ObjectivesTo determine the extent and nature of changes in utilisation of healthcare services during COVID-19 pandemic.DesignSystematic review.EligibilityEligible studies compared utilisation of services during COVID-19 pandemic to at least one comparable period in prior years. Services included visits, admissions, diagnostics and therapeutics. Studies were excluded if from single centres or studied only patients with COVID-19.Data sourcesPubMed, Embase, Cochrane COVID-19 Study Register and preprints were searched, without language restrictions, until 10 August, using detailed searches with key concepts including COVID-19, health services and impact.Data analysisRisk of bias was assessed by adapting the Risk of Bias in Non-randomised Studies of Interventions tool, and a Cochrane Effective Practice and Organization of Care tool. Results were analysed using descriptive statistics, graphical figures and narrative synthesis.Outcome measuresPrimary outcome was change in service utilisation between prepandemic and pandemic periods. Secondary outcome was the change in proportions of users of healthcare services with milder or more severe illness (eg, triage scores).Results3097 unique references were identified, and 81 studies across 20 countries included, reporting on >11 million services prepandemic and 6.9 million during pandemic. For the primary outcome, there were 143 estimates of changes, with a median 37% reduction in services overall (IQR −51% to −20%), comprising median reductions for visits of 42% (−53% to −32%), admissions 28% (−40% to −17%), diagnostics 31% (−53% to −24%) and for therapeutics 30% (−57% to −19%). Among 35 studies reporting secondary outcomes, there were 60 estimates, with 27 (45%) reporting larger reductions in utilisation among people with a milder spectrum of illness, and 33 (55%) reporting no difference.ConclusionsHealthcare utilisation decreased by about a third during the pandemic, with considerable variation, and with greater reductions among people with less severe illness. While addressing unmet need remains a priority, studies of health impacts of reductions may help health systems reduce unnecessary care in the postpandemic recovery.PROSPERO registration numberCRD42020203729.

Objectives: To determine patient participation rates in outpatient cardiac rehabilitation (OCR) programs; ascertain the barriers to participation; and evaluate the quality of OCR programs. Design and setting: Retrospective cohort study of patient separations from selected public and private Queensland hospitals; questionnaire survey of hospitals and all registered OCR programs. Participants: Patients discharged with cardiac diagnoses between 1 July 1999 and 30 June 2000 from 31 hospitals (24 public; 7 private). Main outcome measures: Rates of referral of hospitalised patients to OCR programs; rates of program attendance and completion; barriers to OCR referral and attendance. Results: 15 186 patients were discharged with cardiac diagnoses from participating hospitals, of whom 4346 (29%) were referred to an OCR program after discharge, compared with an estimated 59% (8895/15 186) of patients who were eligible for such a program. Proportionately more patients were referred from secondary (38% [1720/4500]) and private (52% [2116/4031]; P < 0.001) hospitals than from tertiary (25% [2626/10 686]) and public (20% [2230/11 155]) hospitals. Patients undergoing coronary revascularisation procedures comprised 35% of discharges, but accounted for 56% of all program attendances. Fewer than a third of all referred patients completed OCR programs, and only 39% of available OCR program places were fully utilised. Catchment populations of programs with unused places had excess coronary mortality. Conclusion: There is significant underutilisation of facility‐based OCR programs in Queensland. Procedures are required for identifying and referring eligible patients to existing programs and improving program compliance. Alternative OCR models are also required.

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Everyone makes mistakes, but greater awareness of the causes would help clinicians to avoid many of them, as Ian Scott explains

This paper reports on a population study of the onychophoran Euperipatoides leuckartii. The species was found to be extremely abundant in decomposing logs from the Tallaganda State Forest area. Analysis of its distribution indicated that E. leuckartii occurred only in logs with a relative water content greater than 211 %. Furthermore, the occurrence of E. leuckartii was found to be congruent with that of termites. Abundance was also correlated with altitude, which suggests an effect of tree species, although other correlated variables may be implicated. Overall, sex ratio was found to be heavily skewed in favour of females. However, the sex ratio was not homogeneous among age cohorts or logs. The skewed overall sex ratio is considered to be a result of differential lifespans and age at maturity of the sexes, and a model for log-colonisation patterns is presented that may explain the differences in peripatus sex ratios observed among logs.

Financial incentives can sometimes improve the quality of clinical practice, but they may also be an expensive distraction. Paul Glasziou and colleagues have devised a checklist to help prevent their premature or inappropriate implementation

Machine learning algorithms are being used to screen and diagnose disease, prognosticate and predict therapeutic responses. Hundreds of new algorithms are being developed, but whether they improve clinical decision making and patient outcomes remains uncertain. If clinicians are to use algorithms, they need to be reassured that key issues relating to their validity, utility, feasibility, safety and ethical use have been addressed. We propose a checklist of 10 questions that clinicians can ask of those advocating for the use of a particular algorithm, but which do not expect clinicians, as non-experts, to demonstrate mastery over what can be highly complex statistical and computational concepts. The questions are: (1) What is the purpose and context of the algorithm? (2) How good were the data used to train the algorithm? (3) Were there sufficient data to train the algorithm? (4) How well does the algorithm perform? (5) Is the algorithm transferable to new clinical settings? (6) Are the outputs of the algorithm clinically intelligible? (7) How will this algorithm fit into and complement current workflows? (8) Has use of the algorithm been shown to improve patient care and outcomes? (9) Could the algorithm cause patient harm? and (10) Does use of the algorithm raise ethical, legal or social concerns? We provide examples where an algorithm may raise concerns and apply the checklist to a recent review of diagnostic imaging applications. This checklist aims to assist clinicians in assessing algorithm readiness for routine care and identify situations where further refinement and evaluation is required prior to large-scale use.

Compared to a conservative strategy for UA/NSTEMI, an invasive strategy is associated with reduced rates of refractory angina and rehospitalization in the shorter term and myocardial infarction in the longer term. However, the invasive strategy is associated with a doubled risk of procedure-related heart attack and increased risk of bleeding and procedural biomarker leaks. Available data suggest that an invasive strategy may be particularly useful in those at high risk for recurrent events.