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1. The True Colours Remote Symptom Monitoring System: A Decade of Evolution

Author

Goodday, Sarah M, Atkinson, Lauren, Goodwin, Guy, Saunders, Kate, South, Matthew, Mackay, Clare, Denis, Mike, Hinds, Chris, Attenburrow, Mary-Jane, Davies, Jim, Welch, James, Stevens, William, Mansfield, Karen, Suvilehto, Juulia, and Geddes, John

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

The True Colours remote mood monitoring system was developed over a decade ago by researchers, psychiatrists, and software engineers at the University of Oxford to allow patients to report on a range of symptoms via text messages, Web interfaces, or mobile phone apps. The system has evolved to encompass a wide range of measures, including psychiatric symptoms, quality of life, and medication. Patients are prompted to provide data according to an agreed personal schedule: weekly, daily, or at specific times during the day. The system has been applied across a number of different populations, for the reporting of mood, anxiety, substance use, eating and personality disorders, psychosis, self-harm, and inflammatory bowel disease, and it has shown good compliance. Over the past decade, there have been over 36,000 registered True Colours patients and participants in the United Kingdom, with more than 20 deployments of the system supporting clinical service and research delivery. The system has been adopted for routine clinical care in mental health services, supporting more than 3000 adult patients in secondary care, and 27,263 adolescent patients are currently registered within Oxfordshire and Buckinghamshire. The system has also proven to be an invaluable scientific resource as a platform for research into mood instability and as an electronic outcome measure in randomized controlled trials. This paper aimed to report on the existing applications of the system, setting out lessons learned, and to discuss the implications for tailored symptom monitoring, as well as the barriers to implementation at a larger scale.

Published
2020
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4. The Dementias Platform UK (DPUK) Data Portal

Author

Bauermeister, Sarah, Orton, Christopher, Thompson, Simon, Barker, Roger A., Bauermeister, Joshua R., Ben-Shlomo, Yoav, Brayne, Carol, Burn, David, Campbell, Archie, Calvin, Catherine, Chandran, Siddharthan, Chaturvedi, Nishi, Chêne, Geneviève, Chessell, Iain P., Corbett, Anne, Davis, Daniel H. J., Denis, Mike, Dufouil, Carole, Elliott, Paul, Fox, Nick, Hill, Derek, Hofer, Scott M., Hu, Michele T., Jindra, Christoph, Kee, Frank, Kim, Chi-Hun, Kim, Changsoo, Kivimaki, Mika, Koychev, Ivan, Lawson, Rachael A., Linden, Gerry J., Lyons, Ronan A., Mackay, Clare, Matthews, Paul M., McGuiness, Bernadette, Middleton, Lefkos, Moody, Catherine, Moore, Katrina, Na, Duk L., O’Brien, John T., Ourselin, Sebastien, Paranjothy, Shantini, Park, Ki-Soo, Porteous, David J., Richards, Marcus, Ritchie, Craig W., Rohrer, Jonathan D., Rossor, Martin N., Rowe, James B., Scahill, Rachael, Schnier, Christian, Schott, Jonathan M., Seo, Sang W., South, Matthew, Steptoe, Matthew, Tabrizi, Sarah J., Tales, Andrea, Tillin, Therese, Timpson, Nicholas J., Toga, Arthur W., Visser, Pieter-Jelle, Wade-Martins, Richard, Wilkinson, Tim, Williams, Julie, Wong, Andrew, and Gallacher, John E. J.

Published
2020

6. Modelling clinical narrative as computable knowledge: The NICE computable implementation guidance project

Author

Scott, Philip, primary, Heigl, Michaela, additional, McCay, Charles, additional, Shepperdson, Polly, additional, Lima‐Walton, Elia, additional, Andrikopoulou, Elisavet, additional, Brunnhuber, Klara, additional, Cornelius, Gary, additional, Faulding, Susan, additional, McAlister, Ben, additional, Rowark, Shaun, additional, South, Matthew, additional, Thomas, Mark R., additional, Whatling, Justin, additional, Williams, John, additional, Wyatt, Jeremy C., additional, and Greaves, Felix, additional

Published
2023
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7. Exploring UK medical school differences: the MedDifs study of selection, teaching, student and F1 perceptions, postgraduate outcomes and fitness to practise

