National Healthcare Quality and Disparities Report
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Topics
- Access to Care (2)
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- Arthritis (2)
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- COVID-19 (1)
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- Education: Patient and Caregiver (1)
- Electronic Health Records (EHRs) (16)
- Electronic Prescribing (E-Prescribing) (1)
- Evidence-Based Practice (1)
- Healthcare-Associated Infections (HAIs) (9)
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- (-) Health Information Technology (HIT) (50)
- Health Services Research (HSR) (1)
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- Provider: Nurse (2)
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- Risk (5)
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- (-) Surgery (50)
- Telehealth (12)
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- Transitions of Care (1)
- Transplantation (1)
- Urinary Tract Infection (UTI) (1)
- Vaccination (1)
AHRQ Research Studies
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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 25 of 50 Research Studies DisplayedWu J, Yuan CT, Moyal-Smith R
Electronic health record-supported implementation of an evidence-based pathway for perioperative surgical care.
This study examines the role of electronic health records (EHRs) in implementing enhanced recovery pathways (ERPs) for perioperative surgical care. Interviews with informaticians and clinicians from eight US hospitals revealed three thematic clusters: "EHR difficulties," "EHR enablers," and "EHR barriers." Researchers concluded that high performers and improvers successfully integrated ERPs into EHRs with dedicated multidisciplinary teams, while others faced challenges. Early involvement of informatics expertise benefited ERP implementation and sustainability.
AHRQ-funded; 2332015000201.
Citation: Wu J, Yuan CT, Moyal-Smith R .
Electronic health record-supported implementation of an evidence-based pathway for perioperative surgical care.
J Am Med Inform Assoc 2024 Feb 16; 31(3):591-99. doi: 10.1093/jamia/ocad237.
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Surgery, Evidence-Based Practice, Hospitals
Rolfzen ML, Wick A, Mascha EJ
Best Practice Alerts Informed by Inpatient Opioid Intake to Reduce Opioid Prescribing after Surgery (PRIOR): a cluster randomized multiple crossover trial.
This study tested the hypothesis that a decision-support tool embedded in electronic health records (EHRs) leads clinicians to prescribe fewer opioids at discharge after inpatient surgery. Over 21,000 surgical inpatient discharges in a cluster randomized multiple crossover trial in four Colorado hospitals were included. The results indicated that within the context of vigorous opioid education and awareness efforts a decision-support tool incorporated into EHRs did not reduce discharge opioid prescribing for postoperative patients. The authors concluded that opioid prescribing alerts might be valuable in other contexts.
AHRQ-funded; HS027795.
Citation: Rolfzen ML, Wick A, Mascha EJ .
Best Practice Alerts Informed by Inpatient Opioid Intake to Reduce Opioid Prescribing after Surgery (PRIOR): a cluster randomized multiple crossover trial.
Anesthesiology 2023 Aug 1; 139(2):186-96. doi: 10.1097/aln.0000000000004607..
Keywords: Opioids, Medication, Surgery, Inpatient Care, Clinical Decision Support (CDS), Health Information Technology (HIT)
Wissel BD, Greiner HM, Glauser TA
Automated, machine learning-based alerts increase epilepsy surgery referrals: a randomized controlled trial.
Researchers conducted a prospective, randomized controlled trial of a natural language processing-based clinical decision support system in the electronic health record at 14 pediatric neurology outpatient clinics to determine whether automated, electronic alerts increased referrals for epilepsy surgery. Children with epilepsy and at least two prior neurology visits were screened by the system prior to their scheduled visit to identify potential surgical candidates, and the potential candidates randomized 2:1 for their providers to receive an alert or standard of care (no alert). The results showed that patients whose providers received an alert were more likely to be referred for a presurgical evaluation. The researchers concluded that machine learning-based automated alerts may improve the utilization of referrals for epilepsy surgery evaluations.
AHRQ-funded; HS024977.
Citation: Wissel BD, Greiner HM, Glauser TA .
Automated, machine learning-based alerts increase epilepsy surgery referrals: a randomized controlled trial.
Epilepsia 2023 Jul; 64(7):1791-99. doi: 10.1111/epi.17629..
Keywords: Neurological Disorders, Surgery, Health Information Technology (HIT)
Ingraham NE, Jones EK, King S
Re-aiming equity evaluation in clinical decision support: a scoping review of equity assessments in surgical decision support systems.
