Publications

2022

Robinson, Kortney A, Cornelius A Thiels, Sean Stokes, Sarah Duncan, Mario Feranil, Aaron Fleishman, Charles H Cook, et al. (2022) 2022. “Comparing Clinician Consensus Recommendations to Patient-Reported Opioid Use Across Multiple Hospital Systems.”. Annals of Surgery 275 (2): e361-e365. https://doi.org/10.1097/SLA.0000000000003986.
Publisher's Version

OBJECTIVE: We compare consensus recommendations for 5 surgical procedures to prospectively collected patient consumption data. To address local variation, we combined data from multiple hospitals across the country.

SUMMARY OF BACKGROUND DATA: One approach to address the opioid epidemic has been to create prescribing consensus reports for common surgical procedures. However, it is unclear how these guidelines compare to patient-reported data from multiple hospital systems.

METHODS: Prospective observational studies of surgery patients were completed between 3/2017 and 12/2018. Data were collected utilizing post-discharge surveys and chart reviews from 5 hospitals (representing 3 hospital systems) in 5 states across the USA. Prescribing recommendations for 5 common surgical procedures identified in 2 recent consensus reports were compared to the prospectively collected aggregated data. Surgeries included: laparoscopic cholecystectomy, open inguinal hernia repair, laparoscopic inguinal hernia repair, partial mastectomy without sentinel lymph node biopsy, and partial mastectomy with sentinel lymph node biopsy.

RESULTS: Eight hundred forty-seven opioid-naïve patients who underwent 1 of the 5 studied procedures reported counts of unused opioid pills after discharge. Forty-one percent did not take any opioid medications, and across all surgeries, the median consumption was 3 5 mg oxycodone pills or less. Generally, consensus reports recommended opioid quantities that were greater than the 75th percentile of consumption, and for 2 procedures, recommendations exceeded the 90th percentile of consumption.

CONCLUSIONS: Although consensus recommendations were an important first step to address opioid prescribing, our data suggests that following these recommendations would result in 47%-56% of pills prescribed remaining unused. Future multi-institutional efforts should be directed toward refining and personalizing prescribing recommendations.

Marwaha, Jayson S, Adam B Landman, Gabriel A Brat, Todd Dunn, and William J Gordon. (2022) 2022. “Deploying Digital Health Tools Within Large, Complex Health Systems: Key Considerations for Adoption and Implementation.”. NPJ Digital Medicine 5 (1): 13. https://doi.org/10.1038/s41746-022-00557-1.
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In recent years, the number of digital health tools with the potential to significantly improve delivery of healthcare services has grown tremendously. However, the use of these tools in large, complex health systems remains comparatively limited. The adoption and implementation of digital health tools at an enterprise level is a challenge; few strategies exist to help tools cross the chasm from clinical validation to integration within the workflows of a large health system. Many previously proposed frameworks for digital health implementation are difficult to operationalize in these dynamic organizations. In this piece, we put forth nine dimensions along which clinically validated digital health tools should be examined by health systems prior to adoption, and propose strategies for selecting digital health tools and planning for implementation in this setting. By evaluating prospective tools along these dimensions, health systems can evaluate which existing digital health solutions are worthy of adoption, ensure they have sufficient resources for deployment and long-term use, and devise a strategic plan for implementation.

Robinson, Kortney A, Jayson S Marwaha, Chris J Kennedy, Brendin R Beaulieu-Jones, Aaron Fleishman, Justin K Yu, Larry A Nathanson, and Gabriel A Brat. (2022) 2022. “Evaluation of U.S. State Opioid Prescribing Restrictions Using Patient Opioid Consumption Patterns from a Single, Urban, Academic Institution.”. Substance Abuse 43 (1): 932-36. https://doi.org/10.1080/08897077.2022.2056934.
Publisher's Version

Background: Since 2017, states, insurers, and pharmacies have placed blanket limits on the duration and quantity of opioid prescriptions. In many states, overlapping duration and daily dose limits yield maximum prescription limits of 150-350 morphine milligram equivalents (MMEs). There is limited knowledge of how these restrictions compare with actual patient opioid consumption; while changes in prescription patterns and opioid misuse rates have been studied, these are, at best, weak proxies for actual pain control consumption. We sought to determine how patients undergoing surgery would be affected by opioid prescribing restrictions using actual patient opioid consumption data. Methods: We constructed a prospective database of post-discharge opioid consumption: patients undergoing surgery at one institution were called after discharge to collect opioid consumption data. Patients whose opioid consumption exceeded 150 and 350 MME were identified. Results: Two thousand nine hundred and seventy-one patients undergoing 54 common surgical procedures were included in our study. Twenty-one percent of patients consumed more than the 150 MME limit. Only 7% of patients consumed above the 350 MME limit. Typical (non-outlier) opioid consumption, defined as less than the 75th percentile of consumption for any given procedure, exceeded the 150 MME and 350 MME limits for 41 and 7% of procedures, respectively. Orthopedic, spinal/neurosurgical, and complex abdominal procedures most commonly exceeded these limits. Conclusions: While most patients undergoing surgery are unaffected by recent blanket prescribing limits, those undergoing a specific subset of procedures are likely to require more opioids than the restrictions permit; providers should be aware that these patients may require a refill to adequately control post-surgical pain. Real consumption data should be used to guide these restrictions and inform future interventions so the risk of worsened pain control (and its troublesome effects on opioid misuse) is minimized. Procedure-specific prescribing limits may be one approach to prevent misuse, while also optimizing post-operative pain control.