Author

McManus, I. C., Harborne, Andrew Christopher, Horsfall, Hugo Layard, Joseph, Tobin, Smith, Daniel T., Marshall-Andon, Tess, Samuels, Ryan, Kearsley, Joshua William, Abbas, Nadine, Baig, Hassan, Beecham, Joseph, Benons, Natasha, Caird, Charlie, Clark, Ryan, Cope, Thomas, Coultas, James, Debenham, Luke, Douglas, Sarah, Eldridge, Jack, Hughes-Gooding, Thomas, Jakubowska, Agnieszka, Jones, Oliver, Lancaster, Eve, MacMillan, Calum, McAllister, Ross, Merzougui, Wassim, Phillips, Ben, Phillips, Simon, Risk, Omar, Sage, Adam, Sooltangos, Aisha, Spencer, Robert, Tajbakhsh, Roxanne, Adesalu, Oluseyi, Aganin, Ivan, Ahmed, Ammar, Aiken, Katherine, Akeredolu, Alimatu-Sadia, Alam, Ibrahim, Ali, Aamna, Anderson, Richard, Ang, Jia Jun, Anis, Fady Sameh, Aojula, Sonam, Arthur, Catherine, Ashby, Alena, Ashraf, Ahmed, Aspinall, Emma, Awad, Mark, Yahaya, Abdul-Muiz Azri, Badhrinarayanan, Shreya, Bandyopadhyay, Soham, Barnes, Sam, Bassey-Duke, Daisy, Boreham, Charlotte, Braine, Rebecca, Brandreth, Joseph, Carrington, Zoe, Cashin, Zoe, Chatterjee, Shaunak, Chawla, Mehar, Chean, Chung Shen, Clements, Chris, Clough, Richard, Coulthurst, Jessica, Curry, Liam, Daniels, Vinnie Christine, Davies, Simon, Davis, Rebecca, De Waal, Hanelie, Desai, Nasreen, Douglas, Hannah, Druce, James, Ejamike, Lady-Namera, Esere, Meron, Eyre, Alex, Fazmin, Ibrahim Talal, Fitzgerald-Smith, Sophia, Ford, Verity, Freeston, Sarah, Garnett, Katherine, General, Whitney, Gilbert, Helen, Gowie, Zein, Grafton-Clarke, Ciaran, Gudka, Keshni, Gumber, Leher, Gupta, Rishi, Harlow, Chris, Harrington, Amy, Heaney, Adele, Ho, Wing Hang Serene, Holloway, Lucy, Hood, Christina, Houghton, Eleanor, Houshangi, Saba, Howard, Emma, Human, Benjamin, Hunter, Harriet, Hussain, Ifrah, Hussain, Sami, Jackson-Taylor, Richard Thomas, Jacob-Ramsdale, Bronwen, Janjuha, Ryan, Jawad, Saleh, Jelani, Muzzamil, Johnston, David, Jones, Mike, Kalidindi, Sadhana, Kalsi, Savraj, Kalyanasundaram, Asanish, Kane, Anna, Kaur, Sahaj, Al-Othman, Othman Khaled, Khan, Qaisar, Khullar, Sajan, Kirkland, Priscilla, Lawrence-Smith, Hannah, Leeson, Charlotte, Lenaerts, Julius Elisabeth Richard, Long, Kerry, Lubbock, Simon, Burrell, Jamie Mac Donald, Maguire, Rachel, Mahendran, Praveen, Majeed, Saad, Malhotra, Prabhjot Singh, Mandagere, Vinay, Mantelakis, Angelos, McGovern, Sophie, Mosuro, Anjola, Moxley, Adam, Mustoe, Sophie, Myers, Sam, Nadeem, Kiran, Nasseri, Reza, Newman, Tom, Nzewi, Richard, Ogborne, Rosalie, Omatseye, Joyce, Paddock, Sophie, Parkin, James, Patel, Mohit, Pawar, Sohini, Pearce, Stuart, Penrice, Samuel, Purdy, Julian, Ramjan, Raisa, Randhawa, Ratan, Rasul, Usman, Raymond-Taggert, Elliot, Razey, Rebecca, Razzaghi, Carmel, Reel, Eimear, Revell, Elliot John, Rigbye, Joanna, Rotimi, Oloruntobi, Said, Abdelrahman, Sanders, Emma, Sangal, Pranoy, Grandal, Nora Sangvik, Shah, Aadam, Shah, Rahul Atul, Shotton, Oliver, Sims, Daniel, Smart, Katie, Smith, Martha Amy, Smith, Nick, Sopian, Aninditya Salma, South, Matthew, Speller, Jessica, Syer, Tom J., Ta, Ngan Hong, Tadross, Daniel, Thompson, Benjamin, Trevett, Jess, Tyler, Matthew, Ullah, Roshan, Utukuri, Mrudula, Vadera, Shree, Van Den Tooren, Harriet, Venturini, Sara, Vijayakumar, Aradhya, Vine, Melanie, Wellbelove, Zoe, Wittner, Liora, Yong, Geoffrey Hong Kiat, Ziyada, Farris, and Devine, Oliver Patrick

