Digital Orthopaedics: A Glimpse Into the Future in the Midst of a Pandemic.

BackgroundThe response to COVID-19 catalyzed the adoption and integration of digital health tools into the health care delivery model for musculoskeletal patients. The change, suspension, or relaxation of Medicare and federal guidelines enabled the rapid implementation of these technologies. The expansion of payment models for virtual care facilitated its rapid adoption. The authors aim to provide several examples of digital health solutions utilized to manage orthopedic patients during the pandemic and discuss what features of these technologies are likely to continue to provide value to patients and clinicians following its resolution.ConclusionThe widespread adoption of new technologies enabling providers to care for patients remotely has the potential to permanently change the expectations of all stakeholders about the way care is provided in orthopedics. The new era of Digital Orthopaedics will see a gradual and nondisruptive integration of technologies that support the patient's journey through the successful management of their musculoskeletal disease

Equivalent Mid-Term Results of Open vs Endoscopic Gluteal Tendon Tear Repair Using Suture Anchors in Forty-Five Patients.

BackgroundLittle is known about the relative efficacy of open (OGR) vs endoscopic (EGR) gluteal tendon repair of gluteal tendon tears in minimizing pain and restoring function. Our aim is to compare these 2 surgical techniques and quantify their impact on clinical outcomes.MethodsAll patients undergoing gluteal tendon tear repair at our institution between 2015 and 2018 were retrospectively reviewed. Pain scores, limp, hip abduction strength, and the use of analgesics were recorded preoperatively and at last follow-up. The Hip disability and Osteoarthritis Outcome Score Junior and Harris Hip Score Section1 were obtained at last follow-up. Fatty degeneration was quantified using the Goutallier-Fuchs Classification (GFC). Statistical analysis was conducted using one-way analysis of variance and t-tests.ResultsForty-five patients (mean age 66, 87% females) met inclusion criteria. Average follow-up was 20.3 months. None of the 10 patients (22%) undergoing EGR had prior surgery. Of 35 patients (78%) undergoing OGR, 12 (27%) had prior hip replacement (75% via lateral approach). The OGRs had more patients with GFC ≥2 (50% vs 11%, P = .02) and used more anchors (P = .03). Both groups showed statistical improvement (P ≤ .01) for all outcomes measured. GFC >2 was independently associated with a worst limp and Harris Hip Score Section 1 score (P = .05). EGR had a statistically higher opioid use reduction (P < .05) than OGR. Other comparisons between EGR and OGR did not reach statistical significance.ConclusionIn this series, open vs endoscopic operative approach did not impact clinical outcomes. More complex tears were treated open and with more anchors. Fatty degeneration adversely impacted outcomes. Although further evaluation of the efficacy of EGR in complex tears is indicated, both approaches can be used successfully

Preoperative predictions of in-hospital mortality using electronic medical record data

