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53. Preventing chronic disease in overweight and obese patients with low health literacy using eHealth and teamwork in primary healthcare (HeLP-GP): a cluster randomised controlled trial

Author

Parker, Sharon M, primary, Barr, Margo, additional, Stocks, Nigel, additional, Denney-Wilson, Elizabeth, additional, Zwar, Nicholas, additional, Karnon, Jon, additional, Kabir, Alamgir, additional, Nutbeam, Don, additional, Roseleur, Jackie, additional, Liaw, Siaw-Teng, additional, McNamara, Carmel, additional, Frank, Oliver, additional, Tran, An, additional, Osborne, Richard, additional, Lau, Annie Y S, additional, and Harris, Mark, additional

Published
2022
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54. Parental experience of an early developmental surveillance programme for autism within Australian general practice: a qualitative study

Author

Eapen, Valsamma, primary, Winata, Teresa, additional, Gilbert, Melissa, additional, Nair, Radhika, additional, Khan, Feroza, additional, Lucien, Abbie, additional, Islam, Raisa, additional, Masi, Anne, additional, Lam-Cassettari, Christa, additional, Mendoza Diaz, Antonio, additional, Dissanayake, Cheryl, additional, Karlov, Lisa, additional, Descallar, Joseph, additional, Eastwood, John, additional, Hasan, Iqbal, additional, Jalaludin, Bin, additional, Kohlhoff, Jane, additional, Liaw, Siaw-Teng, additional, Lingam, Raghu, additional, Ong, Natalie, additional, Tam, Chun Wah Michael, additional, Woolfenden, Susan, additional, and Barbaro, Josephine, additional

Published
2022
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56. Can Australian GPs Trust Their Software?

Author

National Health Informatics Conference (2006 : Sydney, N.S.W.), Boyle, Douglas IR, Liaw, Siaw-Teng, Morrison, Iain, and Cross, Maddalena

Published
2006

57. Strengthening Care for Children (SC4C): protocol for a stepped wedge cluster randomised controlled trial of an integrated general practitioner-paediatrician model of primary care

Author

Khano, Sonia, primary, Sanci, Lena, additional, Woolfenden, Susan, additional, Zurynski, Yvonne, additional, Dalziel, Kim, additional, Liaw, Siaw-Teng, additional, Boyle, Douglas, additional, Freed, Gary L, additional, Moore, Cecilia, additional, Hodgins, Michael, additional, Le, Jane, additional, Morris, Tammy Meyers, additional, Germano, Stephanie, additional, Wheeler, Karen, additional, Lingam, Raghu, additional, and Hiscock, Harriet, additional

Published
2022
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59. Watch me grow integrated (WMG-I): protocol for a cluster randomised controlled trial of a web-based surveillance approach for developmental screening in primary care settings

Author

Eapen, Valsamma, primary, Liaw, Siaw-Teng, additional, Lingam, Raghu, additional, Woolfenden, Susan, additional, Jalaludin, Bin, additional, Page, Andrew, additional, Kohlhoff, Jane, additional, Scott, James G, additional, Lawson, K D, additional, Lam-Cassettari, Christa, additional, Heussler, Helen, additional, Descallar, Joseph, additional, Karlov, Lisa, additional, Ong, Natalie, additional, Colditz, Paul B, additional, Littlewood, Robyn, additional, Murphy, Elisabeth, additional, Deering, April, additional, Short, Kate, additional, Garg, Pankaj, additional, Blight, Victoria, additional, Rodgers, Kim, additional, Chalmers, Lucille, additional, Webb, Kerri-Lyn, additional, Atkins, Heidi, additional, Newcomb, Dana, additional, Beswick, Rachael, additional, Thomas, Clare, additional, Marron, Catherine, additional, Chambers, Aaron, additional, Scheinpflug, Sue, additional, Statham, Matt, additional, Samaranayake, Dimuthu, additional, Chay, Paul, additional, Tam, Chun Wah Michael, additional, Khan, Feroza, additional, Mendoza Diaz, Antonio, additional, Cibralic, Sara, additional, Winata, Teresa, additional, and Pritchard, Margo, additional

Published
2022
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72. Developing a Web-based Learning Network for Continuing Medical Education.

