Towards a sustainable and fit‐for‐purpose health workforce — lessons from New Zealand

Gorman, Des

doi:10.5694/mja11.11583

Cited by 8 publications

(9 citation statements)

References 15 publications

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“…Within medical disciplines, a key consideration is the mix of generalists and specialists. Gorman contended that New Zealand needs a “core workforce that can be employed flexibly and redeployed quickly” with a “scope of practice that is as general as possible, while such practitioners work at the top end of their licence” 6 . Gorman also noted the lack of agreed national workforce goals in any jurisdiction 7 and recommended workforce planning by service aggregates such as mental health or aged care rather than by professional discipline 8 .…”

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confidence: 99%

Will Australia have a fit‐for‐purpose medical workforce in 2025?

Strasser

2018

Medical Journal of Australia

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confidence: 99%

Will Australia have a fit‐for‐purpose medical workforce in 2025?

Strasser

2018

Medical Journal of Australia

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“…The scenarios we presented overcame this to some extent, by stating or implying what integration may look like, though some panellists still asked for more detail. However, developing scenarios with competing integrated care definitions could also be a way for policy makers and service planners to present their versions of integrated care by drafting narratives that reveal how patients and the workforce are proposed to interact and how a care model's processes may operate differently from the present [59].…”

Section: Discussionmentioning

confidence: 99%

Rethinking workforce planning for integrated care: using scenario analysis to facilitate policy development

Rees

Crampton

Gauld

et al. 2020

BMC Health Serv Res

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Background: A goal of health workforce planning is to have the most appropriate workforce available to meet prevailing needs. However, this is a difficult task when considering integrated care, as future workforces may require different numbers, roles and skill mixes than those at present. With this uncertainty and large variations in what constitutes integrated care, current health workforce policy and planning processes are poorly placed to respond. In order to address this issue, we present a scenario-based workforce planning approach. Methods: We propose a novel mixed methods design, incorporating content analysis, scenario methods and scenario analysis through the use of a policy Delphi. The design prescribes that data be gathered from workforce documents and studies that are used to develop scenarios, which are then assessed by a panel of suitably qualified people. Assessment consists of evaluating scenario desirability, feasibility and validity and includes a process for indicating policy development opportunities. Results: We confirmed our method using data from New Zealand's Older Persons Health sector and its workforce. Three scenarios resulted, one that reflects a normative direction and two alternatives that reflect key sector workforce drivers and trends. One of these, based on alternative assumptions, was found to be more desirable by the policy Delphi panel. The panel also found a number of favourable policy proposals. Conclusions: The method shows that through applying techniques that have been developed to accommodate uncertainty, health workforce planning can benefit when confronting issues associated with integrated care. The method contributes to overcoming significant weaknesses of present health workforce planning approaches by identifying a wider range of plausible futures and thematic kernels for policy development. The use of scenarios provides a means to contemplate future situations and provides opportunities for policy rehearsal and reflection.

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“…1, we are able to expand the appreciation of whether a workforce is fit for purpose and fit to practice. A fit-for-purpose health workforce [91][92][93][94][95][96][97] should have the competencies and quality standards required to meet the current and anticipated future population needs and achieve the intended policy outcomes. The concept translates to whether the availability, accessibility, acceptability and quality of the workforce are collectively able to deliver, both now and in the foreseeable future, effective coverage of the services required: that is, to attain, sustain or accelerate progress on universal health coverage and the principles and obligations of the right to health.…”

Section: What Are the Human Resources For Health Dimensions Of Univermentioning

confidence: 99%

Artificial Intelligence-Based Differential Diagnosis: Development and Validation of a Probabilistic Model to Address Lack of Large-Scale Clinical Datasets

Chishti¹,

Jaggi²,

Saini³

et al. 2020

J Med Internet Res

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Background Machine-learning or deep-learning algorithms for clinical diagnosis are inherently dependent on the availability of large-scale clinical datasets. Lack of such datasets and inherent problems such as overfitting often necessitate the development of innovative solutions. Probabilistic modeling closely mimics the rationale behind clinical diagnosis and represents a unique solution. Objective The aim of this study was to develop and validate a probabilistic model for differential diagnosis in different medical domains. Methods Numerical values of symptom-disease associations were utilized to mathematically represent medical domain knowledge. These values served as the core engine for the probabilistic model. For the given set of symptoms, the model was utilized to produce a ranked list of differential diagnoses, which was compared to the differential diagnosis constructed by a physician in a consult. Practicing medical specialists were integral in the development and validation of this model. Clinical vignettes (patient case studies) were utilized to compare the accuracy of doctors and the model against the assumed gold standard. The accuracy analysis was carried out over the following metrics: top 3 accuracy, precision, and recall. Results The model demonstrated a statistically significant improvement (P=.002) in diagnostic accuracy (85%) as compared to the doctors’ performance (67%). This advantage was retained across all three categories of clinical vignettes: 100% vs 82% (P<.001) for highly specific disease presentation, 83% vs 65% for moderately specific disease presentation (P=.005), and 72% vs 49% (P<.001) for nonspecific disease presentation. The model performed slightly better than the doctors’ average in precision (62% vs 60%, P=.43) but there was no improvement with respect to recall (53% vs 56%, P=.27). However, neither difference was statistically significant. Conclusions The present study demonstrates a drastic improvement over previously reported results that can be attributed to the development of a stable probabilistic framework utilizing symptom-disease associations to mathematically represent medical domain knowledge. The current iteration relies on static, manually curated values for calculating the degree of association. Shifting to real-world data–derived values represents the next step in model development.

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Towards a sustainable and fit‐for‐purpose health workforce — lessons from New Zealand

Cited by 8 publications

References 15 publications

Will Australia have a fit‐for‐purpose medical workforce in 2025?

Will Australia have a fit‐for‐purpose medical workforce in 2025?

Rethinking workforce planning for integrated care: using scenario analysis to facilitate policy development

Artificial Intelligence-Based Differential Diagnosis: Development and Validation of a Probabilistic Model to Address Lack of Large-Scale Clinical Datasets

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