Study protocol for the Anesthesiology Control Tower-Feedback Alerts to Supplement Treatments (ACTFAST-3) trial: a pilot randomized controlled trial in intraoperative telemedicine

User-Centered Design of a Machine Learning Dashboard for Prediction of Postoperative Complications

BACKGROUND

Machine learning models can help anesthesiology clinicians assess patients and make clinical and operational decisions, but well-designed human-computer interfaces are necessary for machine learning model predictions to result in clinician actions that help patients. Therefore, the goal of this study was to apply a user-centered design framework to create a user interface for displaying machine learning model predictions of postoperative complications to anesthesiology clinicians.

METHODS

Twenty-five anesthesiology clinicians (attending anesthesiologists, resident physicians, and certified registered nurse anesthetists) participated in a 3-phase study that included (phase 1) semistructured focus group interviews and a card sorting activity to characterize user workflows and needs; (phase 2) simulated patient evaluation incorporating a low-fidelity static prototype display interface followed by a semistructured interview; and (phase 3) simulated patient evaluation with concurrent think-aloud incorporating a high-fidelity prototype display interface in the electronic health record. In each phase, data analysis included open coding of session transcripts and thematic analysis.

RESULTS

During the needs assessment phase (phase 1), participants voiced that (a) identifying preventable risk related to modifiable risk factors is more important than nonpreventable risk, (b) comprehensive patient evaluation follows a systematic approach that relies heavily on the electronic health record, and (c) an easy-to-use display interface should have a simple layout that uses color and graphs to minimize time and energy spent reading it. When performing simulations using the low-fidelity prototype (phase 2), participants reported that (a) the machine learning predictions helped them to evaluate patient risk, (b) additional information about how to act on the risk estimate would be useful, and (c) correctable problems related to textual content existed. When performing simulations using the high-fidelity prototype (phase 3), usability problems predominantly related to the presentation of information and functionality. Despite the usability problems, participants rated the system highly on the System Usability Scale (mean score, 82.5; standard deviation, 10.5).

CONCLUSIONS

Incorporating user needs and preferences into the design of a machine learning dashboard results in a display interface that clinicians rate as highly usable. Because the system demonstrates usability, evaluation of the effects of implementation on both process and clinical outcomes is warranted.

Remote Monitoring and Artificial Intelligence: Outlook for 2050

Remote monitoring and artificial intelligence will become common and intertwined in anesthesiology by 2050. In the intraoperative period, technology will lead to the development of integrated monitoring systems that will integrate multiple data streams and allow anesthesiologists to track patients more effectively. This will free up anesthesiologists to focus on more complex tasks, such as managing risk and making value-based decisions. This will also enable the continued integration of remote monitoring and control towers having profound effects on coverage and practice models. In the PACU and ICU, the technology will lead to the development of early warning systems that can identify patients who are at risk of complications, enabling early interventions and more proactive care. The integration of augmented reality will allow for better integration of diverse types of data and better decision-making. Postoperatively, the proliferation of wearable devices that can monitor patient vital signs and track their progress will allow patients to be discharged from the hospital sooner and receive care at home. This will require increased use of telemedicine, which will allow patients to consult with doctors remotely. All of these advances will require changes to legal and regulatory frameworks that will enable new workflows that are different from those familiar to today's providers.

An ethnographic study on the impact of a novel telemedicine-based support system in the operating room

OBJECTIVE

The Anesthesiology Control Tower (ACT) for operating rooms (ORs) remotely assesses the progress of surgeries and provides real-time perioperative risk alerts, communicating risk mitigation recommendations to bedside clinicians. We aim to identify and map ACT-OR nonroutine events (NREs)-risk-inducing or risk-mitigating workflow deviations-and ascertain ACT's impact on clinical workflow and patient safety.

MATERIALS AND METHODS

We used ethnographic methods including shadowing ACT and OR clinicians during 83 surgeries, artifact collection, chart reviews for decision alerts sent to the OR, and 10 clinician interviews. We used hybrid thematic analysis informed by a human-factors systems-oriented approach to assess ACT's role and impact on safety, conducting content analysis to assess NREs.

