicare costs (-$22; 95% CI, -$342 to $298) but higher potentially preventable costs ($53; 95% CI, $1-$104) than patients with the intermediate level social conditions. CONCLUSIONS: Disadvantaged neighborhood conditions are associated with lower total annual Medicare costs but higher potentially preventable costs after controlling for demographic, medical, and other patient characteristics. Socioeconomic barriers may limit access and use of primary care and disease management services, resulting in a higher proportion of their health care costs going to potentially preventable care.

Potentially Preventable Spending Among High-Cost Medicare Patients: Implications for Healthcare Delivery.

BACKGROUND: High-cost patients account for a disproportionate share of healthcare spending. The proportion and distribution of potentially preventable spending among subgroups of high-cost patients are largely unknown. OBJECTIVE: To examine the distribution of potentially preventable spending among high-cost Medicare patients overall and potentially preventable spending associated with each high-cost category. DESIGN: A cross-sectional study. We merged Medicare claims and social determinants of health data to group patients into high-cost categories and quantify potentially preventable spending. PATIENTS: A total of 556,053 Medicare fee-for-service and dual-eligible beneficiaries with at least one healthcare encounter in the New York metropolitan area in 2014. MAIN MEASURES: High-cost patients were mapped into 10 non-mutually exclusive categories. The primary outcome was episodic spending associated with preventable ED visits, preventable hospitalizations, and unplanned 30-day readmissions. KEY RESULTS: Overall, potentially preventable spending accounted for 10.4% of overall spending in 2014. Preventable spending accounted for 13.3% of total spending among high-cost patients and 4.9% among non-high-cost patients (P < 0.001). Among high-cost patients, 44.0% experienced at least one potentially preventable encounter compared with 11.4% of non-high-cost patients (P < 0.001), and high-cost patients accounted for 71.5% of total preventable spending. High-cost patients had on average $11,502 in potentially preventable spending-more than 20 times more than non-high-cost patients ($510). High-cost patients in the seriously ill, frail, or serious mental illness categories accounted for the highest proportion of potentially preventable spending overall, while end-stage renal disease, serious illness, and opioid use disorder were associated with the highest preventable spending per patient. CONCLUSION: Potentially preventable spending was concentrated among high-cost patients who were seriously ill, frail, or had a serious mental illness. Interventions targeting these subgroups may be helpful for reducing preventable utilization.

Identifying Patients with Persistent Preventable Utilization Offers an Opportunity to Reduce Unnecessary Spending.

BACKGROUND: Improving care for high-cost patients is increasingly important for improving the value of healthcare. Most prior research has focused on identifying patients with high costs, but the extent to which these costs are potentially preventable remains unclear. OBJECTIVE: To identify patients with persistent preventable utilization and compare their characteristics with high-cost patients. DESIGN: Descriptive analysis using Medicare claims data from 2013 to 2014. PARTICIPANTS: Medicare fee-for-service and dual-eligible beneficiaries (N = 515,689) from the New York metropolitan area who were continuously enrolled in Medicare Parts A and B in 2013 and 2014. MAIN MEASURES: The primary analysis focuses on patients with persistent preventable utilization (at least one preventable emergency department visit, hospitalization, or 30-day readmission in both 2013 and 2014) and high-cost patients in 2014 (top 10% of total annual spending). We compared demographic, medical, behavioral, and social characteristics and total and preventable healthcare utilization between these two groups. KEY RESULTS: Patients with persistent preventable utilization accounted for 4.8% of the overall patient population, 13.4% of overall costs, but 46.2% of preventable costs among all Medicare patients. Compared with high-cost patients, patients with persistent preventable utilization had lower median healthcare costs ($33,383 vs. $56,552), but their median potentially preventable costs were seven times higher ($7151 vs. $928). We also found that 1.9% of patients could be categorized in both the persistent preventable utilization group and the high-cost group. This subset of patients had the highest median Medicare costs and preventable costs and represented over 30% of total preventable spending and 9.4% of overall costs among all Medicare patients. CONCLUSION: Designing and targeting interventions for patients with persistent preventable utilization may offer an important opportunity to reduce unnecessary utilization and promote high-value care.

Correction to: Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system.

Following publication of the original article [1], the authors reported that the article erroneously stated that Dr. Ancker was affiliated with the Tehran University of Medical Sciences. Dr. Ancker is not affiliated with that institution.

