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      Lessons learned from developing a COVID-19 algorithm governance framework in Aotearoa New Zealand

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          ABSTRACT

          Aotearoa New Zealand’s response to the COVID-19 pandemic has included the use of algorithms that could aid decision making. Te Pokapū Hātepe o Aotearoa, the New Zealand Algorithm Hub, was established to evaluate and host COVID-19 related models and algorithms, and provide a central and secure infrastructure to support the country’s pandemic response. A critical aspect of the Hub was the formation of an appropriate governance group to ensure that algorithms being deployed underwent cross-disciplinary scrutiny prior to being made available for quick and safe implementation. This framework necessarily canvassed a broad range of perspectives, including from data science, clinical, Māori, consumer, ethical, public health, privacy, legal and governmental perspectives. To our knowledge, this is the first implementation of national algorithm governance of this type, building upon broad local and global discussion of guidelines in recent years. This paper describes the experiences and lessons learned through this process from the perspective of governance group members, emphasising the role of robust governance processes in building a high-trust platform that enables rapid translation of algorithms from research to practice.

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          Most cited references26

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          A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation

          The objective of this study was to develop a prospectively applicable method for classifying comorbid conditions which might alter the risk of mortality for use in longitudinal studies. A weighted index that takes into account the number and the seriousness of comorbid disease was developed in a cohort of 559 medical patients. The 1-yr mortality rates for the different scores were: "0", 12% (181); "1-2", 26% (225); "3-4", 52% (71); and "greater than or equal to 5", 85% (82). The index was tested for its ability to predict risk of death from comorbid disease in the second cohort of 685 patients during a 10-yr follow-up. The percent of patients who died of comorbid disease for the different scores were: "0", 8% (588); "1", 25% (54); "2", 48% (25); "greater than or equal to 3", 59% (18). With each increased level of the comorbidity index, there were stepwise increases in the cumulative mortality attributable to comorbid disease (log rank chi 2 = 165; p less than 0.0001). In this longer follow-up, age was also a predictor of mortality (p less than 0.001). The new index performed similarly to a previous system devised by Kaplan and Feinstein. The method of classifying comorbidity provides a simple, readily applicable and valid method of estimating risk of death from comorbid disease for use in longitudinal studies. Further work in larger populations is still required to refine the approach because the number of patients with any given condition in this study was relatively small.
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            Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration

            The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Statement includes a 22-item checklist, which aims to improve the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD Statement is explained in detail and accompanied by published examples of good reporting. The document also provides a valuable reference of issues to consider when designing, conducting, and analyzing prediction model studies. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, it is recommended that authors include a completed checklist in their submission. The TRIPOD checklist can also be downloaded from www.tripod-statement.org.
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              PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration

              Prediction models in health care use predictors to estimate for an individual the probability that a condition or disease is already present (diagnostic model) or will occur in the future (prognostic model). Publications on prediction models have become more common in recent years, and competing prediction models frequently exist for the same outcome or target population. Health care providers, guideline developers, and policymakers are often unsure which model to use or recommend, and in which persons or settings. Hence, systematic reviews of these studies are increasingly demanded, required, and performed. A key part of a systematic review of prediction models is examination of risk of bias and applicability to the intended population and setting. To help reviewers with this process, the authors developed PROBAST (Prediction model Risk Of Bias ASsessment Tool) for studies developing, validating, or updating (for example, extending) prediction models, both diagnostic and prognostic. PROBAST was developed through a consensus process involving a group of experts in the field. It includes 20 signaling questions across 4 domains (participants, predictors, outcome, and analysis). This explanation and elaboration document describes the rationale for including each domain and signaling question and guides researchers, reviewers, readers, and guideline developers in how to use them to assess risk of bias and applicability concerns. All concepts are illustrated with published examples across different topics. The latest version of the PROBAST checklist, accompanying documents, and filled-in examples can be downloaded from www.probast.org.

                Author and article information

                Journal
                J R Soc N Z
                J R Soc N Z
                Journal of the Royal Society of New Zealand
                Taylor & Francis
                0303-6758
                1175-8899
                19 September 2022
                2023
                19 September 2022
                : 53
                : 1 , Special issue: Artificial Intelligence in New Zealand: applications and innovation. Guest Editors: Bing Xue, Richard Green and Mengjie Zhang
                : 82-94
                Affiliations
                [a ]School of Computer Science, Waipapa Taumata Rau/University of Auckland , Auckland, New Zealand
                [b ]Auror Ltd. , Auckland, New Zealand
                [c ]Precision Driven Health, Auckland, New Zealand
                [d ]Critical Care Complex, Middlemore Hospital , Auckland, New Zealand
                [e ]Statistics New Zealand, Wellington, New Zealand
                [f ]Department of Philosophy, Waipapa Taumata Rau/University of Auckland , Auckland, New Zealand
                [g ]Orion Health, Auckland, New Zealand
                [h ]Precision Driven Health Independent Advisory Group , Auckland, New Zealand
                Author notes
                [CONTACT ] Kevin Ross kevin.ross@ 123456orionhealth.com

                HANDLING EDITOR: Bing Xue

                Supplemental data for this article can be accessed online at https://doi.org/10.1080/03036758.2022.2121290.

                Author information
                https://orcid.org/0000-0002-3593-6234
                Article
                2121290
                10.1080/03036758.2022.2121290
                11459790
                39439990
                ec53ea32-a272-461f-b835-0dcbbd97ad2c
                © 2022 Precision Driven Health. Published by Informa UK Limited, trading as Taylor & Francis Group

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License ( http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.

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                Page count
                Figures: 0, Tables: 1, Equations: 0, References: 37, Pages: 13
                Categories
                Research Articles
                Research Article

                algorithms,artificial intelligence,covid-19,clinical decision support,governance framework,healthcare data or healthcare algorithms,prediction models,risk models

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