Author

Liaw, Siaw-Teng, Pearce, Chris, and Keppell, Mike

Abstract

Features of a web-based learning environment for physicians included the following: balance between content richness and bandwidth; shared knowledge and interaction using actual cases; constructivist approach to develop basic cases into advanced cases for reflection and interaction. Evaluation of the program showed comparable participation rates with similar programs. (SK)

Published
2002

73. Seek COVER:using a disease proxy to rapidly develop and validate a personalized risk calculator for COVID-19 outcomes in an international network

Author

Williams, Ross D, Markus, Aniek F, Yang, Cynthia, Duarte-Salles, Talita, DuVall, Scott L, Falconer, Thomas, Jonnagaddala, Jitendra, Kim, Chungsoo, Rho, Yeunsook, Williams, Andrew E, Machado, Amanda Alberga, An, Min Ho, Aragón, María, Areia, Carlos, Burn, Edward, Choi, Young Hwa, Drakos, Iannis, Abrahão, Maria Tereza Fernandes, Fernández-Bertolín, Sergio, Hripcsak, George, Kaas-Hansen, Benjamin Skov, Kandukuri, Prasanna L, Kors, Jan A, Kostka, Kristin, Liaw, Siaw-Teng, Lynch, Kristine E, Machnicki, Gerardo, Matheny, Michael E, Morales, Daniel, Nyberg, Fredrik, Park, Rae Woong, Prats-Uribe, Albert, Pratt, Nicole, Rao, Gowtham, Reich, Christian G, Rivera, Marcela, Seinen, Tom, Shoaibi, Azza, Spotnitz, Matthew E, Steyerberg, Ewout W, Suchard, Marc A, You, Seng Chan, Zhang, Lin, Zhou, Lili, Ryan, Patrick B, Prieto-Alhambra, Daniel, Reps, Jenna M, Rijnbeek, Peter R, Williams, Ross D, Markus, Aniek F, Yang, Cynthia, Duarte-Salles, Talita, DuVall, Scott L, Falconer, Thomas, Jonnagaddala, Jitendra, Kim, Chungsoo, Rho, Yeunsook, Williams, Andrew E, Machado, Amanda Alberga, An, Min Ho, Aragón, María, Areia, Carlos, Burn, Edward, Choi, Young Hwa, Drakos, Iannis, Abrahão, Maria Tereza Fernandes, Fernández-Bertolín, Sergio, Hripcsak, George, Kaas-Hansen, Benjamin Skov, Kandukuri, Prasanna L, Kors, Jan A, Kostka, Kristin, Liaw, Siaw-Teng, Lynch, Kristine E, Machnicki, Gerardo, Matheny, Michael E, Morales, Daniel, Nyberg, Fredrik, Park, Rae Woong, Prats-Uribe, Albert, Pratt, Nicole, Rao, Gowtham, Reich, Christian G, Rivera, Marcela, Seinen, Tom, Shoaibi, Azza, Spotnitz, Matthew E, Steyerberg, Ewout W, Suchard, Marc A, You, Seng Chan, Zhang, Lin, Zhou, Lili, Ryan, Patrick B, Prieto-Alhambra, Daniel, Reps, Jenna M, and Rijnbeek, Peter R

