Almost-linear time decoding algorithm for topological codes

In order to build a large scale quantum computer, one must be able to correct errors extremely fast. We design a fast decoding algorithm for topological codes to correct for Pauli errors and erasure and combination of both errors and erasure. Our algorithm has a worst case complexity of O(nα(n)), where n is the number of physical qubits and α is the inverse of Ackermann's function, which is very slowly growing. For all practical purposes, α(n)≤3. We prove that our algorithm performs optimally for errors of weight up to (d−1)/2 and for loss of up to d−1 qubits, where d is the minimum distance of the code. Numerically, we obtain a threshold of 9.9% for the 2d-toric code with perfect syndrome measurements and 2.6% with faulty measurements.

How do healthcare providers construe patient complexity? A qualitative study of multimorbidity in HIV outpatient clinical practice

IntroductionPatient complexity is an increasingly used concept in clinical practice, policy debates and medical research. Yet the literature lacks a clear definition of its meaning and drivers from the health provider’s perspective. This shortcoming is problematic for clinical practice and medical education in the light of a rising number of multimorbid patients and the need for future healthcare providers that are adequately trained in treating complex patients.ObjectivesTo develop an empirically grounded framework of healthcare providers’ perceptions of patient complexity and to characterise the relationship between case complexity, care complexity and provider experience as complexity-contributing factors.DesignQualitative study based on semistructured in-depth interviews with healthcare practitioners.SettingA Swiss hospital-based HIV outpatient clinic.ParticipantsA total of 31 healthcare providers participated. Participants volunteered to take part and comprised 17 nurses, 8 junior physicians (interns) and 6 senior physicians (residents, fellows and attendings).ResultsPerceived patient complexity arises from the combination of case complexity drivers, the provider’s perceived controllability, and a set of complexity moderators at the levels of the patient, the care provider and the broader care context. We develop a conceptual framework that outlines key relationships among these complexity-contributing factors and present 10 key questions to help guide medical professionals in making complexity more explicit and more manageable in daily practice.ConclusionsThe framework presented in this study helps to advance a shared understanding of patient complexity. Our findings inform curriculum design and the teaching of essential skills to medical students in areas characterised by high patient complexity such as general internal medicine and geriatrics. From a policy perspective, our findings have important implications for the design of more effective healthcare interventions for complex patients.

Establishing the influence of case complexity on the order of cataract lists: a cross-sectional survey

ObjectiveOrder of the theatre list and complexity of the cases are important considerations which are known to influence surgical outcomes. This survey aimed to establish their influence on cataract surgery.Methods and AnalysisCataract surgeons ordered five cataract cases according to their surgical preference, first using case notes and second using composite ORs (CORs) for posterior capsule rupture. Descriptive and non-parametric statistics were used to analyse the data.ResultsBetween 11 June and 14 July 2020, 192 cataract surgeons from 14 countries completed the online survey. Majority of the surgeons (142 vs 50) preferred to choose the order of their list (p<0.01) and to review the case notes prior to the day of surgery (89 vs 53; p=0.04). 39.86% preferred to start with the less risky case and 32.43% reserved the last position on the list for the riskiest case. There was a significant trend to order the list in an ascending level of risk, independent of whether case notes or CORs were used. Additionally, 44.79% of the respondents indicated they would be happy to have their list order planned by an automated program based on their preferred risk score.ConclusionThis survey demonstrates that cataract surgeons prefer to choose the order of their theatre list and that the order is dependent on the complexity of cases. There is support among surgeons for automated list ordering based on an objective score for risk stratification, such as a COR.

Understanding complexity in psychological services: a modified Delphi study

Rationale, aims and objectives The concept of patient or case complexity is relevant – and widely used – at all levels and stages of mental health service provision, but there have been few methodologically robust attempts to define this term. This study aimed to establish a consensus on factors contributing to patient complexity in adult psychological services using Delphi Methodology. Method Applied psychologists in a single urban/suburban UK National Health Service setting took part in a three-round modified Delphi study. Twenty-eight respondents in round one gave qualitative data on factors they considered when assessing complexity, which was subject to thematic analysis. Twenty-five respondents in round two rated how central/peripheral each theme was to their judgement using Likert scales. In a third round, twenty respondents addressed discrepancies and possible utilities of the emerging framework. Results Thirteen factors contributing to patient/case complexity (Active Severe/Enduring Mental Health, Current Coping/Functioning, Engagement, Forensic History, Iatrogenic Factors, Interpersonal Functioning, Neuro-Cognitive Functioning, Physical Health, Problematic Substance Use, Risk, Severity/Chronicity of Presenting Problems, Systemic and Socio-Economic Factors and Trauma) were identified with a high degree of consensus. All were rated as central to complexity. Conclusions We conclude that applied psychologists do have a shared understanding of complexity and make recommendations for further research validating, developing and applying this empirically derived framework. Keywords: psychological, complexity, definition, operationalising, framework development, clinical judgement

Deriving Effective Decision-Making Strategies of Prosthetists: Using Hidden Markov Modeling and Qualitative Analysis to Compare Experts and Novices

Objective This research focuses on studying the clinical decision-making strategies of expert and novice prosthetists for different case complexities. Background With an increasing global amputee population, there is an urgent need for improved amputee care. However, current prosthetic prescription standards are based on subjective expertise, making the process challenging for novices, specifically during complex patient cases. Hence, there is a need for studying the decision-making strategies of prosthetists. Method An interactive web-based survey was developed with two case studies of varying complexities. Navigation between survey pages and time spent were recorded for 28 participants including experts ( n = 20) and novices ( n = 8). Using these data, decision-making strategies, or patterns of decisions, during prosthetic prescription were derived using hidden Markov modeling. A qualitative analysis of participants’ rationale regarding decisions was used to add a deep contextualized understanding of decision-making strategies derived from the quantitative analysis. Results Unique decision-making strategies were observed across expert and novice participants. Experts tended to focus on the personal details, activity level, and state of the residual limb prior to prescription, and this strategy was independent of case complexity. Novices tended to change strategies dependent upon case complexity, fixating on certain factors when case complexity was high. Conclusion The decision-making strategies of experts stayed the same across the two cases, whereas the novices exhibited mixed strategies. Application By modeling the decision-making strategies of experts and novices, this study builds a foundation for development of an automated decision-support tool for prosthetic prescription, advancing novice training, and amputee care.