RESULTS

Across 83 cases, 469 risk alerts were triggered, and the ACT sent 280 care recommendations to the OR. 135 NREs were observed. Critical factors facilitating ACT's role in supporting patient safety included providing backup support and offering a fresh-eye perspective on OR decisions. Factors impeding ACT included message timing and ACT and OR clinician cognitive lapses. Suggestions for improvement included tailoring ACT message content (structure, timing, presentation) and incorporating predictive analytics for advanced planning.

DISCUSSION

ACT served as a safety net with remote surveillance features and as a learning healthcare system with feedback/auditing features. Supporting strategies include adaptive coordination and harnessing clinician/patient support to improve ACT's sustainability. Study insights inform future intraoperative telemedicine design considerations to mitigate safety risks.

CONCLUSION

Incorporating similar remote technology enhancement into routine perioperative care could markedly improve safety and quality for millions of surgical patients.

Non-pharmacological interventions for preventing delirium in hospitalised non-ICU patients

BACKGROUND

Delirium is an acute neuropsychological disorder that is common in hospitalised patients. It can be distressing to patients and carers and it is associated with serious adverse outcomes. Treatment options for established delirium are limited and so prevention of delirium is desirable. Non-pharmacological interventions are thought to be important in delirium prevention.  OBJECTIVES: To assess the effectiveness of non-pharmacological interventions designed to prevent delirium in hospitalised patients outside intensive care units (ICU).

SEARCH METHODS

We searched ALOIS, the specialised register of the Cochrane Dementia and Cognitive Improvement Group, with additional searches conducted in MEDLINE, Embase, PsycINFO, CINAHL, LILACS, Web of Science Core Collection, ClinicalTrials.gov and the World Health Organization Portal/ICTRP to 16 September 2020. There were no language or date restrictions applied to the electronic searches, and no methodological filters were used to restrict the search.

SELECTION CRITERIA

We included randomised controlled trials (RCTs) of single and multicomponent non-pharmacological interventions for preventing delirium in hospitalised adults cared for outside intensive care or high dependency settings. We only included non-pharmacological interventions which were designed and implemented to prevent delirium.  DATA COLLECTION AND ANALYSIS: Two review authors independently examined titles and abstracts identified by the search for eligibility and extracted data from full-text articles. Any disagreements on eligibility and inclusion were resolved by consensus. We used standard Cochrane methodological procedures. The primary outcomes were: incidence of delirium; inpatient and later mortality; and new diagnosis of dementia. We included secondary and adverse outcomes as pre-specified in the review protocol. We used risk ratios (RRs) as measures of treatment effect for dichotomous outcomes and between-group mean differences for continuous outcomes. The certainty of the evidence was assessed using GRADE. A complementary exploratory analysis was undertaker using a Bayesian component network meta-analysis fixed-effect model to evaluate the comparative effectiveness of the individual components of multicomponent interventions and describe which components were most strongly associated with reducing the incidence of delirium.