Abstract

BACKGROUND: We investigated whether we could use influenza data to develop prediction models for COVID-19 to increase the speed at which prediction models can reliably be developed and validated early in a pandemic. We developed COVID-19 Estimated Risk (COVER) scores that quantify a patient's risk of hospital admission with pneumonia (COVER-H), hospitalization with pneumonia requiring intensive services or death (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis using historical data from patients with influenza or flu-like symptoms and tested this in COVID-19 patients.METHODS: We analyzed a federated network of electronic medical records and administrative claims data from 14 data sources and 6 countries containing data collected on or before 4/27/2020. We used a 2-step process to develop 3 scores using historical data from patients with influenza or flu-like symptoms any time prior to 2020. The first step was to create a data-driven model using LASSO regularized logistic regression, the covariates of which were used to develop aggregate covariates for the second step where the COVER scores were developed using a smaller set of features. These 3 COVER scores were then externally validated on patients with 1) influenza or flu-like symptoms and 2) confirmed or suspected COVID-19 diagnosis across 5 databases from South Korea, Spain, and the United States. Outcomes included i) hospitalization with pneumonia, ii) hospitalization with pneumonia requiring intensive services or death, and iii) death in the 30 days after index date.RESULTS: Overall, 44,507 COVID-19 patients were included for model validation. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, kidney disease) which combined with age and sex discriminated which patients would experience any of our three outcomes. The models achieved good performance in influenza and COVID-19 coh

Published
2022

74. Seek COVER: using a disease proxy to rapidly develop and validate a personalized risk calculator for COVID-19 outcomes in an international network

Author

Williams, Ross D., Markus, Aniek F., Yang, Cynthia, Duarte-Salles, Talita, DuVall, Scott L., Falconer, Thomas, Jonnagaddala, Jitendra, Kim, Chungsoo, Rho, Yeunsook, Williams, Andrew E., Machado, Amanda Alberga, An, Min Ho, Aragon, Maria, Areia, Carlos, Burn, Edward, Choi, Young Hwa, Drakos, Iannis, Abrahao, Maria Tereza Fernandes, Fernandez-Bertolin, Sergio, Hripcsak, George, Kaas-Hansen, Benjamin Skov, Kandukuri, Prasanna L., Kors, Jan A., Kostka, Kristin, Liaw, Siaw-Teng, Lynch, Kristine E., Machnicki, Gerardo, Matheny, Michael E., Morales, Daniel, Nyberg, Fredrik, Park, Rae Woong, Prats-Uribe, Albert, Pratt, Nicole, Rao, Gowtham, Reich, Christian G., Rivera, Marcela, Seinen, Tom, Shoaibi, Azza, Spotnitz, Matthew E., Steyerberg, Ewout W., Suchard, Marc A., You, Seng Chan, Zhang, Lin, Zhou, Lili, Ryan, Patrick B., Prieto-Alhambra, Daniel, Reps, Jenna M., Rijnbeek, Peter R., Williams, Ross D., Markus, Aniek F., Yang, Cynthia, Duarte-Salles, Talita, DuVall, Scott L., Falconer, Thomas, Jonnagaddala, Jitendra, Kim, Chungsoo, Rho, Yeunsook, Williams, Andrew E., Machado, Amanda Alberga, An, Min Ho, Aragon, Maria, Areia, Carlos, Burn, Edward, Choi, Young Hwa, Drakos, Iannis, Abrahao, Maria Tereza Fernandes, Fernandez-Bertolin, Sergio, Hripcsak, George, Kaas-Hansen, Benjamin Skov, Kandukuri, Prasanna L., Kors, Jan A., Kostka, Kristin, Liaw, Siaw-Teng, Lynch, Kristine E., Machnicki, Gerardo, Matheny, Michael E., Morales, Daniel, Nyberg, Fredrik, Park, Rae Woong, Prats-Uribe, Albert, Pratt, Nicole, Rao, Gowtham, Reich, Christian G., Rivera, Marcela, Seinen, Tom, Shoaibi, Azza, Spotnitz, Matthew E., Steyerberg, Ewout W., Suchard, Marc A., You, Seng Chan, Zhang, Lin, Zhou, Lili, Ryan, Patrick B., Prieto-Alhambra, Daniel, Reps, Jenna M., and Rijnbeek, Peter R.