2021

Weber, Griffin, Chuan Hong, Nathan Palmer, Paul Avillach, Shawn Murphy, Alba Gutiérrez-Sacristán, Zongqi Xia, et al. 2021. “International Comparisons of Harmonized Laboratory Value Trajectories to Predict Severe COVID-19: Leveraging the 4CE Collaborative Across 342 Hospitals and 6 Countries: A Retrospective Cohort Study”. MedRxiv. https://doi.org/10.1101/2020.12.16.20247684.
OBJECTIVES: To perform an international comparison of the trajectory of laboratory values among hospitalized patients with COVID-19 who develop severe disease and identify optimal timing of laboratory value collection to predict severity across hospitals and regions. DESIGN: Retrospective cohort study. SETTING: The Consortium for Clinical Characterization of COVID-19 by EHR (4CE), an international multi-site data-sharing collaborative of 342 hospitals in the US and in Europe. PARTICIPANTS: Patients hospitalized with COVID-19, admitted before or after PCR-confirmed result for SARS-CoV-2. Primary and secondary outcome measures: Patients were categorized as ″ever-severe″ or ″never-severe″ using the validated 4CE severity criteria. Eighteen laboratory tests associated with poor COVID-19-related outcomes were evaluated for predictive accuracy by area under the curve (AUC), compared between the severity categories. Subgroup analysis was performed to validate a subset of laboratory values as predictive of severity against a published algorithm. A subset of laboratory values (CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin) was compared between North American and European sites for severity prediction. RESULTS: Of 36,447 patients with COVID-19, 19,953 (43.7%) were categorized as ever-severe. Most patients (78.7%) were 50 years of age or older and male (60.5%). Longitudinal trajectories of CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin showed association with disease severity. Significant differences of laboratory values at admission were found between the two groups. With the exception of D-dimer, predictive discrimination of laboratory values did not improve after admission. Sub-group analysis using age, D-dimer, CRP, and lymphocyte count as predictive of severity at admission showed similar discrimination to a published algorithm (AUC=0.88 and 0.91, respectively). Both models deteriorated in predictive accuracy as the disease progressed. On average, no difference in severity prediction was found between North American and European sites. CONCLUSIONS: Laboratory test values at admission can be used to predict severity in patients with COVID-19. Prediction models show consistency across international sites highlighting the potential generalizability of these models.
Kohane, Isaac, Bruce Aronow, Paul Avillach, Brett Beaulieu-Jones, Riccardo Bellazzi, Robert Bradford, Gabriel Brat, et al. 2021. “What Every Reader Should Know About Studies Using Electronic Health Record Data But May Be Afraid to Ask”. J Med Internet Res 23 (3): e22219. https://doi.org/10.2196/22219.
Coincident with the tsunami of COVID-19-related publications, there has been a surge of studies using real-world data, including those obtained from the electronic health record (EHR). Unfortunately, several of these high-profile publications were retracted because of concerns regarding the soundness and quality of the studies and the EHR data they purported to analyze. These retractions highlight that although a small community of EHR informatics experts can readily identify strengths and flaws in EHR-derived studies, many medical editorial teams and otherwise sophisticated medical readers lack the framework to fully critically appraise these studies. In addition, conventional statistical analyses cannot overcome the need for an understanding of the opportunities and limitations of EHR-derived studies. We distill here from the broader informatics literature six key considerations that are crucial for appraising studies utilizing EHR data: data completeness, data collection and handling (eg, transformation), data type (ie, codified, textual), robustness of methods against EHR variability (within and across institutions, countries, and time), transparency of data and analytic code, and the multidisciplinary approach. These considerations will inform researchers, clinicians, and other stakeholders as to the recommended best practices in reviewing manuscripts, grants, and other outputs from EHR-data derived studies, and thereby promote and foster rigor, quality, and reliability of this rapidly growing field.
Beaulieu-Jones, Brett, William Yuan, Gabriel Brat, Andrew Beam, Griffin Weber, Marshall Ruffin, and Isaac Kohane. 2021. “Machine Learning for Patient Risk Stratification: Standing On, or Looking Over, the Shoulders of Clinicians?”. NPJ Digit Med 4 (1): 62. https://doi.org/10.1038/s41746-021-00426-3.