MAIN RESULTS

We included 22 RCTs that recruited a total of 5718 adult participants. Fourteen trials compared a multicomponent delirium prevention intervention with usual care. Two trials compared liberal and restrictive blood transfusion thresholds. The remaining six trials each investigated a different non-pharmacological intervention. Incidence of delirium was reported in all studies.  Using the Cochrane risk of bias tool, we identified risks of bias in all included trials. All were at high risk of performance bias as participants and personnel were not blinded to the interventions. Nine trials were at high risk of detection bias due to lack of blinding of outcome assessors and three more were at unclear risk in this domain.  Pooled data showed that multi-component non-pharmacological interventions probably reduce the incidence of delirium compared to usual care (10.5% incidence in the intervention group, compared to 18.4% in the control group, risk ratio (RR) 0.57, 95% confidence interval (CI) 0.46 to 0.71, I² = 39%; 14 studies; 3693 participants; moderate-certainty evidence, downgraded due to risk of bias).  There may be little or no effect of multicomponent interventions on inpatient mortality compared to usual care (5.2% in the intervention group, compared to 4.5% in the control group, RR 1.17, 95% CI 0.79 to 1.74, I² = 15%; 10 studies; 2640 participants; low-certainty evidence downgraded due to inconsistency and imprecision).  No studies of multicomponent interventions reported data on new diagnoses of dementia.  Multicomponent interventions may result in a small reduction of around a day in the duration of a delirium episode (mean difference (MD) -0.93, 95% CI -2.01 to 0.14 days, I² = 65%; 351 participants; low-certainty evidence downgraded due to risk of bias and imprecision). The evidence is very uncertain about the effect of multicomponent interventions on delirium severity (standardised mean difference (SMD) -0.49, 95% CI -1.13 to 0.14, I²=64%; 147 participants; very low-certainty evidence downgraded due to risk of bias and serious imprecision). Multicomponent interventions may result in a reduction in hospital length of stay compared to usual care (MD -1.30 days, 95% CI -2.56 to -0.04 days, I²=91%; 3351 participants; low-certainty evidence downgraded due to risk of bias and inconsistency), but little to no difference in new care home admission at the time of hospital discharge (RR 0.77, 95% CI 0.55 to 1.07; 536 participants; low-certainty evidence downgraded due to risk of bias and imprecision). Reporting of other adverse outcomes was limited.  Our exploratory component network meta-analysis found that re-orientation (including use of familiar objects), cognitive stimulation and sleep hygiene were associated with reduced risk of incident delirium. Attention to nutrition and hydration, oxygenation, medication review, assessment of mood and bowel and bladder care were probably associated with a reduction in incident delirium but estimates included the possibility of no benefit or harm.  Reducing sensory deprivation, identification of infection, mobilisation and pain control all had summary estimates that suggested potential increases in delirium incidence, but the uncertainty in the estimates was substantial.  Evidence from two trials suggests that use of a liberal transfusion threshold over a restrictive transfusion threshold probably results in little to no difference in incident delirium (RR 0.92, 95% CI 0.62 to 1.36; I² = 9%; 294 participants; moderate-certainty evidence downgraded due to risk of bias).  Six other interventions were examined, but evidence for each was limited to single studies and we identified no evidence of delirium prevention.  AUTHORS' CONCLUSIONS: There is moderate-certainty evidence regarding the benefit of multicomponent non-pharmacological interventions for the prevention of delirium in hospitalised adults, estimated to reduce incidence by 43% compared to usual care. We found no evidence of an effect on mortality. There is emerging evidence that these interventions may reduce hospital length of stay, with a trend towards reduced delirium duration, although the effect on delirium severity remains uncertain. Further research should focus on implementation and detailed analysis of the components of the interventions to support more effective, tailored practice recommendations.

Protocol for a proof-of-concept observational study evaluating the potential utility and acceptability of a telemedicine solution for the post-anesthesia care unit

Introduction: The post-anesthesia care unit (PACU) is a clinical area designated for patients recovering from invasive procedures. There are typically several geographically dispersed PACUs within hospitals. Patients in the PACU can be unstable and at risk for complications. However, clinician coverage and patient monitoring in PACUs is not well regulated and might be sub-optimal. We hypothesize that a telemedicine center for the PACU can improve key PACU functions.

Objectives: The objective of this study is to demonstrate the potential utility and acceptability of a telemedicine center to complement the key functions of the PACU. These include participation in hand-off activities to and from the PACU, detection of physiological derangements, identification of symptoms requiring treatment, recognition of situations requiring emergency medical intervention, and determination of patient readiness for PACU discharge.

Methods and analysis: This will be a single center prospective before-and-after proof-of-concept study. Adults (18 years and older) undergoing elective surgery and recovering in two selected PACU bays will be enrolled. During the initial three-month observation phase, clinicians in the telemedicine center will not communicate with clinicians in the PACU, unless there is a specific patient safety concern. During the subsequent three-month interaction phase, clinicians in the telemedicine center will provide structured decision support to PACU clinicians. The primary outcome will be time to PACU discharge readiness determination in the two study phases. The attitudes of key stakeholders towards the telemedicine center will be assessed. Other outcomes will include detection of physiological derangements, complications, adverse symptoms requiring treatments, and emergencies requiring medical intervention.

Registration: This trial is registered on clinicaltrials.gov, NCT04020887 (16 ^th July 2019).