Abstract

Background: We investigated whether we could use influenza data to develop prediction models for COVID-19 to increase the speed at which prediction models can reliably be developed and validated early in a pandemic. We developed COVID-19 Estimated Risk (COVER) scores that quantify a patient’s risk of hospital admission with pneumonia (COVER-H), hospitalization with pneumonia requiring intensive services or death (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis using historical data from patients with influenza or flu-like symptoms and tested this in COVID-19 patients. Methods: We analyzed a federated network of electronic medical records and administrative claims data from 14 data sources and 6 countries containing data collected on or before 4/27/2020. We used a 2-step process to develop 3 scores using historical data from patients with influenza or flu-like symptoms any time prior to 2020. The first step was to create a data-driven model using LASSO regularized logistic regression, the covariates of which were used to develop aggregate covariates for the second step where the COVER scores were developed using a smaller set of features. These 3 COVER scores were then externally validated on patients with 1) influenza or flu-like symptoms and 2) confirmed or suspected COVID-19 diagnosis across 5 databases from South Korea, Spain, and the United States. Outcomes included i) hospitalization with pneumonia, ii) hospitalization with pneumonia requiring intensive services or death, and iii) death in the 30 days after index date. Results: Overall, 44,507 COVID-19 patients were included for model validation. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, kidney disease) which combined with age and sex discriminated which patients would experience any of our three outcomes. The models achieved good performance in influenza and COVID-19 cohorts. For CO

Published
2022

76. Analysis of Dual Combination Therapies Used in Treatment of Hypertension in a Multinational Cohort

Author

Lu, Yuan, primary, Van Zandt, Mui, additional, Liu, Yun, additional, Li, Jing, additional, Wang, Xialin, additional, Chen, Yong, additional, Chen, Zhengfeng, additional, Cho, Jaehyeong, additional, Dorajoo, Sreemanee Raaj, additional, Feng, Mengling, additional, Hsu, Min-Huei, additional, Hsu, Jason C., additional, Iqbal, Usman, additional, Jonnagaddala, Jitendra, additional, Li, Yu-Chuan, additional, Liaw, Siaw-Teng, additional, Lim, Hong-Seok, additional, Ngiam, Kee Yuan, additional, Nguyen, Phung-Anh, additional, Park, Rae Woong, additional, Pratt, Nicole, additional, Reich, Christian, additional, Rhee, Sang Youl, additional, Sathappan, Selva Muthu Kumaran, additional, Shin, Seo Jeong, additional, Tan, Hui Xing, additional, You, Seng Chan, additional, Zhang, Xin, additional, Krumholz, Harlan M., additional, Suchard, Marc A., additional, and Xu, Hua, additional

Published
2022
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77. Additional file 1 of Seek COVER: using a disease proxy to rapidly develop and validate a personalized risk calculator for COVID-19 outcomes in an international network

Author

Williams, Ross D., Markus, Aniek F., Yang, Cynthia, Duarte-Salles, Talita, DuVall, Scott L., Falconer, Thomas, Jonnagaddala, Jitendra, Kim, Chungsoo, Rho, Yeunsook, Williams, Andrew E., Machado, Amanda Alberga, An, Min Ho, Arag��n, Mar��a, Areia, Carlos, Burn, Edward, Choi, Young Hwa, Drakos, Iannis, Abrah��o, Maria Tereza Fernandes, Fern��ndez-Bertol��n, Sergio, Hripcsak, George, Kaas-Hansen, Benjamin Skov, Kandukuri, Prasanna L., Kors, Jan A., Kostka, Kristin, Liaw, Siaw-Teng, Lynch, Kristine E., Machnicki, Gerardo, Matheny, Michael E., Morales, Daniel, Nyberg, Fredrik, Park, Rae Woong, Prats-Uribe, Albert, Pratt, Nicole, Rao, Gowtham, Reich, Christian G., Rivera, Marcela, Seinen, Tom, Shoaibi, Azza, Spotnitz, Matthew E., Steyerberg, Ewout W., Suchard, Marc A., You, Seng Chan, Zhang, Lin, Zhou, Lili, Ryan, Patrick B., Prieto-Alhambra, Daniel, Reps, Jenna M., and Rijnbeek, Peter R.

Subjects
Data_FILES
Abstract

Additional file 1.