Published
2020
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8. The Analysis of Teaching of Medical Schools (AToMS) survey: an analysis of 47,258 timetabled teaching events in 25 UK medical schools relating to timing, duration, teaching formats, teaching content, and problem-based learning

Author

Devine, Oliver Patrick, Harborne, Andrew Christopher, Horsfall, Hugo Layard, Joseph, Tobin, Marshall-Andon, Tess, Samuels, Ryan, Kearsley, Joshua William, Abbas, Nadine, Baig, Hassan, Beecham, Joseph, Benons, Natasha, Caird, Charlie, Clark, Ryan, Cope, Thomas, Coultas, James, Debenham, Luke, Douglas, Sarah, Eldridge, Jack, Hughes-Gooding, Thomas, Jakubowska, Agnieszka, Jones, Oliver, Lancaster, Eve, MacMillan, Calum, McAllister, Ross, Merzougui, Wassim, Phillips, Ben, Phillips, Simon, Risk, Omar, Sage, Adam, Sooltangos, Aisha, Spencer, Robert, Tajbakhsh, Roxanne, Adesalu, Oluseyi, Aganin, Ivan, Ahmed, Ammar, Aiken, Katherine, Akeredolu, Alimatu-Sadia, Alam, Ibrahim, Ali, Aamna, Anderson, Richard, Ang, Jia Jun, Anis, Fady Sameh, Aojula, Sonam, Arthur, Catherine, Ashby, Alena, Ashraf, Ahmed, Aspinall, Emma, Awad, Mark, Yahaya, Abdul-Muiz Azri, Badhrinarayanan, Shreya, Bandyopadhyay, Soham, Barnes, Sam, Bassey-Duke, Daisy, Boreham, Charlotte, Braine, Rebecca, Brandreth, Joseph, Carrington, Zoe, Cashin, Zoe, Chatterjee, Shaunak, Chawla, Mehar, Chean, Chung Shen, Clements, Chris, Clough, Richard, Coulthurst, Jessica, Curry, Liam, Daniels, Vinnie Christine, Davies, Simon, Davis, Rebecca, De Waal, Hanelie, Desai, Nasreen, Douglas, Hannah, Druce, James, Ejamike, Lady-Namera, Esere, Meron, Eyre, Alex, Fazmin, Ibrahim Talal, Fitzgerald-Smith, Sophia, Ford, Verity, Freeston, Sarah, Garnett, Katherine, General, Whitney, Gilbert, Helen, Gowie, Zein, Grafton-Clarke, Ciaran, Gudka, Keshni, Gumber, Leher, Gupta, Rishi, Harlow, Chris, Harrington, Amy, Heaney, Adele, Ho, Wing Hang Serene, Holloway, Lucy, Hood, Christina, Houghton, Eleanor, Houshangi, Saba, Howard, Emma, Human, Benjamin, Hunter, Harriet, Hussain, Ifrah, Hussain, Sami, Jackson-Taylor, Richard Thomas, Jacob-Ramsdale, Bronwen, Janjuha, Ryan, Jawad, Saleh, Jelani, Muzzamil, Johnston, David, Jones, Mike, Kalidindi, Sadhana, Kalsi, Savraj, Kalyanasundaram, Asanish, Kane, Anna, Kaur, Sahaj, Al-Othman, Othman Khaled, Khan, Qaisar, Khullar, Sajan, Kirkland, Priscilla, Lawrence-Smith, Hannah, Leeson, Charlotte, Lenaerts, Julius Elisabeth Richard, Long, Kerry, Lubbock, Simon, Burrell, Jamie Mac Donald, Maguire, Rachel, Mahendran, Praveen, Majeed, Saad, Malhotra, Prabhjot Singh, Mandagere, Vinay, Mantelakis, Angelos, McGovern, Sophie, Mosuro, Anjola, Moxley, Adam, Mustoe, Sophie, Myers, Sam, Nadeem, Kiran, Nasseri, Reza, Newman, Tom, Nzewi, Richard, Ogborne, Rosalie, Omatseye, Joyce, Paddock, Sophie, Parkin, James, Patel, Mohit, Pawar, Sohini, Pearce, Stuart, Penrice, Samuel, Purdy, Julian, Ramjan, Raisa, Randhawa, Ratan, Rasul, Usman, Raymond-Taggert, Elliot, Razey, Rebecca, Razzaghi, Carmel, Reel, Eimear, Revell, Elliot John, Rigbye, Joanna, Rotimi, Oloruntobi, Said, Abdelrahman, Sanders, Emma, Sangal, Pranoy, Grandal, Nora Sangvik, Shah, Aadam, Shah, Rahul Atul, Shotton, Oliver, Sims, Daniel, Smart, Katie, Smith, Martha Amy, Smith, Nick, Sopian, Aninditya Salma, South, Matthew, Speller, Jessica, Syer, Tom J., Ta, Ngan Hong, Tadross, Daniel, Thompson, Benjamin, Trevett, Jess, Tyler, Matthew, Ullah, Roshan, Utukuri, Mrudula, Vadera, Shree, Van Den Tooren, Harriet, Venturini, Sara, Vijayakumar, Aradhya, Vine, Melanie, Wellbelove, Zoe, Wittner, Liora, Yong, Geoffrey Hong Kiat, Ziyada, Farris, and McManus, I. C.

