Describe the population receiving orthotic/prosthetic services using telehealth in Australia, and their experience and satisfaction: a quantitative and qualitative investigation

Telehealth may help meet the growing demand for orthotic/prosthetic services. Despite the resurgence of telehealth due to COVID-19, there is limited evidence to inform policy and funding decisions, nor guide practitioners. Participants were adult orthosis/prosthesis users or parents/guardians of child orthosis/prosthesis users. Participants were convenience sampled following an orthotic/prosthetic telehealth service. An online survey included: demographics, Telehealth Usability Questionnaire, and the Orthotic Prosthetic Users Survey – Client Satisfaction with Services. A subsample of participants took part in a semi-structured interview. Most participants were tertiary educated, middle-aged, female, and lived in metropolitan or regional centres. Most telehealth services were for routine reviews. Most participants chose to use telehealth given the distance to the orthotic/prosthetic service, irrespective of whether they lived in metropolitan cities or regional areas. Participants were highly satisfied with the telehealth mode and the clinical service they received via telehealth. While orthosis/prosthesis users were highly satisfied with the clinical service received, and the telehealth mode, technical issues affected reliability and detracted from the user experience. Interviews highlighted the importance of high-quality interpersonal communication, agency and control over the decision to use telehealth, and a degree of health literacy from a lived experience of using an orthosis/prosthesis. Orthotic/prosthetic users were highly satisfied with the clinical services they received via telehealth.Satisfaction was linked to having agency and control over the decision to use telehealth, a clear understanding of the purpose of the appointment and any requirements, and a degree of health literacy that facilitated communication.Orthosis/prosthesis users and practitioners can make informed choices about using telehealth which suggests that many telehealth guidelines maybe unnecessarily risk averse.Telehealth is a useful tool to overcome barriers to accessing orthotic/prosthetic care for people in both metropolitan and regional areas.There are opportunities to support clinicians with targeted telehealth education to improve practice and reduce barriers to high-quality telehealth services. Orthotic/prosthetic users were highly satisfied with the clinical services they received via telehealth. Satisfaction was linked to having agency and control over the decision to use telehealth, a clear understanding of the purpose of the appointment and any requirements, and a degree of health literacy that facilitated communication. Orthosis/prosthesis users and practitioners can make informed choices about using telehealth which suggests that many telehealth guidelines maybe unnecessarily risk averse. Telehealth is a useful tool to overcome barriers to accessing orthotic/prosthetic care for people in both metropolitan and regional areas. There are opportunities to support clinicians with targeted telehealth education to improve practice and reduce barriers to high-quality telehealth services.</p

Demographic characteristics by state/territory for all years combined (from 2007–8 to 2011–12).

<p>Demographic characteristics by state/territory for all years combined (from 2007–8 to 2011–12).</p

Relative risk and 95%CI (in brackets) describing the influence of the independent variables on the crude incidence rate of lower limb amputation.

<p>The relative risk for each year is expressed relative to the baseline year 2007–8.</p

Geographic Variation of the Incidence Rate of Lower Limb Amputation in Australia from 2007-12

<div><p>In Australia, little is known about how the incidence rate (IR) of lower limb amputation (LLA) varies across the country. While studies in other economically developed countries have shown considerable geographic variation in the IR-LLA, mostly these have not considered whether the effect of common risk factors are the same across regions. Mapping variation of the IR-LLA, and the effect of common risk factors, is an important first step to focus research into areas of greatest need and support the development of regional specific hypotheses for in-depth examination. The aim of this study was to describe the geographic variation in the IR-LLA across Australia and understand whether the effect of common risk factors was the same across regions. Using hospital episode data from the Australian National Hospital Morbidity database and Australian Bureau of Statistics, the all-cause crude and age-standardised IR-LLA in males and females were calculated for the nation and each state and territory. Generalised Linear Models were developed to understand which factors influenced geographic variation in the crude IR-LLA. While the crude and age-standardised IR-LLA in males and females were similar in most states and territories, they were higher in the Northern Territory. The effect of older age, being male and the presence of type 2 diabetes was associated with an increase of IR-LLA in most states and territories. In the Northern Territory, the younger age at amputation confounded the effect of sex and type 2 diabetes. There are likely to be many factors not included in this investigation, such as Indigenous status, that may explain part of the variation in the IR-LLA not captured in our models. Further research is needed to identify regional- and population- specific factors that could be modified to reduce the IR-LLA in all states and territories of Australia.</p></div

Age-standardised incidence rate for males and females by nation and state/territory per 100,000 person-years, from 2007–2008 to 2011–2012.

