Physiotherapy students\u27 perceptions and experiences of clinical prediction rules

Objectives: Clinical reasoning can be difficult to teach to pre-professional physiotherapy students due to their lack of clinical experience. It may be that tools such as clinical prediction rules (CPRs) could aid the process, but there has been little investigation into their use in physiotherapy clinical education. This study aimed to determine the perceptions and experiences of physiotherapy students regarding CPRs, and whether they are learning about CPRs on clinical placement. Design: Cross-sectional survey using a paper-based questionnaire. Participants: Final year pre-professional physiotherapy students (n=371, response rate 77%) from five universities across five states of Australia. Results: Sixty percent of respondents had not heard of CPRs, and a further 19% had not clinically used CPRs. Only 21% reported using CPRs, and of these nearly three-quarters were rarely, if ever, learning about CPRs in the clinical setting. However most of those who used CPRs (78%) believed CPRs assisted in the development of clinical reasoning skills and none (0%) was opposed to the teaching of CPRs to students. The CPRs most commonly recognised and used by students were those for determining the need for an X-ray following injuries to the ankle and foot (67%), and for identifying deep venous thrombosis (63%). Conclusions: The large majority of students in this sample knew little, if anything, about CPRs and few had learned about, experienced or practiced them on clinical placement. However, students who were aware of CPRs found them helpful for their clinical reasoning and were in favour of learning more about them

Automation of Patient Trajectory Management: A deep-learning system for critical care outreach

The application of machine learning models to big data has become ubiquitous, however their successful translation into clinical practice is currently mostly limited to the field of imaging. Despite much interest and promise, there are many complex and interrelated barriers that exist in clinical settings, which must be addressed systematically in advance of wide-spread adoption of these technologies. There is limited evidence of comprehensive efforts to consider not only their raw performance metrics, but also their effective deployment, particularly in terms of the ways in which they are perceived, used and accepted by clinicians. The critical care outreach team at St Vincent’s Public Hospital want to automatically prioritise their workload by predicting in-patient deterioration risk, presented as a watch-list application. This work proposes that the proactive management of in-patients at risk of serious deterioration provides a comprehensive case-study in which to understand clinician readiness to adopt deep-learning technology due to the significant known limitations of existing manual processes. Herein is described the development of a proof of concept application uses as its input the subset of real-time clinical data available in the EMR. This data set has the noteworthy challenge of not including any electronically recorded vital signs data. Despite this, the system meets or exceeds similar benchmark models for predicting in-patient death and unplanned ICU admission, using a recurrent neural network architecture, extended with a novel data-augmentation strategy. This augmentation method has been re-implemented in the public MIMIC-III data set to confirm its generalisability. The method is notable for its applicability to discrete time-series data. Furthermore, it is rooted in knowledge of how data entry is performed within the clinical record and is therefore not restricted in applicability to a single clinical domain, instead having the potential for wide-ranging impact. The system was presented to likely end-users to understand their readiness to adopt it into their workflow, using the Technology Adoption Model. In addition to confirming feasibility of predicting risk from this limited data set, this study investigates clinician readiness to adopt artificial intelligence in the critical care setting. This is done with a two-pronged strategy, addressing technical and clinically-focused research questions in parallel

The creation and pilot testing of a method to identify strong and instantaneous responders to spinal manipulation therapy