Published
2020
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9. Which computable biomedical knowledge objects will be regulated? Results of a UK workshop discussing the regulation of knowledge libraries and software as a medical device

Author

Wyatt, Jeremy C., Scott, Philip, Ordish, Johan, South, Matthew, Thomas, Mark, Jones, Caroline, Lacey‐Bryant, Sue, Wyatt, Jeremy C., Scott, Philip, Ordish, Johan, South, Matthew, Thomas, Mark, Jones, Caroline, and Lacey‐Bryant, Sue

Abstract

Introduction: To understand when knowledge objects in a computable biomedical knowledge library are likely to be subject to regulation as a medical device in the United Kingdom. Methods: A briefing paper was circulated to a multi‐disciplinary group of 25 including regulators, lawyers and others with insights into device regulation. A 1‐day workshop was convened to discuss questions relating to our aim. A discussion paper was drafted by lead authors and circulated to other authors for their comments and contributions. Results: This article reports on those deliberations and describes how UK device regulators are likely to treat the different kinds of knowledge objects that may be stored in computable biomedical knowledge libraries. While our focus is the likely approach of UK regulators, our analogies and analysis will also be relevant to the approaches taken by regulators elsewhere. We include a table examining the implications for each of the four knowledge levels described by Boxwala in 2011 and propose an additional level. Conclusions: If a knowledge object is described as directly executable for a medical purpose to provide decision support, it will generally be in scope of UK regulation as “software as a medical device.” However, if the knowledge object consists of an algorithm, a ruleset, pseudocode or some other representation that is not directly executable and whose developers make no claim that it can be used for a medical purpose, it is not likely to be subject to regulation. We expect similar reasoning to be applied by regulators in other countries.

Published
2023

10. Modelling clinical narrative as computable knowledge: The NICE computable implementation guidance project

Author

Scott, Philip, Heigl, Michaela, McCay, Charles, Shepperdson, Polly, Lima‐Walton, Elia, Andrikopoulou, Elisavet, Brunnhuber, Klara, Cornelius, Gary, Faulding, Susan, McAlister, Ben, Rowark, Shaun, South, Matthew, Thomas, Mark R., Whatling, Justin, Williams, John, Wyatt, Jeremy C., Greaves, Felix, Scott, Philip, Heigl, Michaela, McCay, Charles, Shepperdson, Polly, Lima‐Walton, Elia, Andrikopoulou, Elisavet, Brunnhuber, Klara, Cornelius, Gary, Faulding, Susan, McAlister, Ben, Rowark, Shaun, South, Matthew, Thomas, Mark R., Whatling, Justin, Williams, John, Wyatt, Jeremy C., and Greaves, Felix

Abstract

Introduction: Translating narrative clinical guidelines to computable knowledge is a long‐standing challenge that has seen a diverse range of approaches. The UK National Institute for Health and Care Excellence (NICE) Content Advisory Board (CAB) aims ultimately to (1) guide clinical decision support and other software developers to increase traceability, fidelity and consistency in supporting clinical use of NICE recommendations, (2) guide local practice audit and intervention to reduce unwarranted variation, (3) provide feedback to NICE on how future recommendations should be developed. Objectives: The first phase of work was to explore a range of technical approaches to transition NICE toward the production of natively digital content. Methods: Following an initial ‘collaborathon’ in November 2022, the NICE Computable Implementation Guidance project (NCIG) was established. We held a series of workstream calls approximately fortnightly, focusing on (1) user stories and trigger events, (2) information model and definitions, (3) horizon‐scanning and output format. A second collaborathon was held in March 2023 to consolidate progress across the workstreams and agree residual actions to complete. Results: While we initially focussed on technical implementation standards, we decided that an intermediate logical model was a more achievable first step in the journey from narrative to fully computable representation. NCIG adopted the WHO Digital Adaptation Kit (DAK) as a technology‐agnostic method to model user scenarios, personae, processes and workflow, core data elements and decision‐support logic. Further work will address indicators, such as prescribing compliance, and implementation in document templates for primary care patient record systems. Conclusions: The project has shown that the WHO DAK, with some modification, is a promising approach to build technology‐neutral logical specifications of NICE recommendations. Implementation of concurrent computable modellin

