Implications of Practice Variability

Closing the Chasm: Understanding and Addressing the Anesthesia Workforce Supply and Demand Imbalance

The imbalance in anesthesia workforce supply and demand has been exacerbated post-COVID due to a surge in demand for anesthesia care, especially in non-operating room anesthetizing sites, at a faster rate than the increase in anesthesia clinicians. The consequences of this imbalance or labor shortage compromise healthcare facilities, adversely affect the cost of care, worsen anesthesia workforce burnout, disrupt procedural and surgical schedules, and threaten academic missions and the ability to educate future anesthesiologists. In developing possible solutions, one must examine emerging trends that are affecting the anesthesia workforce, new technologies that will transform anesthesia care and the workforce, and financial considerations, including governmental payment policies. Possible practice solutions to this imbalance will require both short- and long-term multifactorial approaches that include increasing training positions and retention policies, improving capacity through innovations, leveraging technology, and addressing financial constraints.

Drivers of telemedicine in primary care clinics at a large academic medical centre

BACKGROUND

COVID-19 disrupted healthcare routines and prompted rapid telemedicine implementation. We investigated the drivers of visit modality selection (telemedicine versus in-person) in primary care clinics at an academic medical centre.

METHODS

We used electronic medical record data from March 2020 to May 2022 from 13 primary care clinics (N = 21,031 new, N = 207,292 return visits), with 55% overall telemedicine use. Hierarchical logistic regression and cross-validation methods were used to estimate the variation in visit modality explained by the patient, clinician and visit factors as measured by the mean-test area under the curve (AUC).

RESULTS

There was significant variation in telemedicine use across clinicians (ranging from 0-100%) for the same visit diagnosis. The strongest predictors of telemedicine were the clinician seen for new visits (mean AUC of 0.79) and the primary visit diagnosis for return visits (0.77). Models based on all patient characteristics combined accounted for relatively little variation in modality selection, 0.54 for new and 0.58 for return visits, respectively. Amongst patient characteristics, males, patients over 65 years, Asians and patient's with non-English language preferences used less telemedicine; however, those using interpreter services used significantly more telemedicine.

CONCLUSION

Clinician seen and primary visit diagnoses were the best predictors of visit modality. The distinction between new and return visits and the minimal impact of patient characteristics on visit modality highlights the complexity of clinical care and warrants research approaches that go beyond linear models to uncover the emergent causal effects of specific technology features mediated by tasks, people and organisations.

Can we nudge to reduce the perioperative low value care? Decision making factors influencing safe practice implementation

PURPOSE OF THE REVIEW

Highlight sources of low-value care (LVC) during the perioperative period help understanding the decision making behind its persistence, the barriers for change, and the potential implementation strategies to reduce it.

RECENT FINDINGS

The behavioural economics science spread of use through aligned strategies or nudge units offer an opportunity to improve success in the LVC reduction.

SUMMARY

LVC, such as unneeded surgeries, or preanaesthesia tests for low-risk surgeries in low-risk patients, is a relevant source of waste and preventable harm, most especially in the perioperative period. Despite the international focus on it, initial efforts to reduce it in the last decade have not clearly shown a sustainable improvement. Understanding the shared decision-making process and the barriers to be expected when tackling LVC is the first step to build the change. Applying a structured strategy based on the behavioural science principles may be the path to increasing high value care in an effective an efficient way. It is time to foster nudge units at different healthcare system levels.

Patient-, Clinician-, and Institution-level Variation in Inotrope Use for Cardiac Surgery: A Multicenter Observational Analysis

BACKGROUND

Conflicting evidence exists regarding the risks and benefits of inotropic therapies during cardiac surgery, and the extent of variation in clinical practice remains understudied. Therefore, the authors sought to quantify patient-, anesthesiologist-, and hospital-related contributions to variation in inotrope use.

METHODS

In this observational study, nonemergent adult cardiac surgeries using cardiopulmonary bypass were reviewed across a multicenter cohort of academic and community hospitals from 2014 to 2019. Patients who were moribund, receiving mechanical circulatory support, or receiving preoperative or home inotropes were excluded. The primary outcome was an inotrope infusion (epinephrine, dobutamine, milrinone, dopamine) administered for greater than 60 consecutive min intraoperatively or ongoing upon transport from the operating room. Institution-, clinician-, and patient-level variance components were studied.

RESULTS

Among 51,085 cases across 611 attending anesthesiologists and 29 hospitals, 27,033 (52.9%) cases received at least one intraoperative inotrope, including 21,796 (42.7%) epinephrine, 6,360 (12.4%) milrinone, 2,000 (3.9%) dobutamine, and 602 (1.2%) dopamine (non-mutually exclusive). Variation in inotrope use was 22.6% attributable to the institution, 6.8% attributable to the primary attending anesthesiologist, and 70.6% attributable to the patient. The adjusted median odds ratio for the same patient receiving inotropes was 1.73 between 2 randomly selected clinicians and 3.55 between 2 randomly selected institutions. Factors most strongly associated with increased likelihood of inotrope use were institutional medical school affiliation (adjusted odds ratio, 6.2; 95% CI, 1.39 to 27.8), heart failure (adjusted odds ratio, 2.60; 95% CI, 2.46 to 2.76), pulmonary circulation disorder (adjusted odds ratio, 1.72; 95% CI, 1.58 to 1.87), loop diuretic home medication (adjusted odds ratio, 1.55; 95% CI, 1.42 to 1.69), Black race (adjusted odds ratio, 1.49; 95% CI, 1.32 to 1.68), and digoxin home medication (adjusted odds ratio, 1.48; 95% CI, 1.18 to 1.86).