Introduction: Spinal manipulative therapy (SMT) can provide pain relief for individuals with non-specific low back pain (NSLBP). Clinical prediction rules can be used to identify patients who are likely to respond positively to a particular treatment approach. A list of 18 signs and symptoms across 5 domains have previously been developed by expert manual therapists, and are suggested to be predictors of instantaneous relief in people with NSLBP after SMT. However, these items have yet to be developed into a workable format and tested in a clinical setting. Objectives: To develop a workable questionnaire and subsequently run a pilot study which tests the feasibility of the study in chiropractic patients with NSLBP, and the preliminary relationships between the 18 signs and symptoms (predictors) and those who have a strong and instantaneous response to SMT. Methods: Practitioner and patient questionnaires were designed based on the previously identified 18 predictors of instantaneous relief following SMT. Ten chiropractors were recruited and were each asked to recruit 10 NSLBP patients from among their normal patients. Each practitioner and patient answered the questionnaires, and feedback from practitioners was sought on the study and questionnaires. Predictors of immediate improvement after SMT were investigated using linear regression. Results: Three validated outcome measures were used in designing the questionnaires and a further nine questions were designed to cover gaps in the literature. Of the 10 chiropractors who agreed to participate, two withdrew and two were lost to follow up. In total there were 63 out of a planned 100 practitioner/patient responses. Three of the five domains had predictors showing statistically significant results for predictive outcomes. These included the patient’s prior response to SMT, the patient’s expected response, Dr’s rating of patient’s health status, Dr’s rating of how well they felt they understood the patient’s goals, and decreased range of motion identified on physical examination. Conclusion: The design of the questionnaire was based on best available evidence-based literature at the time of development. A fully powered study appears to be feasible; however, suggested changes to the questionnaire and data collection process were made. Pilot testing identified multiple possible predictors for instantaneous relief after SMT in chiropractic patients with NSLBP. These results support the need for a fully powered study to further explore the 18 possible predictors of instantaneous relief after SMT

Reducing Respiratory Virus Testing In Hospitalized Children With Machine Learning And Text Mining

Despite pressure from the federal government for US hospitals to adopt electronic medical records systems (EMR), the benefits of adopting such systems have not been fully realized. One proposed advantage of EMRs involves secondary use, in which personal health information is used for purposes other than direct health care delivery, particularly quality improvement. We sought to determine whether information recorded in the EMR could improve diagnostic pathways used to diagnose respiratory viruses in children, the most common etiology of diagnoses in the pediatric population. These tests potentially represent a source of unnecessary testing. We performed a retrospective observational study analyzing pediatric inpatients receiving respiratory virus testing at Yale-New Haven Children\u27s Hospital between March 2010 to March 2012. Billing data (age, gender, season), laboratory data (sample adequacy, results), and clinical documents were gathered. We used MetaMap, a program distributed by the National Library of Medicine, to identify phrases denoting symptoms and diseases in the admission notes of patients. Identified concepts were added as additional variables to be modeled. Weka, another freely available software that allows for easy incorporation of machine learning algorithms, was used to derive models based on the C4.5 decision tree algorithm that aim to predict whether or not patients should be tested. Orders for pediatric patients accounted for 26.3% of all respiratory virus test orders placed during this time. Negative test results accounted for 69.5% of all tests ordered during the study period. The lengths of stay for all viral diagnoses were not statistically different. Models based on age, gender and season alone, were predictive for influenza (AUC 0.743, SE = 0.126), parainfluenza (AUC 0.686, SE = 0.078), RSV (AUC 0.658, SE = 0.048), and hMPV (AUC 0.713, SE = 0.143). Using MetaMap terms alone, only the model for RSV showed discriminatory ability (AUC 0.661, SE = 0.048). When basic variables were used in conjunction with MetaMap concepts, only the model for RSV showed improved performance (AUC 0.722, SE = 0.051) in comparison to both the basic and MetaMap models. Respiratory virus tests for general admission pediatric inpatients are ordered year-round and are mostly negative. Using models based on decision tree learning, our results showed that test volume could be reduced by about 20-50% for certain tests, as measured by model specificity. Furthermore, clinical concepts obtained via text mining in conjunction with basic variables improved prediction of RSV test results. The tradeoff between the false negative rates required to achieve any substantive specificity may be mitigated by our finding that hospital stays were nearly identical, regardless of the diagnostic outcome. These results support the use of EMR data for the auditing of and improvement of laboratory utilization. In addition, the improvement of predictive modeling for RSV with a simple implementation of text mining support the idea that clinical notes can be used for secondary use

Methodological standards for the development and evaluation of clinical prediction rules: A review of the literature