A Tale of 2 Constituencies: Exploring Patient and Clinician Perspectives in the Age of Big Data.

BACKGROUND: Patient and clinician stakeholders are inadequately engaged in key aspects of research, particularly regarding use of Big Data to study and improve patient-centered outcomes. Little is known about the attitudes, interests, and concerns of stakeholders regarding such data. RESEARCH DESIGN: The New York City Clinical Data Research Network (NYC-CDRN), a collaboration of research, clinical, and community leaders built a deidentified dataset containing electronic health records from millions of New Yorkers. Guided by a patient-clinician advisory board, we developed a question guide to explore patient and clinician experiences and ideas about research using large datasets. Trained facilitators led discussions during preexisting patient, community, and clinician group meetings. The research team coded meeting notes and identified themes. RESULTS: Fully 272 individuals participated in 19 listening sessions (139 patients/advocates, 133 clinicians) at 6 medical centers with diverse NYC communities: 76% were female and 63% were nonwhite. Clinicians and patients agreed on all major themes including the central role of clinicians in introducing patients to research and the need for public campaigns to inform stakeholders about Big Data. Stakeholders were interested in using granular data to compare the care and clinical outcomes of their neighborhoods with others across NYC, but were also concerned that data could not truly be deidentified. CONCLUSIONS: Clinicians and patients agree on potential benefits of stakeholder-engaged Big Data research and provided suggestions for further research and building stakeholder research capacity. This evaluation demonstrated the potential of brief meetings with existing patient and clinical groups to explore barriers and facilitators to patient and clinician engagement.

The Expanding Role of Antimicrobial Stewardship Programs in Hospitals in the United States: Lessons Learned from a Multisite Qualitative Study.

BACKGROUND: Misuse of antibiotics can lead to the development of antibiotic resistance, which adversely affects morbidity, mortality, length of stay, and cost. To combat the threat of antimicrobial resistance, The Joint Commission and the Centers for Medicare & Medicaid Services have initiated or proposed requirements for hospitals to have antimicrobial stewardship programs (ASPs), but implementation remains challenging. A key-informant interview study was conducted to describe the characteristics and innovative strategies of leading ASPs. METHODS: Semistructured interviews were conducted with 12 program leaders at four ASPs in the United States, chosen by purposive sampling on the basis of national reputation, scholarship, and geography. Questions focused on ASP implementation, program structure, strengths, weaknesses, lessons learned, and future directions. Content analysis was used to identify dominant themes. RESULTS: Three major themes were identified. The first was evolution of ASPs from a top-down structure to a more diffuse approach involving unit-based pharmacists, multidisciplinary staff, and shared responsibility for antimicrobial prescribing under the ASPs' leadership. The second theme was integration of information technology (IT) systems, which enabled real-time interventions to optimize antimicrobial therapy and patient management. The third was barriers to technology integration, including limited resources for data analysis and poor interoperability between software systems. CONCLUSION: The study provides valuable insights on program implementation at a sample of leading ASPs across the United States. These ASPs used expansion of personnel to amplify the ASP's impact and integrated IT resources into daily work flow to improve efficiency. These findings can be used to guide implementation at other hospitals and aid in future policy development.

Meaningful Use of Electronic Health Records by Outpatient Physicians and Readmissions of Medicare Fee-for-Service Beneficiaries.

BACKGROUND: Nearly one-fifth of hospitalized Medicare fee-for-service beneficiaries are readmitted within 30 days. Participation in the Meaningful Use initiative among outpatient physicians may reduce readmissions. OBJECTIVE: To evaluate the impact of outpatient physicians' participation in Meaningful Use on readmissions. SUBJECTS AND RESEARCH DESIGN: The study population included 90,774 Medicare fee-for-service beneficiaries from New York State (2010-2012). We compared changes in the adjusted odds of readmission for patients of physicians who participated in Meaningful Use-stage 1, before and after attestation as meaningful users, with concurrent patients of matched control physicians who used paper records or electronic health records without Meaningful Use participation. Three secondary analyses were conducted: (1) limited to patients with 3+ Elixhauser comorbidities; (2) limited to patients with conditions used by Medicare to penalize hospitals with high readmission rates (acute myocardial infarction, congestive heart failure, and pneumonia); and (3) using only patients of physicians with electronic health records who were not meaningful users as the controls. MAIN OUTCOME: Thirty-day readmission. RESULTS: Patients of Meaningful Use physicians had 6% lower odds of readmission compared with patients of physicians who were not meaningful users, but the estimate was not statistically significant (odds ratio: 0.94, 95% confidence interval, 0.88-1.01). Estimated odds ratios from secondary analyses were broadly consistent with our primary analysis. CONCLUSIONS: Physician participation in Meaningful Use was not associated with reduced readmissions. Additional studies are warranted to see if readmissions decline in future stages of Meaningful Use where more emphasis is placed on health information exchange and outcomes.

