Implementing criteria-led discharge for acute admissions to facilitate the elective recovery from COVID-19: an example in acute tonsillitis.

BACKGROUND: Healthcare systems face unprecedented numbers of patients waiting for elective treatments in the wake of the COVID-19 pandemic. Hospitals must urgently optimise patient pathways and build capacity to meet the populations health needs. Criteria-led discharge (CLD) is frequently used to optimise elective care pathways but may hold potential in discharging patients at the end of an acute hospital admission. METHODS: We conducted a quality improvement project to design and introduce a novel inpatient pathway using CLD for patients with severe acute tonsillitis. Our analysis compared the standardisation of treatment, length of stay, discharge time and readmission rate between those treated on the novel pathway compared with standard treatment. RESULTS: The study population included 137 patients admitted to a tertiary centre with acute tonsillitis. Introduction of the tonsillitis pathway using CLD resulted in a significant reduction in median length of stay from 24 hours to 18 hours. Of those treated on the tonsillitis pathway, 52.2% were discharged prior to midday compared with 29.1% who received standard treatment. No patient discharged using CLD required readmission. CONCLUSION: CLD is safe and effective at reducing length of stay in patients requiring acute hospital admission for acute tonsillitis. CLD should be used and evaluated in further novel patient pathways across different areas of medicine to optimise care and build capacity for provision of elective healthcare services. Further research is required to investigate safe and optimal criteria which indicate patients are fit for discharge.

Comparison of static and rolling logistic regression models on predicting invasive mechanical ventilation or death from COVID-19-A retrospective, multicentre study.

INTRODUCTION: COVID-19 virus has undergone mutations, and the introduction of vaccines and effective treatments have changed its clinical severity. We hypothesized that models that evolve may better predict invasive mechanical ventilation or death than do static models. METHODS: This retrospective study of adult patients with COVID-19 from six Michigan hospitals analysed 20 demographic, comorbid, vital sign and laboratory factors, one derived factor and nine factors representing changes in vital signs or laboratory values with time for their ability to predict death or invasive mechanical ventilation within the next 4, 8 or 24 h. Static logistic regression was constructed on the initial 300 patients and tested on the remaining 6741 patients. Rolling logistic regression was similarly constructed on the initial 300 patients, but then new patients were added, and older patients removed. Each new construction model was subsequently tested on the next patient. Static and rolling models were compared with receiver operator characteristic and precision-recall curves. RESULTS: Of the 7041 patients, 534 (7.6%) required invasive mechanical ventilation or died within 14 days of arrival. Rolling models improved discrimination (0.865 ± 0.010, 0.856 ± 0.007 and 0.843 ± 0.005 for the 4, 8 and 24-h models, respectively; all p < 0.001 compared with the static logistic regressions with 0.827 ± 0.011, 0.794 ± 0.012 and 0.735 ± 0.012, respectively). Similarly, the areas under the precision-recall curves improved from 0.006, 0.010 and 0.021 with the static models to 0.030, 0.045 and 0.076 for the 4-, 8- and 24-h rolling models, respectively, all p < 0.001. CONCLUSION: Rolling models with contemporaneous data maintained better metrics of performance than static models, which used older data.

Transitioning EPMA applications in a large multisite teaching hospital.

INTRODUCTION: University Hospitals of Leicester (UHL) has co-developed and deployed a novel Electronic Prescribing and Medicines Administration (EPMA) application as part of the trust electronic patient record (EPR) programme that meets specific clinical demands and interoperability standards of the National Health Service (NHS) despite clinical pressures from the COVID-19 pandemic. METHODS: Following an initial limited pilot deployment, a big-bang whole site-based approach allowed transition of 1844 acute adult inpatients beds from an existing standalone EMPA to the new system. This project used a frontline driven and agile management strategy. Clinical risk was managed using a combination of standard risk logs, robust clinical prototyping and robust disaster recovery plans. Early engagement with clinical teams allowed for advanced product configuration before live deployment and reduced the need for sustained transition support for clinical staff. RESULTS: An iterative, well-governed approach, led by a combination of information technology (IT) and clinical staff with a responsive vendor, enabled a complex new EPMA system in a large acute NHS trust to be deployed with limited resources despite the ongoing COVID-19 pandemic. DISCUSSION: The development and deployment of EMPA and EPR systems across NHS trusts is a key enabler for better healthcare delivery. This case study shows it is possible to deploy a new clinical IT system at scale without interruption of clinical services and with a relatively modest deployment team. Sustainability of the project was also ensured through a clear clinically led governance structure to manage risk quickly and carry lessons learnt onto new developments.