Clinical prediction rules (CPRs) that predict the absolute risk of a clinical condition or future outcome for individual patients are abundant in the medical literature, however systematic reviews have demonstrated shortcomings in the methodological quality and reporting of prediction studies. To maximize the potential and clinical usefulness of CPRs, they must be rigorously developed and validated, and their impact on clinical practice and patient outcomes must be evaluated. This review aims to present a comprehensive overview of the stages involved in the development, validation and evaluation of CPRs, and to describe in detail the methodological standards required at each stage, illustrated with examples where appropriate. Important features of the study design, statistical analysis, modelling strategy, data collection, performance assessment, CPR presentation, and reporting are discussed, in addition to other, often overlooked aspects such as the acceptability, cost-effectiveness and longer-term implementation of CPRs, and their comparison with clinical judgment. Although the development and evaluation of a robust, clinically useful CPR is anything but straightforward, adherence to the plethora of methodological standards, recommendations and frameworks at each stage will assist in the development of a rigorous CPR that has the potential to contribute usefully to clinical practice and decision-making and have a positive impact on patient car

Systematic scoping review protocol for clinical prediction rules (CPRs) in the management of patients with spinal cord injuries

Introduction The upsurge in the use of clinical prediction models in general medical practice is a result of evidencebased practice. However, the total number of clinical prediction rules (CPRs) currently being used or undergoing impact analysis in the management of patients who have sustained spinal cord injuries (SCIs) is unknown. This scoping review protocol will describe the current CPRs being used and highlight their possible strengths and weaknesses in SCI management. Methods and analysis Arksey and O’Malley’s scoping review framework will be used. The following databases will be searched to identify relevant literature relating to the use of CPRs in the management of patients who have sustained an SCI: PubMed, Cumulative Index of Nursing and Allied Health Literature (CINAHL), ScienceDirect, EBSCOhost, Medline, OvidMedline and Google Scholar. Grey literature as well as reference lists of included studies will be searched. All studies relating to the use of CPRs in the management of patients with SCIs will be included. Literature searches and data extraction will be performed independently by two groups of reviewers. Ethics and dissemination Ethical clearance is not required for this scoping review study since only secondary data sources will be used. The findings of this review will be disseminated by means of peer-reviewed publication and conference proceedings. The final paper will be submitted for publication. Results of this review will also be presented at relevant conferences and disseminated to important stakeholders such as practicing physicians within specialised spinal care facilities within South Africa

Cognitive debiasing 2: Impediments to and strategies for change

In a companion paper, we proposed that cognitive debiasing is a skill essential in developing sound clinical reasoning to mitigate the incidence of diagnostic failure. We reviewed the origins of cognitive biases and some proposed mechanisms for how debiasing processes might work. In this paper, we first outline a general schema of how cognitive change occurs and the constraints that may apply. We review a variety of individual factors, many of them biases themselves, which may be impediments to change. We then examine the major strategies that have been developed in the social sciences and in medicine to achieve cognitive and affective debiasing, including the important concept of forcing functions. The abundance and rich variety of approaches that exist in the literature and in individual clinical domains illustrate the difficulties inherent in achieving cognitive change, and also the need for such interventions. Ongoing cognitive debiasing is arguably the most important feature of the critical thinker and the well-calibrated mind. We outline three groups of suggested interventions going forward: educational strategies, workplace strategies and forcing functions. We stress the importance of ambient and contextual influences on the quality of individual decision making and the need to address factors known to impair calibration of the decision maker. We also emphasise the importance of introducing these concepts and corollary development of training in critical thinking in the undergraduate level in medical education

Optimising Outcomes in Rehabilitation of Lower Limb Amputation

Limited research exists on outcomes following lower limb amputation. This is the first study to develop and validate Clinical Prediction Rules for prosthetic non-use at 4, 8 and 12 months after rehabilitation discharge. Performance thresholds that identify increased risk of prosthetic non-use have been generated for locomotor tests. Long term outcomes and comorbidities have been described for people with lower limb amputation. This thesis has contributed to evidence based health reform to models of care