Machine learning can help clinicians to make individualized patient predictions only if researchers demonstrate models that contribute novel insights, rather than learning the most likely next step in a set of actions a clinician will take. We trained deep learning models using only clinician-initiated, administrative data for 42.9 million admissions using three subsets of data: demographic data only, demographic data and information available at admission, and the previous data plus charges recorded during the first day of admission. Models trained on charges during the first day of admission achieve performance close to published full EMR-based benchmarks for inpatient outcomes: inhospital mortality (0.89 AUC), prolonged length of stay (0.82 AUC), and 30-day readmission rate (0.71 AUC). Similar performance between models trained with only clinician-initiated data and those trained with full EMR data purporting to include information about patient state and physiology should raise concern in the deployment of these models. Furthermore, these models exhibited significant declines in performance when evaluated over only myocardial infarction (MI) patients relative to models trained over MI patients alone, highlighting the importance of physician diagnosis in the prognostic performance of these models. These results provide a benchmark for predictive accuracy trained only on prior clinical actions and indicate that models with similar performance may derive their signal by looking over clinician's shoulders-using clinical behavior as the expression of preexisting intuition and suspicion to generate a prediction. For models to guide clinicians in individual decisions, performance exceeding these benchmarks is necessary.
Estiri, Hossein, Zachary Strasser, Gabriel Brat, Yevgeniy Semenov, The Consortium Characterization COVID-19 EHR (4CE), Chirag Patel, and Shawn Murphy. 2021. “Evolving Phenotypes of Non-Hospitalized Patients That Indicate Long Covid”. MedRxiv. https://doi.org/10.1101/2021.04.25.21255923.
For some SARS-CoV-2 survivors, recovery from the acute phase of the infection has been grueling with lingering effects. Many of the symptoms characterized as the post-acute sequelae of COVID-19 (PASC) could have multiple causes or are similarly seen in non-COVID patients. Accurate identification of phenotypes will be important to guide future research and help the healthcare system focus its efforts and resources on adequately controlled age- and gender-specific sequelae of a COVID-19 infection. In this retrospective electronic health records (EHR) cohort study, we applied a computational framework for knowledge discovery from clinical data, MLHO, to identify phenotypes that positively associate with a past positive reverse transcription-polymerase chain reaction (RT-PCR) test for COVID-19. We evaluated the post-test phenotypes in two temporal windows at 3-6 and 6-9 months after the test and by age and gender. Data from longitudinal diagnosis records stored in EHRs from Mass General Brigham in the Boston metropolitan area was used for the analyses. Statistical analyses were performed on data from March 2020 to June 2021. Study participants included over 96 thousand patients who had tested positive or negative for COVID-19 and were not hospitalized. We identified 33 phenotypes among different age/gender cohorts or time windows that were positively associated with past SARS-CoV-2 infection. All identified phenotypes were newly recorded in patients’ medical records two months or longer after a COVID-19 RT-PCR test in non-hospitalized patients regardless of the test result. Among these phenotypes, a new diagnosis record for anosmia and dysgeusia (OR: 2.60, 95% CI [1.94 - 3.46]), alopecia (OR: 3.09, 95% CI [2.53 - 3.76]), chest pain (OR: 1.27, 95% CI [1.09 - 1.48]), chronic fatigue syndrome (OR 2.60, 95% CI [1.22-2.10]), shortness of breath (OR 1.41, 95% CI [1.22 - 1.64]), pneumonia (OR 1.66, 95% CI [1.28 - 2.16]), and type 2 diabetes mellitus (OR 1.41, 95% CI [1.22 - 1.64]) are some of the most significant indicators of a past COVID-19 infection. Additionally, more new phenotypes were found with increased confidence among the cohorts who were younger than 65. Our approach avoids a flood of false positive discoveries while offering a more robust probabilistic approach compared to the standard linear phenome-wide association study (PheWAS). The findings of this study confirm many of the post-COVID symptoms and suggest that a variety of new diagnoses, including new diabetes mellitus and neurological disorder diagnoses, are more common among those with a history of COVID-19 than those without the infection. Additionally, more than 63 percent of PASC phenotypes were observed in patients under 65 years of age, pointing out the importance of vaccination to minimize the risk of debilitating post-acute sequelae of COVID-19 among younger adults.Competing Interest StatementThe authors have declared no competing interest.Funding StatementThis work was supported by the National Human Genome Research Institute grant 3U01HG008685-05S2 and the National Library of Medicine grant T15LM007092.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:The use of clinical data in this study was approved by the MGB Human Research Committee with a waiver of informed consent.All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesData contains PHI and therefore is not publicly available.