This scoping review explored surgical literature to determine frequency and rigor of clinical decision support (CDS) equity assessments and offer recommendations to improve CDS equity by appending existing frameworks. The authors performed a scoping review of PubMed and Google Scholar and identified 1,415 citations with 229 abstracts meeting criteria for review. A total of 84 papers underwent full review after 145 were excluded if they did not assess outcomes of an electronic CDS tool or have a surgical use case. Only 6% of surgical CDS systems reported equity analyses, suggesting that current methods for optimizing equity in surgical CDS are inadequate. The authors proposed revising the RE-AIM framework to include an Equity element (RE2-AIM) specifying that CDS foundational analyses and algorithms are performed or trained on balanced datasets with sociodemographic characteristics that accurately represent the CDS target population and are assessed by sensitivity analyses focused on vulnerable subpopulations.
AHRQ-funded; HS026379; HS024532.
Citation: Ingraham NE, Jones EK, King S .
Re-aiming equity evaluation in clinical decision support: a scoping review of equity assessments in surgical decision support systems.
Ann Surg 2023 Mar; 277(3):359-64. doi: 10.1097/sla.0000000000005661..
Keywords: Clinical Decision Support (CDS), Health Information Technology (HIT), Disparities, Surgery
Fritz B, King C, Chen Y
Protocol for the perioperative outcome risk assessment with computer learning enhancement (Periop ORACLE) randomized study.
This paper describes a protocol for an ongoing study that hypothesizes that anesthesiology clinicians can predict postoperative complications more accurately with machine learning assistance than without machine learning assistance. This investigation is a sub-study nested within the TECTONICS randomized clinical trial. Study team members who are anesthesiology clinicians working in a telemedicine setting are currently reviewing ongoing surgical cases and documenting how likely they feel the patient is to experience 30-day in-hospital death or acute kidney injury. These case reviews will be randomized to be performed with access to a display showing machine learning predictions for the postoperative complications or without access to the display, and the accuracy of the predictions will be compared across these two groups.
AHRQ-funded; HS024581.
Citation: Fritz B, King C, Chen Y .
Protocol for the perioperative outcome risk assessment with computer learning enhancement (Periop ORACLE) randomized study.
F1000Res 2022; 11:653. doi: 10.12688/f1000research.122286.2..
Keywords: Surgery, Risk, Outcomes, Health Information Technology (HIT)
Nanji KC, Garabedian PM, Langlieb ME
Usability of a perioperative medication-related clinical decision support software application: a randomized controlled trial.
The purpose of this study was assess the usability of a newly developed, comprehensive, medication-related operating room clinical decision support (CDS) software and compare it with the standard electronic health record (EHR) medication workflow. Forty participants were randomized to a CDS group (n=20) or a control group (n=20) and asked to complete 7 simulation tasks. The study found that in a simulation setting the new CDS software improved efficiency and quality of care and reduced task time, excelling over the current EHR workflow.
AHRQ-funded; HS024764.
Citation: Nanji KC, Garabedian PM, Langlieb ME .
Usability of a perioperative medication-related clinical decision support software application: a randomized controlled trial.
J Am Med Inform Assoc 2022 Jul 12;29(8):1416-24. doi: 10.1093/jamia/ocac035..
Keywords: Medication, Clinical Decision Support (CDS), Health Information Technology (HIT), Surgery, Shared Decision Making
Skube SJ, Hu Z, Simon GJ
Accelerating surgical site infection abstraction with a semi-automated machine-learning approach.
The purpose of this study was to test a supervised machine learning algorithm developed for testing surgical site infection (SSI) on performing semi-automated SSI abstraction, and to demonstrate that a semi-automated approach to health data abstraction provides a high level of accuracy and significant efficiencies. The researchers evaluated data from 6,188 patients in a 2011-2013 dataset and 5,132 patients in a 2015-2015 dataset. The study concluded that very good performance is achieved using the semi-automated machine learning-aided SSI abstraction, which also accelerates the abstraction process.
AHRQ-funded; HS024532.
Citation: Skube SJ, Hu Z, Simon GJ .
Accelerating surgical site infection abstraction with a semi-automated machine-learning approach.
Ann Surg 2022 Jul 1;276(1):180-85. doi: 10.1097/sla.0000000000004354..