Background: Predicting preoperative in-hospital mortality using readily-available electronic medical record (EMR) data can aid clinicians in accurately and rapidly determining surgical risk. While previous work has shown that the American Society of Anesthesiologists (ASA) Physical Status Classification is a useful, though subjective, feature for predicting surgical outcomes, obtaining this classification requires a clinician to review the patient's medical records. Our goal here is to create an improved risk score using electronic medical records and demonstrate its utility in predicting in-hospital mortality without requiring clinician-derived ASA scores. Methods: Data from 49,513 surgical patients were used to train logistic regression, random forest, and gradient boosted tree classifiers for predicting in-hospital mortality. The features used are readily available before surgery from EMR databases. A gradient boosted tree regression model was trained to impute the ASA Physical Status Classification, and this new, imputed score was included as an additional feature to preoperatively predict in-hospital post-surgical mortality. The preoperative risk prediction was then used as an input feature to a deep neural network (DNN), along with intraoperative features, to predict postoperative in-hospital mortality risk. Performance was measured using the area under the receiver operating characteristic (ROC) curve (AUC). Results: We found that the random forest classifier (AUC 0.921, 95%CI 0.908-0.934) outperforms logistic regression (AUC 0.871, 95%CI 0.841-0.900) and gradient boosted trees (AUC 0.897, 95%CI 0.881-0.912) in predicting in-hospital post-surgical mortality. Using logistic regression, the ASA Physical Status Classification score alone had an AUC of 0.865 (95%CI 0.848-0.882). Adding preoperative features to the ASA Physical Status Classification improved the random forest AUC to 0.929 (95%CI 0.915-0.943). Using only automatically obtained preoperative features with no clinician intervention, we found that the random forest model achieved an AUC of 0.921 (95%CI 0.908-0.934). Integrating the preoperative risk prediction into the DNN for postoperative risk prediction results in an AUC of 0.924 (95%CI 0.905-0.941), and with both a preoperative and postoperative risk score for each patient, we were able to show that the mortality risk changes over time. Conclusions: Features easily extracted from EMR data can be used to preoperatively predict the risk of in-hospital post-surgical mortality in a fully automated fashion, with accuracy comparable to models trained on features that require clinical expertise. This preoperative risk score can then be compared to the postoperative risk score to show that the risk changes, and therefore should be monitored longitudinally over time

Postoperative Remote Automated Monitoring:Need for and State of the Science

Worldwide, more than 230 million adults have major noncardiac surgery each year. Although surgery can improve quality and duration of life, it can also precipitate major complications. Moreover, a substantial proportion of deaths occur after discharge. Current systems for monitoring patients postoperatively, on surgical wards and after transition to home, are inadequate. On the surgical ward, vital signs evaluation usually occurs only every 4-8 hours. Reduced in-hospital ward monitoring, followed by no vital signs monitoring at home, leads to thousands of cases of undetected/delayed detection of hemodynamic compromise. In this article we review work to date on postoperative remote automated monitoring on surgical wards and strategy for advancing this field. Key considerations for overcoming current barriers to implementing remote automated monitoring in Canada are also presented

A Root-Cause Analysis of Mortality Following Major Pancreatectomy

Abstract Introduction Although mortality rates from pancreatectomy have decreased worldwide, death remains an infrequent but profound event at an individual practice level. Root-cause analysis is a retrospective method commonly employed to understand adverse events. We evaluate whether emerging mortality risk assessment tools sufficiently predict and account for actual clinical events that are often identified by root-cause analysis. Methods We assembled a Pancreatic Surgery Mortality Study Group comprised of 36 pancreatic surgeons from 15 institutions in 4 countries. Mortalities after pancreatectomy (30 and 90 days) were accrued from 2000 to 2010. For root-cause analysis, each surgeon "deconstructed" the clinical events preceding a death to determine cause. We next tested whether mortality risk assessment tools (ASA, POSSUM, Charlson, SOAR, and NSQIP) could predict those patients who would die (n=218) and compared their prognostic accuracy against a cohort of resections in which no patient died (n=1,177). Results Two hundred eighteen deaths (184 Whipple's resection, 18 distal pancreatectomies, and 16 total pancreatectomies) were identified from 11,559 pancreatectomies performed by surgeons whose experience averaged 14.5 years. Overall 30-and 90-day mortalities were 0.96% and 1.89%, respectively. Individual surgeon rates ranged from 0% to 4.7%. Only 5 patients died intraoperatively, while the other 213 succumbed at a median of 29 days. Mean patient age was 70 years old (38% were >75 years old). Malignancy was the indication in 90% of cases, mostly pancreatic cancer (57%). Median operative time was 365 min and estimated blood loss was 700 cc (range, 100-16,000 cc). Vascular repair or multivisceral resections were required for 19.7% and 15.1%, respectively. Seventy-seven percent had a variety of major complications before death. Eighty-seven percent required intensive care unit care, 55% were transfused, and 35% were reoperated upon. Fifty percent died during the index admission, while another 11% died after a readmission. Almost half (n=107) expired between 31 and 90 days. Only 11% had autopsies. Operation-related complications contributed to 40% of deaths, with pancreatic fistula being the most evident (14%). Technical errors (21%) and poor patient selection (15%) were cited by surgeons. Of deaths, 5.5% had associated cancer progression-all occurring between 31 and 90 days. Even after root-cause scrutiny, the ultimate cause of death could not be determined for a quarter of the patients-most often between 31 and 90 days. While assorted risk models predicted mortality with variable discrimination from nonmortalities, they consistently underestimated the actual mortality events we report. Conclusion Root-cause analysis suggests that risk prediction should include, if not emphasize, operative factors related to pancreatectomy. While risk models can distinguish between mortalities and nonmortalities in a collective fashion, they vastly miscalculate the actual chance of death on an individual basis. This study reveals the contributions of both comorbidities and aggressive surgical decisions to mortality