<p>Age-standardised incidence rate for males and females by nation and state/territory per 100,000 person-years, from 2007–2008 to 2011–2012.</p

Geographic variation in the age-adjusted incidence rate of lower limb amputation for females (top) and males (bottom) across Australia from 2007–8 to 2011–12.

<p>Incidence rates expressed per 100,000 population by state/territory.</p

Inside the Mind of a Medicinal Chemist: The Role of Human Bias in Compound Prioritization during Drug Discovery

<div><p>Medicinal chemists’ “intuition” is critical for success in modern drug discovery. Early in the discovery process, chemists select a subset of compounds for further research, often from many viable candidates. These decisions determine the success of a discovery campaign, and ultimately what kind of drugs are developed and marketed to the public. Surprisingly little is known about the cognitive aspects of chemists’ decision-making when they prioritize compounds. We investigate 1) how and to what extent chemists simplify the problem of identifying promising compounds, 2) whether chemists agree with each other about the criteria used for such decisions, and 3) how accurately chemists report the criteria they use for these decisions. Chemists were surveyed and asked to select chemical fragments that they would be willing to develop into a lead compound from a set of ∼4,000 available fragments. Based on each chemist’s selections, computational classifiers were built to model each chemist’s selection strategy. Results suggest that chemists greatly simplified the problem, typically using only 1–2 of many possible parameters when making their selections. Although chemists tended to use the same parameters to select compounds, differing value preferences for these parameters led to an overall lack of consensus in compound selections. Moreover, what little agreement there was among the chemists was largely in what fragments were <em>undesirable</em>. Furthermore, chemists were often unaware of the parameters (such as compound size) which were statistically significant in their selections, and overestimated the number of parameters they employed. A critical evaluation of the problem space faced by medicinal chemists and cognitive models of categorization were especially useful in understanding the low consensus between chemists.</p> </div

The parameters extracted from the SNB (red) and RF (blue) classifiers are compared with parameters designated as important in chemists’ self-reports (grey).

<p>The primary parameters for the classifiers are depicted as stars, and the secondary parameters are depicted as circles. The one-tailed Fisher exact probability test (<i>p</i>) is reported for each parameter (except chains and charge), indicating that the SNB and RF parameters show agreement with each other, while the self reported parameters are independent of either of the classifier’s parameters.</p

The selection characteristics of chemists with high estimated consensus.

<p>The cultural consensus model was applied to a subset of fragments (311) with >75% agreement by chemists. The estimated consensus obtained by this method is plotted against the fraction of fragments passed by chemists for the entire survey. Each shape describes the primary SNB parameter used to reproduce chemists’ selections, and the color depicts the ROC score of naïve Bayesian classifiers built using ECFP4 as a descriptor for each chemist. A subset of high consensus chemists is above the dashed grey line.</p

Examples of selection preferences based on simple physicochemical properties, and the corresponding SNB classifiers.

<p><b>A</b>: Histogram of number of atoms of fragments selected by chemist 3 as good (green) or bad (red) starting points for drug discovery campaigns. Frequencies are normalized by the total number of selected or unselected compounds, respectively. <b>B</b>: Bayesian score versus number of atoms for minimal Bayesian model build for chemist 3. A positive score indicates a favorable number of atoms, while a negative score indicates an unfavorable number of atoms. <b>C</b>: Histogram of molecular polar surface area of fragments selected by chemist 12 as good (green) or bad (red) starting points for drug discovery campaigns. Frequencies are normalized by the total number of selected or unselected compounds, respectively. <b>D</b>: Bayesian score versus molecular polar surface area bins for SNB classifier built for chemist 12.</p