Keywords: Healthcare-Associated Infections (HAIs), Surgery, Health Information Technology (HIT)
Abraham J, Meng A, Holzer KJ
Exploring patient perspectives on telemedicine monitoring within the operating room.
The authors sought to identify participant-rated items contributing to patient attitudes, beliefs, and level of comfort with electronic OR (eOR) monitoring and to highlight barriers and facilitators to eOR use. They found that participants expressed significant support for intraoperative telemedicine use and greater comfort with local telemedicine systems instead of long-distance telemedicine systems. They further found that reservations centered on organizational policies, procedures, environment, culture; people; workflow and communication; and hardware and software.
Citation: Abraham J, Meng A, Holzer KJ .
Exploring patient perspectives on telemedicine monitoring within the operating room.
Int J Med Inform 2021 Dec;156:104595. doi: 10.1016/j.ijmedinf.2021.104595..
Keywords: Telehealth, Health Information Technology (HIT), Surgery, Patient Experience
Shi J, Hurdle JF, Johnson SA
Natural language processing for the surveillance of postoperative venous thromboembolism.
The objective of the study was to develop a portal natural language processing approach to aid in the identification of postoperative venous thromboembolism events from free-text clinical notes. The investigators concluded that accurate surveillance of postoperative venous thromboembolism may be achieved using natural language processing on clinical notes in 2 independent health care systems. They indicated that these findings suggest natural language processing may augment manual chart abstraction for large registries such as National Surgical Quality Improvement Program.
AHRQ-funded; HS025776.
Citation: Shi J, Hurdle JF, Johnson SA .
Natural language processing for the surveillance of postoperative venous thromboembolism.
Surgery 2021 Oct;170(4):1175-82. doi: 10.1016/j.surg.2021.04.027..
Keywords: Blood Clots, Health Information Technology (HIT), Quality Improvement, Quality of Care, Surgery, Adverse Events
Wissel BD, Greiner HM, Glauser TA
Early identification of epilepsy surgery candidates: a multicenter, machine learning study.
Epilepsy surgery is underutilized. Automating the identification of potential surgical candidates may facilitate earlier intervention. The study objective was to develop site-specific machine learning (ML) algorithms to identify candidates before they undergo surgery. The investigators concluded that site-specific machine learning algorithms could identify candidates for epilepsy surgery early in the disease course in diverse practice settings.
AHRQ-funded; HS024977.
Citation: Wissel BD, Greiner HM, Glauser TA .
Early identification of epilepsy surgery candidates: a multicenter, machine learning study.
Acta Neurol Scand 2021 Jul;114(1):41-50. doi: 10.1111/ane.13418..
Keywords: Neurological Disorders, Surgery, Health Information Technology (HIT)
Kutney-Lee A, Brooks Carthon M, Sloane DM
Electronic health record usability: associations with nurse and patient outcomes in hospitals.
Researchers examined associations between electronic health record (EHR) usability and nurse job and surgical patient outcomes. Data from the American Hospital Association, state patient discharges, and nurse surveys were linked in a cross-sectional analysis. The researchers found that employing EHR systems with suboptimal usability was associated with higher odds of adverse nurse job outcomes and surgical patient mortality and readmission.
AHRQ-funded; HS023805.
Citation: Kutney-Lee A, Brooks Carthon M, Sloane DM .
Electronic health record usability: associations with nurse and patient outcomes in hospitals.
Med Care 2021 Jul;59(7):625-31. doi: 10.1097/mlr.0000000000001536..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Surgery, Outcomes, Nursing, Provider: Nurse
Chao GF, Li KY, Zhu Z
Use of telehealth by surgical specialties during the COVID-19 pandemic.
This study’s objective was to determine telehealth use by surgical specialty before and during the pandemic period starting in March 2020. Insurance claims from a Michigan statewide commercial payer for new patient visits with a surgeon from 1 of 9 surgical specialties during one of the following periods: prior to the COVID-19 pandemic (period 1: January 5 to March 7, 2020), early pandemic (period 2: March 8 to June 6, 2020), and late pandemic (period 3: June 7 to September 5, 2020) were analyzed. For new patient visits, 1182 surgeons (26.8%) in any patient context used telehealth. Telehealth use peaked in April 2020 and facilitated 34.6% of all new patient visits during that week. Urology was the specialty with the highest telehealth conversion rate (14.3%).