Published
2022
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81. Digital vaccine passports and digital health diplomacy: an online model WHO simulation.

Author

Godinho, Myron Anthony, Liaw, Siaw-Teng, Kanjo, Chipo, Marin, Heimar F, Martins, Henrique, and Quintana, Yuri

Abstract

In the wake of Coronavirus disease 2019 (COVID-19), several nations have sought to implement digital vaccine passports (DVPs) to enable the resumption of international travel. Comprising a minimum dataset for each unique individual, DVPs have the makings of a global electronic health record, broaching key issues involved in building a global digital health ecosystem. Debate simulations offer a safe, interactive space to foster participatory policy discussions for advancing digital health diplomacy. This study used an online simulation of a Model World Health Assembly to critically analyze the sociotechnical issues associated with the global implementation of DVPs, and to generate useful insights and questions about the role of diplomacy in global digital health. The debate arguments addressed and provided insights into the technological, scientific, ethical, legal, policy, and societal aspects of DVPs. Reflecting on the simulation, we discuss its opportunities and challenges for the digitalization, decolonization, decentralization, and democratization of participatory policymaking. [ABSTRACT FROM AUTHOR]

Published
2023
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82. Digital health and capability maturity models-a critical thematic review and conceptual synthesis of the literature.

Author

Liaw, Siaw-Teng and Godinho, Myron Anthony

Abstract

Objective: A literature review of capability maturity models (MMs) to inform the conceptualization, development, implementation, evaluation, and mainstreaming of MMs in digital health (DH).Methods: Electronic databases were searched using "digital health," "maturity models," and related terms based on the Digital Health Profile and Maturity Assessment Toolkit Maturity Model (DHPMAT-MM). Covidence was used to screen, identify, capture, and achieve consensus on data extracted by the authors. Descriptive statistics were generated. A thematic analysis and conceptual synthesis were conducted.Findings: Diverse domain-specific MMs and model development, implementation, and evaluation methods were found. The spread and pattern of different MMs verified the essential DH foundations and five maturity stages of the DHPMAT-MM. An unanticipated finding was the existence of a new category of community-facing MMs. Common characteristics included:1. A dynamic lifecycle approach to digital capability maturity, which is:a. responsive to environmental changes and may improve or worsen over time;b. accumulative, incorporating the attributes of the preceding stage; andc. sequential, where no maturity stage must be skipped.2. Sociotechnical quality improvement of the DH ecosystem and MM, which includes:a. investing in the organization's human, hardware, and software resources andb. a need to engage and improve the DH competencies of citizens.Conclusions: The diversity in MMs and variability in methods and content can create cognitive dissonance. A metamodel like the DHPMAT-MM can logically unify the many domain-specific MMs and guide the overall implementation and evaluation of DH ecosystems and MMs over the maturity lifecycle. [ABSTRACT FROM AUTHOR]

Published
2023
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89. Additional file 1 of Online training in evidence-based medicine and research methods for GP registrars: a mixed-methods evaluation of engagement and impact

Author

Rahimi-Ardabili, Hania, Spooner, Catherine, Harris, Mark F., Magin, Parker, Tam, Chun Wah Michael, Liaw, Siaw-Teng, and Zwar, Nicholas

Abstract

Additional file 1 Table S1 Topics, lessons and learning objectives of the six modules. Fig. S1 Examples of the course screens. Table S2 Response to an open-ended question on what participants liked most about the course (n = 160). Table S3 Responses to an open-ended question on what participants would like to see changed in the course (n = 98).