Klann, Jeffrey, Hossein Estiri, Griffin Weber, Bertrand Moal, Paul Avillach, Chuan Hong, Amelia Tan, et al. 2021. “Validation of an Internationally Derived Patient Severity Phenotype to Support COVID-19 Analytics from Electronic Health Record Data”. Journal of the American Medical Informatics Association 28 (7): 1411-20. https://doi.org/10.1093/jamia/ocab018.
The Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing coronavirus disease 2019 (COVID-19) with federated analyses of electronic health record (EHR) data. We sought to develop and validate a computable phenotype for COVID-19 severity.Twelve 4CE sites participated. First, we developed an EHR-based severity phenotype consisting of 6 code classes, and we validated it on patient hospitalization data from the 12 4CE clinical sites against the outcomes of intensive care unit (ICU) admission and/or death. We also piloted an alternative machine learning approach and compared selected predictors of severity with the 4CE phenotype at 1 site.The full 4CE severity phenotype had pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of individual code categories for acuity had high variability—up to 0.65 across sites. At one pilot site, the expert-derived phenotype had mean area under the curve of 0.903 (95\% confidence interval, 0.886-0.921), compared with an area under the curve of 0.956 (95\% confidence interval, 0.952-0.959) for the machine learning approach. Billing codes were poor proxies of ICU admission, with as low as 49\% precision and recall compared with chart review.We developed a severity phenotype using 6 code classes that proved resilient to coding variability across international institutions. In contrast, machine learning approaches may overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold-standard outcomes, possibly owing to heterogeneous pandemic conditions.We developed an EHR-based severity phenotype for COVID-19 in hospitalized patients and validated it at 12 international sites.
Stensland, Kristian, Peter Chang, David Jiang, David Canes, Aaron Berkenwald, Adrian Waisman, Kortney Robinson, et al. 2021. “Reducing Postoperative Opioid Pill Prescribing via a Quality Improvement Approach”. International Journal for Quality in Health Care 33 (3). https://doi.org/10.1093/intqhc/mzab099.
The opioid epidemic has been fueled by prescribing unnecessary quantities of opioid pills for postoperative use. While evidence mounts that postoperative opioids can be reduced or eliminated, implementing such changes within various institutions can be met with many barriers to adoption.To address excess opioid prescribing within our institutions, we applied a plan-do-study-act (PDSA)-like quality improvement strategy to assess local opioid prescribing and use, modify our institutional protocols, and assess the impacts of the change. The opioid epidemic has been fueled by prescribing unnecessary quantities of opioid pills for postoperative use. While evidence mounts that postoperative opioids can be reduced or eliminated, implementing such changes within various institutions can be met with many barriers to adoption. We describe our approach, findings, and lessons learned from our quality improvement approach.We prospectively recorded home pain pill usage after robotic-assisted laparoscopic prostatectomy (RALP) and robotic-assisted partial nephrectomy (RAPN) at two academic institutions from July 2016 to July 2019. Patients prospectively recorded their home pain pill use on a take-home log. Other factors, including numeric pain rating scale on the day of discharge, were extracted from patient records. We analyzed our data and modified opioid prescription protocols to meet the reported use data of 80\% of patients. We continued collecting data after the protocol change. We also used our prospectively collected data to assess the accuracy of a retrospective phone survey designed to measure postdischarge opioid use. Our primary outcomes were the proportion of patients taking zero opioid pills postdischarge, median pills taken after discharge and the number of excess pills prescribed but not taken. We compared these outcomes before and after protocol change.A total of 266 patients (193 RALP, 73 RAPN) were included. Reducing the standard number of prescribed pills did not increase the percentage of patients taking zero pills postdischarge in either group (RALP: 47\% vs. 41\%; RAPN 48\% vs. 34\%). The patients in either group reporting postoperative Day 1 pain score of 0 or 1 were much more likely to use zero postdischarge opioid pills. Our reduction in prescribing protocol resulted in an estimated reduction in excess pills from 1555 excess pills in the prior protocol to just 155 excess pills in the new protocol.Our PDSA-like approach led to an acceptable protocol revision resulting in significant reductions in excess pills released into the community. Reducing the quantity of opioids prescribed postoperatively does not increase the percentage of patients taking zero pills postdischarge. To eliminate opioid use may require no-opioid pathways. Our approach can be used in implementing zero opioid discharge plans and can be applied to opioid reduction interventions at other institutions where barriers to reduced prescribing exist.