AHRQ-funded; HS027632.
Citation: Chao GF, Li KY, Zhu Z .
Use of telehealth by surgical specialties during the COVID-19 pandemic.
JAMA Surg 2021 Jul;156(7):620-26. doi: 10.1001/jamasurg.2021.0979..
Keywords: COVID-19, Telehealth, Health Information Technology (HIT), Access to Care, Practice Patterns, Surgery
Zhu Y, Simon GJ, Wick EC
Applying machine learning across sites: external validation of a surgical site infection detection algorithm.
Surgical complications have tremendous consequences and costs. Complication detection is important for quality improvement, but traditional manual chart review is burdensome. Automated mechanisms are needed to make this more efficient. The purpose of the study was to understand the generalizability of a machine learning algorithm between sites; automated surgical site infection (SSI) detection algorithms developed at one center were tested at another distinct center.
AHRQ-funded; HS024532.
Citation: Zhu Y, Simon GJ, Wick EC .
Applying machine learning across sites: external validation of a surgical site infection detection algorithm.
J Am Coll Surg 2021 Jun;232(6):963-71.e1. doi: 10.1016/j.jamcollsurg.2021.03.026..
Keywords: Healthcare-Associated Infections (HAIs), Surgery, Adverse Events, Diagnostic Safety and Quality, Electronic Health Records (EHRs), Health Information Technology (HIT), Quality Improvement, Quality of Care
Kemp MT, Williams AM, Brown CS
Practical guidance for early identification of barriers in surgical telehealth clinics.
The authors provide advice on early identification of and response to barriers in telehealth settings in order to help patients receive optimal care. Their focus is on standardizing expectations, assessing technological knowledge and resource access, evaluating understanding and comfort with telehealth, and assessing social support.
AHRQ-funded; HS000053.
Citation: Kemp MT, Williams AM, Brown CS .
Practical guidance for early identification of barriers in surgical telehealth clinics.
Ann Surg 2021 Jun;273(6):e268-e70. doi: 10.1097/sla.0000000000004633..
Keywords: Surgery, Telehealth, Health Information Technology (HIT), Healthcare Delivery, Access to Care
Bucher BT, Shi J, Ferraro JP
Portable automated surveillance of surgical site infections using natural language processing: development and validation.
The authors presented the development and validation of a portable natural language processing (NLP) approach for automated surveillance of surgical site infections (SSIs). Patient clinical text notes from EHRs following surgical procedures from two independent healthcare systems were abstracted. The authors found that automated surveillance of SSIs can be achieved using NLP of clinical notes with high sensitivity and specificity.
AHRQ-funded; HS025776.
Citation: Bucher BT, Shi J, Ferraro JP .
Portable automated surveillance of surgical site infections using natural language processing: development and validation.
Ann Surg 2020 Oct;272(4):629-36. doi: 10.1097/sla.0000000000004133..
Keywords: Surgery, Healthcare-Associated Infections (HAIs), Electronic Health Records (EHRs), Health Information Technology (HIT), Quality Improvement, Quality of Care
Giardina JC, Cha T, Atlas SJ
Validation of an electronic coding algorithm to identify the primary indication of orthopedic surgeries from administrative data.
The purpose of this study was to develop and validate an algorithm to identify patients receiving four elective orthopedic surgeries to promote shared decision-making. The surgeries included were: 1) knee arthroplasty to treat knee osteoarthritis (KOA); 2) hip arthroplasty to treat hip osteoarthritis (HOA); 3) spinal surgery to treat lumbar spinal stenosis (SpS); and 4) spinal surgery to treat lumber herniated disc (HD). Electronic medical records were reviewed to ascertain a “gold standard” determination of the procedure and primary indication status. Each case had electronic algorithms consisting of ICD-10 and CPT codes for each combination and indication applied to their record. A total of 790 procedures were included in the study. The sensitivity of the algorithms ranged from 0.70 (HD) to 0.92 (KOA). Specificity ranged from 0.94 (SpS) to 0.99 (HOA, KOA).
AHRQ-funded; HS000055.
Citation: Giardina JC, Cha T, Atlas SJ .
Validation of an electronic coding algorithm to identify the primary indication of orthopedic surgeries from administrative data.