Optimising cardiac services using routinely collected data and discrete event simulation

Background: The current practice of managing hospital resources, including beds, is very much driven by measuring past or expected utilisation of resources. This practice, however, doesn’t reflect variability among patients. Consequently, managers and clinicians cannot make fully informed decisions based upon these measures which are considered inadequate in planning and managing complex systems. Aim: to analyse how variation related to patient conditions and adverse events affect resource utilisation and operational performance. Methods: Data pertaining to cardiac patients (cardiothoracic and cardiology, n=2241) were collected from two major hospitals in Oman. Factors influential to resource utilisation were assessed using logistic regressions. Other analysis related to classifying patients based on their resource utilisation was carried out using decision tree to assist in predicting hospital stay. Finally, discrete event simulation modelling was used to evaluate how patient factors and postoperative complications are affecting operational performance. Results: 26.5% of the patients experienced prolonged Length of Stay (LOS) in intensive care units and 30% in the ward. Patients with prolonged postoperative LOS had 60% of the total patient days. Some of the factors that explained the largest amount of variance in resource use following cardiac procedure included body mass index, type of surgery, Cardiopulmonary Bypass (CPB) use, non-elective surgery, number of complications, blood transfusion, chronic heart failure, and previous angioplasty. Allocating resources based on patient expected LOS has resulted in a reduction of surgery cancellations and waiting times while overall throughput has increased. Complications had a significant effect on perioperative operational performance such as surgery cancellations. The effect was profound when complications occurred in the intensive care unit where a limited capacity was observed. Based on the simulation model, eliminating some complications can enlarge patient population. Conclusion: Integrating influential factors into resource planning through simulation modelling is an effective way to estimate and manage hospital capacity.Open Acces

Application of Systems Engineering Science to the Healthcare Environment

This Doctoral dissertation consists of a research portfolio examining the application of systems engineering techniques to the healthcare environment. The portfolio consists of three final publishable articles submitted to meet the program requirements for the, Doctor of Philosophy in Nursing degree from the University of San Diego, Hahn school of Nursing and Health Sciences. Article one is titled; Use of a bed projection tool to predict ICU bed needs. This article describes the dissertation research study in which a bed projection tool was piloted on an ICU unit to determine the tool\u27s ability to predict inpatient bed requirements. Article 2 is titled; Reducing Disruptive Communication in the Health Care Setting: Use of the Crew Resource Model (CRM). Crew resource is a human factor-engineering model that creates uniform team roles and communication structure. This article advocates the use of this model to assist in dealing with disruptive behaviors by healthcare team professionals. The article advocates the use of the CRM model for meeting the Joint Commission on Hospital Accreditation requirement for organization\u27s in which a plan is implemented for dealing with disruptive communication in the health care environment (by health care team professionals). Article 3 is titled; Application of systems engineering to the hospital environment; has the time for a Nurse Engineer role arrived? This article describes the evolution of systems engineering as a discipline and its historical application. The article stresses the need for Nurses to acquire an engineering skill set in order to participate in the redesign of clinical health systems, which will ensure efficiency and patient safety