Early detection and treatment of acute illness in medical patients with novel software: a prospective quality improvement initiative.

OBJECTIVE: An ageing population and rising healthcare costs place healthcare systems at risk of failure. Our goal was to develop a technology that would identify illness early, initiate action and therein improve patient care, outcomes and save healthcare resources. DESIGN: This was a prospective interventional quality improvement study. SETTING: A 40 bed medical floor in a 300 bed Canadian tertiary care regional referral hospital. PARTICIPANTS: General ward patients randomly assigned to control or treatment groups. There was no cross-over or loss to follow-up. INTERVENTION: We designed an algorithm and software programme capable of detecting the sentinel change in a deteriorating patient's clinical condition and once detected direct early investigation and care. Study duration was 1 year. MAIN OUTCOME MEASURES: Primary outcome was patient transfer from the general medical ward to the intensive care unit (ICU). The secondary outcome was the time needed to (1) order investigations (2) contact senior medical staff and (3) senior medical staff intervention. RESULTS: We identified a decrease in the transfer of patients from the medical ward to the ICU. Over the course of the study including 273 patients (110 in the control group and 163 in the treatment group), transfers dropped from 14 to 3 with a relative risk reduction of 85.54% (95% CI 84.96 to 86.1), a number needed to treat of 9.19 (95% CI 9.01 to 9.36) and a absolute risk reduction of 10.89% (95% CI 10.7 to 11.1). We also found a statistically significant reduction in the time required to order investigations (p=0.049), contact senior medical staff (p=0.040) and senior medical staff intervention (p=0.045). CONCLUSION: A novel algorithm and software in the hands of nursing staff identified acute illness with adequate sensitivity and specificity to dramatically reduce ICU transfers and time to clinical intervention on a medical ward.

Safety implications of remote assessments for suspected COVID-19: qualitative study in UK primary care.

BACKGROUND: The introduction of remote triage and assessment early in the pandemic raised questions about patient safety. We sought to capture patients and clinicians' experiences of the management of suspected acute COVID-19 and generate wider lessons to inform safer care. SETTING AND SAMPLE: UK primary healthcare. A subset of relevant data was drawn from five linked in-pandemic qualitative studies. The data set, on a total of 87 participants recruited via social media, patient groups and snowballing, comprised free text excerpts from narrative interviews (10 survivors of acute COVID-19), online focus groups (20 patients and 30 clinicians), contributions to a Delphi panel (12 clinicians) and fieldnotes from an online workshop (15 patients, clinicians and stakeholders). METHODS: Data were uploaded onto NVivo. Coding was initially deductive and informed by WHO and Institute of Medicine frameworks of quality and safety. Further inductive analysis refined our theorisation using a wider range of theories-including those of risk, resilience, crisis management and social justice. RESULTS: In the early weeks of the pandemic, patient safety was compromised by the driving logic of 'stay home' and 'protect the NHS', in which both patients and clinicians were encouraged to act in a way that helped reduce pressure on an overloaded system facing a novel pathogen with insufficient staff, tools, processes and systems. Furthermore, patients and clinicians observed a shift to a more transactional approach characterised by overuse of algorithms and decision support tools, limited empathy and lack of holistic assessment. CONCLUSION: Lessons from the pandemic suggest three key strategies are needed to prevent avoidable deaths and inequalities in the next crisis: (1) strengthen system resilience (including improved resourcing and staffing; support of new tools and processes; and recognising primary care's role as the 'risk sink' of the healthcare system); (2) develop evidence-based triage and scoring systems; and (3) address social vulnerability.