BMC Med Inform Decis Mak 2020 Aug 12;20(1):187. doi: 10.1186/s12911-020-01175-1.
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Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Orthopedics, Surgery, Arthritis, Shared Decision Making
Bronsert M, Singh AB, Henderson WG
Identification of postoperative complications using electronic health record data and machine learning.
Investigators developed a machine learning algorithm for identifying patients with one or more complications using data from the electronic health record (EHR). They concluded that using machine learning on EHR postoperative data linked to American College of Surgeons National Surgical Quality Improvement Program outcomes data, a model with 163 predictors from the EHR identified complications well at their institution.
AHRQ-funded; HS026019.
Citation: Bronsert M, Singh AB, Henderson WG .
Identification of postoperative complications using electronic health record data and machine learning.
Am J Surg 2020 Jul;220(1):114-19. doi: 10.1016/j.amjsurg.2019.10.009..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Surgery, Quality Improvement, Quality of Care, Diagnostic Safety and Quality
Weng Y, Tian L, Tedesco D
Trajectory analysis for postoperative pain using electronic health records: a nonparametric method with robust linear regression and K-medians cluster analysis.
Postoperative pain scores are widely monitored and collected in the electronic health record, yet current methods fail to fully leverage the data with fast implementation. This article describes a trajectory analysis for postoperative pain using electronic health records. A robust linear regression was fitted to describe the association between the log-scaled pain score and time from discharge after total knee replacement.
AHRQ-funded; HS024096.
Citation: Weng Y, Tian L, Tedesco D .
Trajectory analysis for postoperative pain using electronic health records: a nonparametric method with robust linear regression and K-medians cluster analysis.
Health Informatics J 2020 Jun;26(2):1404-18. doi: 10.1177/1460458219881339..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Pain, Surgery, Orthopedics, Research Methodologies, Health Services Research (HSR)
Wilson N, Jehn M, Kisana H
Nurses' perceptions of implant barcode scanning in surgical services.
Health policy changes have prompted hospital systems to assess implementation of implant barcode scanning systems to capture unique device identifiers. The aims of this project were to assess predictors of operating room nurses' acceptance of a new implant barcode scanning system, describe operating room nurses' perceptions of the system value, and identify operating room nurses' perceived gaps in system implementation.
AHRQ-funded; HS022340.
Citation: Wilson N, Jehn M, Kisana H .
Nurses' perceptions of implant barcode scanning in surgical services.
Comput Inform Nurs 2020 Mar;38(3):131-38. doi: 10.1097/cin.0000000000000579..
Keywords: Provider: Nurse, Provider, Surgery, Health Information Technology (HIT)
Wissel BD, Greiner TA, Holland-Bouley KD
Prospective validation of a machine learning model that uses provider notes to identify candidates for resective epilepsy surgery.
Delay to resective epilepsy surgery results in avoidable disease burden and increased risk of mortality. The objective of this study was to prospectively validate a natural language processing (NLP) application that uses provider notes to assign epilepsy surgery candidacy scores. The authors suggest that an electronic health record-integrated NLP application can accurately assign surgical candidacy scores to patients in a clinical setting.
AHRQ-funded; HS024977.
Citation: Wissel BD, Greiner TA, Holland-Bouley KD .
Prospective validation of a machine learning model that uses provider notes to identify candidates for resective epilepsy surgery.
Epilepsia 2020 Jan;61(1):39-48. doi: 10.1111/epi.16398..
Keywords: Neurological Disorders, Surgery, Health Information Technology (HIT), Clinical Decision Support (CDS), Shared Decision Making
Feldman AG, Atkinson K, Wilson K
Underimmunization of the solid organ transplant population: An urgent problem with potential digital health solutions.
This paper describes ways that digital health technologies may help solid organ transplant recipients stay free from vaccine-preventable infections so they are not underimmunized at the time of transplant and thereafter. Due to vaccine hesitancy and refusal in the general population, recipients can no longer rely on herd immunity to protect them. Digital health technologies can provide accurate information about vaccine safety, efficacy and timing in the pre- and post-transplant periods; make complete immunization records universally available and easily accessible; enable communication between patients and multiple providers; and provide automated vaccine reminders to both patients and providers.
AHRQ-funded; HS026510.
Citation: Feldman AG, Atkinson K, Wilson K .
Underimmunization of the solid organ transplant population: An urgent problem with potential digital health solutions.