CONCLUSIONS

Variation in inotrope use during cardiac surgery is attributable to the institution and to the clinician, in addition to the patient. Variation across institutions and clinicians suggests a need for future quantitative and qualitative research to understand variation in inotrope use affecting outcomes and develop evidence-based, patient-centered inotrope therapies.

EDITOR’S PERSPECTIVE

Drivers of variation in telemedicine use during the COVID-19 pandemic: The experience of a large academic cardiovascular practice

BACKGROUND

COVID-19 spurred rapid adoption and expansion of telemedicine. We investigated the factors driving visit modality (telemedicine vs. in-person) for outpatient visits at a large cardiovascular center.

METHODS

We used electronic health record data from March 2020 to February 2021 from four cardiology subspecialties (general cardiology, electrophysiology, heart failure, and interventional cardiology) at a large academic health system in Northern California. There were 21,912 new and return visits with 69% delivered by telemedicine. We used hierarchical logistic regression and cross-validation methods to estimate the variation in visit modality explained by patient, clinician, and visit factors as measured by the mean area under the curve.

RESULTS

Across all subspecialties, the clinician seen was the strongest predictor of telemedicine usage, while primary visit diagnosis was the next most predictive. In general cardiology, the model based on clinician seen had a mean area under the curve of 0.83, the model based on the primary diagnosis had a mean area under the curve of 0.69, and the model based on all patient characteristics combined had a mean area under the curve of 0.56. There was significant variation in telemedicine use across clinicians within each subspecialty, even for visits with the same primary visit diagnosis.

CONCLUSION

Individual clinician practice patterns had the largest influence on visit modality across subspecialties in a large cardiovascular medicine practice, while primary diagnosis was less predictive, and patient characteristics even less so. Cardiovascular clinics should reduce variability in visit modality selection through standardized processes that integrate clinical factors and patient preference.

Multicentre analysis of practice patterns regarding benzodiazepine use in cardiac surgery

BACKGROUND

There is controversy regarding optimal use of benzodiazepines during cardiac surgery, and it is unknown whether and to what extent there is variation in practice. We sought to describe benzodiazepine use and sources of variation during cardiac surgeries across patients, clinicians, and institutions.

METHODS

We conducted an analysis of adult cardiac surgeries across a multicentre consortium of USA academic and private hospitals from 2014 to 2019. The primary outcome was administration of a benzodiazepine from 2 h before anaesthesia start until anaesthesia end. Institutional-, clinician-, and patient-level variables were analysed via multilevel mixed-effects models.

RESULTS

Of 65 508 patients cared for by 825 anaesthesiology attending clinicians (consultants) at 33 institutions, 58 004 patients (88.5%) received benzodiazepines with a median midazolam-equivalent dose of 4.0 mg (inter-quartile range [IQR], 2.0-6.0 mg). Variation in benzodiazepine dosage administration was 54.7% attributable to institution, 14.7% to primary attending anaesthesiology clinician, and 30.5% to patient factors. The adjusted median odds ratio for two similar patients receiving a benzodiazepine was 2.68 between two randomly selected clinicians and 4.19 between two randomly selected institutions. Factors strongly associated (adjusted odds ratio, <0.75, or >1.25) with significantly decreased likelihoods of benzodiazepine administration included older age (>80 vs ≤50 yr; adjusted odds ratio=0.04; 95% CI, 0.04-0.05), university affiliation (0.08, 0.02-0.35), recent year of surgery (0.42, 0.37-0.49), and low clinician case volume (0.44, 0.25-0.75). Factors strongly associated with significantly increased likelihoods of benzodiazepine administration included cardiopulmonary bypass (2.26, 1.99-2.55), and drug use history (1.29, 1.02-1.65).

CONCLUSIONS

Two-thirds of the variation in benzodiazepine administration during cardiac surgery are associated with institutions and attending anaesthesiology clinicians (consultants). These data, showing wide variations in administration, suggest that rigorous research is needed to guide evidence-based and patient-centred benzodiazepine administration.

Anesthesiologists' Role in Value-based Perioperative Care and Healthcare Transformation

Health care is undergoing major transformation with a shift from fee-for-service care to fee-for-value. The advent of new care delivery and payment models is serving as a driver for value-based care. Hospitals, payors, and patients increasingly expect physicians and healthcare systems to improve outcomes and manage costs. The impact of the coronavirus disease 2019 (COVID-19) pandemic on surgical and procedural practices further highlights the urgency and need for anesthesiologists to expand their roles in perioperative care, and to impact system improvement. While there have been substantial advances in anesthesia care, perioperative complications and mortality after surgery remain a key concern. Anesthesiologists are in a unique position to impact perioperative health care through their multitude of interactions and influences on various aspects of the perioperative domain, by using the surgical experience as the first touchpoint to reengage the patient in their own health care. Among the key interventions that are being effectively instituted by anesthesiologists include proactive engagement in preoperative optimization of patients' health; personalization and standardization of care delivery by segmenting patients based upon their complexity and risk; and implementation of best practices that are data-driven and evidence-based and provide structure that allow the patient to return to their optimal state of functional, cognitive, and psychologic health. Through collaborative relationships with other perioperative stakeholders, anesthesiologists can consolidate their role as clinical leaders driving value-based care and healthcare transformation in the best interests of patients.