The Patient-Centered Medical Home and Associations With Health Care Quality and Utilization: A 5-Year Cohort Study.

BACKGROUND: Effects of the patient-centered medical home (PCMH) are unclear. Previous studies had relatively short follow-up and may not have distinguished effects of the PCMH (which involves electronic health records [EHRs] plus organizational changes) from those of EHRs alone. OBJECTIVE: To determine effects of the PCMH on health care quality and utilization compared with paper records alone and EHRs alone, with extended follow-up. DESIGN: Prospective cohort study (2008 to 2012), including 3 years after PCMH implementation. (ClinicalTrials.gov: NCT00793065). SETTING: The Hudson Valley, a multipayer, multiprovider region in New York. PARTICIPANTS: 438 primary care physicians in 226 practices, with 136 480 patients across 5 health plans. INTERVENTION: Level III PCMH, as defined by the National Committee for Quality Assurance. MEASUREMENTS: Claims-based outcomes included 8 quality and 7 utilization measures. Generalized estimating equations were used to compare adjusted differences in rates of change across study groups. RESULTS: Patterns of quality were fairly similar across groups. Utilization patterns were similar across groups from 2008 to 2011 but showed modest differences between the PCMH and control groups on most measures in 2012. For example, hospitalizations were relatively stable from 2008 to 2011 (approximately 3.9 to 5.2 per 100 patients per year) but decreased in the PCMH group in 2012 (incidence rate ratio, 0.79 [95% CI, 0.69 to 0.90] compared with paper records). Emergency department visits were highest for the PCMH group (16.7 per 100 patients at baseline and 15.4 per 100 patients at the end of the study period) and lowest for the paper group (14.3 per 100 patients at baseline and 12.2 per 100 patients at the end of the study period), but the rate of change did not differ across groups. LIMITATION: Possible unmeasured confounding. CONCLUSION: The PCMH was associated with modest changes in most utilization measures and provided similar quality compared with EHRs and paper records. PRIMARY FUNDING SOURCE: The Commonwealth Fund and the New York State Department of Health.

Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system.

BACKGROUND: Although alert fatigue is blamed for high override rates in contemporary clinical decision support systems, the concept of alert fatigue is poorly defined. We tested hypotheses arising from two possible alert fatigue mechanisms: (A) cognitive overload associated with amount of work, complexity of work, and effort distinguishing informative from uninformative alerts, and (B) desensitization from repeated exposure to the same alert over time. METHODS: Retrospective cohort study using electronic health record data (both drug alerts and clinical practice reminders) from January 2010 through June 2013 from 112 ambulatory primary care clinicians. The cognitive overload hypotheses were that alert acceptance would be lower with higher workload (number of encounters, number of patients), higher work complexity (patient comorbidity, alerts per encounter), and more alerts low in informational value (repeated alerts for the same patient in the same year). The desensitization hypothesis was that, for newly deployed alerts, acceptance rates would decline after an initial peak. RESULTS: On average, one-quarter of drug alerts received by a primary care clinician, and one-third of clinical reminders, were repeats for the same patient within the same year. Alert acceptance was associated with work complexity and repeated alerts, but not with the amount of work. Likelihood of reminder acceptance dropped by 30% for each additional reminder received per encounter, and by 10% for each five percentage point increase in proportion of repeated reminders. The newly deployed reminders did not show a pattern of declining response rates over time, which would have been consistent with desensitization. Interestingly, nurse practitioners were 4 times as likely to accept drug alerts as physicians. CONCLUSIONS: Clinicians became less likely to accept alerts as they received more of them, particularly more repeated alerts. There was no evidence of an effect of workload per se, or of desensitization over time for a newly deployed alert. Reducing within-patient repeats may be a promising target for reducing alert overrides and alert fatigue.