Am J Transplant 2020 Jan;20(1):34-39. doi: 10.1111/ajt.15605..
Keywords: Transplantation, Surgery, Healthcare Utilization, Infectious Diseases, Telehealth, Health Information Technology (HIT), Vaccination
Dolan PT, Afaneh C, Dakin G
Lessons learned from developing a mobile app to assist in patient recovery after weight loss surgery.
This study examines the outcomes of patients recovering from weight loss surgery using a newly developed mobile app to help them recover successfully. The enrolled patients used the app for 30 days from July 2017 to October 2018. As the app was being used, it was updated. Ten patients were enrolled in the trial period with four using the initial version and six with the updated version. All patients were satisfied with the app and liked the notifications of updates. In the trial version only one patient completed at least 70% of the surveys, but five completed the surveys for the updated version. Next steps for the researchers is to conduct a pilot study with a larger set of patients.
AHRQ-funded; HS000066.
Citation: Dolan PT, Afaneh C, Dakin G .
Lessons learned from developing a mobile app to assist in patient recovery after weight loss surgery.
J Surg Res 2019 Dec;244:402-08. doi: 10.1016/j.jss.2019.06.063..
Keywords: Obesity: Weight Management, Obesity, Surgery, Telehealth, Health Information Technology (HIT)
King CR, Abraham J, Kannampallil TG
Protocol for the effectiveness of an anesthesiology control tower system in improving perioperative quality metrics and clinical outcomes: the TECTONICS randomized, pragmatic trial.
The primary objective of this trial was to determine whether an anesthesiology control tower (ACT) prevents clinically relevant adverse postoperative outcomes including 30-day mortality, delirium, respiratory failure, and acute kidney injury. Clinicians in operating rooms randomized to ACT support receive decision support from clinicians in the ACT. In operating rooms randomized to no intervention, the current standard of anesthesia care is delivered. The intention-to-treat principle will be followed for all analyses.
AHRQ-funded; HS024581.
Citation: King CR, Abraham J, Kannampallil TG .
Protocol for the effectiveness of an anesthesiology control tower system in improving perioperative quality metrics and clinical outcomes: the TECTONICS randomized, pragmatic trial.
F1000Res 2019 Nov 29;8:2032. doi: 10.12688/f1000research.21016.1.
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Keywords: Quality Measures, Quality Improvement, Quality of Care, Surgery, Telehealth, Health Information Technology (HIT)
Fritz BA, Cui Z, Zhang M
Deep-learning model for predicting 30-day postoperative mortality.
The currently available prediction tools using summaries of intraoperative data are limited by their inability to reflect shifting risk associated with intraoperative physiological perturbations. In this study the investigators sought to compare similar benchmarks to a deep-learning algorithm predicting postoperative 30-day mortality. They concluded that a deep-learning time-series model improved prediction compared with models with simple summaries of intraoperative data.
AHRQ-funded; HS024581.
Citation: Fritz BA, Cui Z, Zhang M .
Deep-learning model for predicting 30-day postoperative mortality.
Br J Anaesth 2019 Nov;123(5):688-95. doi: 10.1016/j.bja.2019.07.025..
Keywords: Adverse Events, Health Information Technology (HIT), Mortality, Risk, Surgery
Smith AB, Mueller D, Garren B
Using qualitative research to reduce readmissions and optimize perioperative cystectomy care.
This study examined the need for qualitative research on meaningful patient-reported outcomes (PROs) to prevent complications and readmissions after cystectomy. The investigators looked at the potential use of mobile communication devices (mHealth) to capture patients’ experiences and to improve outcomes. Interviews were conducted with 15 readmitted patients and 10 of their partners over 45 semi-structured in-depth interviews. The most common perspectives were that patients and their caregivers were overloaded with cystectomy education; they need to know what are normal post-operative symptoms; and that using mHealth would help with patient and caregiver education.
AHRQ-funded; HS024134.
Citation: Smith AB, Mueller D, Garren B .
Using qualitative research to reduce readmissions and optimize perioperative cystectomy care.
Cancer 2019 Oct 15;125(20):3545-53. doi: 10.1002/cncr.32362..
Keywords: Hospital Readmissions, Surgery, Health Information Technology (HIT), Quality Improvement, Quality of Care, Hospitals, Patient-Centered Healthcare