Published
2021
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90. Characterizing Dual Combination Therapy Use in Treatment Escalation of Hypertension: Real-World Evidence from Multinational Cohorts

Author

Lu, Yuan, primary, Li, Jing, additional, Wang, Xialin, additional, Cho, Jaehyeong, additional, Dorajoo, Sreemanee Raaj, additional, Feng, Mengling, additional, Hsu, Min-Huei, additional, Hsu, Jason C., additional, Hwang, Jiyoung, additional, Iqbal, Usman, additional, Jason, Chen Zhengfeng, additional, Jonnagaddala, Jitendra, additional, Li, Yu-Chuan, additional, Liaw, Siaw-Teng, additional, Lim, Hong-Seok, additional, Ngiam, Kee Yuan, additional, Nguyen, Phung-Anh, additional, Park, Rae Woong, additional, Pratt, Nicole, additional, Reich, Christian, additional, Rhee, Sang Youl, additional, Kumaran Sathappan, Selva Muthu, additional, Shin, Seo Jeong, additional, Tan, Hui Xing, additional, You, Seng Chan, additional, Zhang, Xin, additional, Krumholz, Harlan, additional, Suchard, Marc, additional, Liu, Yun, additional, Van Zandt, Mui, additional, and Xu, Hua, additional

Published
2021
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93. Implementation of the COVID-19 vulnerability index across an international network of health care data sets:Collaborative external validation study

Author

Reps, Jenna M., Kim, Chungsoo, Williams, Ross D., Markus, Aniek F., Yang, Cynthia, Duarte-Salles, Talita, Falconer, Thomas, Jonnagaddala, Jitendra, Williams, Andrew, Fernández-Bertolín, Sergio, DuVall, Scott L., Kostka, Kristin, Rao, Gowtham, Shoaibi, Azza, Ostropolets, Anna, Spotnitz, Matthew E., Zhang, Lin, Casajust, Paula, Steyerberg, Ewout W., Nyberg, Fredrik, Kaas-Hansen, Benjamin Skov, Choi, Young Hwa, Morales, Daniel, Liaw, Siaw Teng, Fernandes Abrahão, Maria Tereza, Areia, Carlos, Matheny, Michael E., Lynch, Kristine E., Aragón, María, Park, Rae Woong, Hripcsak, George, Reich, Christian G., Suchard, Marc A., You, Seng Chan, Ryan, Patrick B., Prieto-Alhambra, Daniel, Rijnbeek, Peter R., Reps, Jenna M., Kim, Chungsoo, Williams, Ross D., Markus, Aniek F., Yang, Cynthia, Duarte-Salles, Talita, Falconer, Thomas, Jonnagaddala, Jitendra, Williams, Andrew, Fernández-Bertolín, Sergio, DuVall, Scott L., Kostka, Kristin, Rao, Gowtham, Shoaibi, Azza, Ostropolets, Anna, Spotnitz, Matthew E., Zhang, Lin, Casajust, Paula, Steyerberg, Ewout W., Nyberg, Fredrik, Kaas-Hansen, Benjamin Skov, Choi, Young Hwa, Morales, Daniel, Liaw, Siaw Teng, Fernandes Abrahão, Maria Tereza, Areia, Carlos, Matheny, Michael E., Lynch, Kristine E., Aragón, María, Park, Rae Woong, Hripcsak, George, Reich, Christian G., Suchard, Marc A., You, Seng Chan, Ryan, Patrick B., Prieto-Alhambra, Daniel, and Rijnbeek, Peter R.

Abstract

Background: SARS-CoV-2 is straining health care systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate patients who require hospitalization from those who do not. The COVID-19 vulnerability (C-19) index, a model that predicts which patients will be admitted to hospital for treatment of pneumonia or pneumonia proxies, has been developed and proposed as a valuable tool for decision-making during the pandemic. However, the model is at high risk of bias according to the "prediction model risk of bias assessment" criteria, and it has not been externally validated. Objective: The aim of this study was to externally validate the C-19 index across a range of health care settings to determine how well it broadly predicts hospitalization due to pneumonia in COVID-19 cases. Methods: We followed the Observational Health Data Sciences and Informatics (OHDSI) framework for external validation to assess the reliability of the C-19 index. We evaluated the model on two different target populations, 41,381 patients who presented with SARS-CoV-2 at an outpatient or emergency department visit and 9,429,285 patients who presented with influenza or related symptoms during an outpatient or emergency department visit, to predict their risk of hospitalization with pneumonia during the following 0-30 days. In total, we validated the model across a network of 14 databases spanning the United States, Europe, Australia, and Asia. Results: The internal validation performance of the C-19 index had a C statistic of 0.73, and the calibration was not reported by the authors. When we externally validated it by transporting it to SARS-CoV-2 data, the model obtained C statistics of 0.36, 0.53 (0.473-0.584) and 0.56 (0.488-0.636) on Spanish, US, and South Korean data sets, respectively. The calibration was poor, with the model underestimating risk. When validated on 12 data sets containing influenza patients across the OH

Published
2021

95. Clinical, information and business process modeling to promote development of safe and flexible software

Author

Liaw, Siaw-Teng, Deveny, Elizabeth, Morrison, Iain, and Lewis, Bryn

Subjects
Decision support systems -- Usage -- Evaluation, Medical protocols -- Technology application -- Models -- Methods -- Usage, Evidence-based medicine -- Methods -- Evaluation -- Models -- Usage -- Technology application, Asthma -- Diagnosis -- Care and treatment, Computers and office automation industries, Health, Technology application, Decision support software, Diagnosis, Care and treatment, Evaluation, Usage, Models, Methods
Abstract

Using a factorial vignette survey and modeling methodology, we developed clinical and information models--incorporating evidence base, key concepts, relevant terms, decision-making and workflow needed to practice safely and effectively--to guide [...]

Published
2006

96. Primary Care Informatics Response to Covid-19 Pandemic: Adaptation, Progress, and Lessons from Four Countries with High ICT Development

Author

Liaw, Siaw-Teng, additional, Kuziemsky, Craig, additional, Schreiber, Richard, additional, Jonnagaddala, Jitendra, additional, Liyanage, Harshana, additional, Chittalia, Aliasgar, additional, Bahniwal, Ravninder, additional, He, Jennifer W., additional, Ryan, Bridget L., additional, Lizotte, Daniel J., additional, Kueper, Jacqueline K., additional, Terry, Amanda L., additional, and de Lusignan, Simon, additional

Published
2021
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99. A New Method for Estimating the Incidence of Infectious Diseases

Author

McManus, Hamish, primary, Callander, Denton, additional, Asselin, Jason, additional, McMahon, James, additional, Hoy, Jennifer F, additional, Templeton, David J, additional, Fairley, Christopher K, additional, Donovan, Basil, additional, Pedrana, Alisa E, additional, Keen, Phillip, additional, Wilson, David P, additional, Elliott, Julian, additional, Kaldor, John, additional, Liaw, Siaw-Teng, additional, Petoumenos, Kathy, additional, Holt, Martin, additional, Hellard, Margaret E, additional, Grulich, Andrew E, additional, Carr, Andrew, additional, Stoove, Mark A, additional, and Guy, Rebecca J, additional

Published
2021
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100. "Digital Health Diplomacy" in Global Digital Health? A call for critique and discourse.

Author

Godinho, Myron Anthony, Martins, Henrique, Al-Shorbaji, Najeeb, Quintana, Yuri, and Liaw, Siaw-Teng

Abstract

Actualizing the vision of Global Digital Health is a central issue on the Global Health Diplomacy agenda. The COVID-reinforced need for accelerated digital health progress will require political structures and processes to build a foundation for Global Digital Health. Simultaneously, Global Health Diplomacy uses digital technologies in its enactment. Both phenomena have driven interest in the term "Digital Health Diplomacy." A review of the literature revealed 2 emerging but distinct definitions that have been published very recently, each with its associated discourse and practice. This multiplicity of ideas demonstrates the myriad ways in which global digital and political systems are becoming increasingly entangled. Untangling these, this paper proposes and discusses 3 dimensions of Digital Health Diplomacy: "Diplomacy for digital health," "Digital health for diplomacy," and "Digital health in diplomacy." It calls upon digital health professionals, diplomats, political and social scientists, epidemiologists, and clinicians to discuss, critique, and advance this emerging domain. [ABSTRACT FROM AUTHOR]

Published
2022
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