Published
2023

13. Patient-reported Outcomes: the ICHOM Standard Set for Inflammatory Bowel Disease in Real-life Practice Helps Quantify Deficits in Current Care

Author

Wong, Darren, primary, Matini, Lawrence, additional, Kormilitzin, Andrey, additional, Kantschuster, Ramona, additional, Simadibrata, Daniel Martin, additional, Lyden, Sara, additional, Wilson, Jean, additional, Brain, Oliver A, additional, Palmer, Rebecca, additional, Ambrose, Tim, additional, Satsangi, Jack, additional, South, Matthew, additional, Geddes, John, additional, Bodger, Keith, additional, Travis, Simon P L, additional, and Walsh, Alissa, additional

Published
2022
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16. Using Item Response Theory (IRT) to Improve the Efficiency of the Simple Clinical Colitis Activity Index (SCCAI) for patients with ulcerative colitis

Author

Walsh, Alissa, primary, Cao, Rena, additional, Wong, Darren, additional, Kantschuster, Ramona, additional, Matini, Lawrence, additional, Wilson, Jean, additional, Cohort, Oxford IBD, additional, Kormilitzin, Andrey, additional, South, Matthew, additional, Travis, Simon, additional, and Bauermeister, Sarah, additional

Published
2020
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21. Additional file 4 of Exploring UK medical school differences: the MedDifs study of selection, teaching, student and F1 perceptions, postgraduate outcomes and fitness to practise

Author

McManus, I. C., Harborne, Andrew Christopher, Horsfall, Hugo Layard, Joseph, Tobin, Smith, Daniel T., Marshall-Andon, Tess, Samuels, Ryan, Kearsley, Joshua William, Abbas, Nadine, Baig, Hassan, Beecham, Joseph, Benons, Natasha, Caird, Charlie, Clark, Ryan, Cope, Thomas, Coultas, James, Debenham, Luke, Douglas, Sarah, Eldridge, Jack, Hughes-Gooding, Thomas, Jakubowska, Agnieszka, Jones, Oliver, Lancaster, Eve, MacMillan, Calum, McAllister, Ross, Merzougui, Wassim, Phillips, Ben, Phillips, Simon, Risk, Omar, Sage, Adam, Sooltangos, Aisha, Spencer, Robert, Tajbakhsh, Roxanne, Adesalu, Oluseyi, Aganin, Ivan, Ahmed, Ammar, Aiken, Katherine, Alimatu-Sadia Akeredolu, Alam, Ibrahim, Ali, Aamna, Anderson, Richard, Ang, Jia Jun, Anis, Fady Sameh, Sonam Aojula, Arthur, Catherine, Ashby, Alena, Ashraf, Ahmed, Aspinall, Emma, Awad, Mark, Yahaya, Abdul-Muiz Azri, Badhrinarayanan, Shreya, Bandyopadhyay, Soham, Barnes, Sam, Bassey-Duke, Daisy, Boreham, Charlotte, Braine, Rebecca, Brandreth, Joseph, Carrington, Zoe, Cashin, Zoe, Shaunak Chatterjee, Chawla, Mehar, Chean, Chung Shen, Clements, Chris, Clough, Richard, Coulthurst, Jessica, Curry, Liam, Daniels, Vinnie Christine, Davies, Simon, Davis, Rebecca, Waal, Hanelie De, Desai, Nasreen, Douglas, Hannah, Druce, James, Ejamike, Lady-Namera, Esere, Meron, Eyre, Alex, Fazmin, Ibrahim Talal, Fitzgerald-Smith, Sophia, Ford, Verity, Freeston, Sarah, Garnett, Katherine, General, Whitney, Gilbert, Helen, Gowie, Zein, Grafton-Clarke, Ciaran, Gudka, Keshni, Gumber, Leher, Gupta, Rishi, Harlow, Chris, Harrington, Amy, Heaney, Adele, Ho, Wing Hang Serene, Holloway, Lucy, Hood, Christina, Houghton, Eleanor, Houshangi, Saba, Howard, Emma, Human, Benjamin, Hunter, Harriet, Ifrah Hussain, Hussain, Sami, Jackson-Taylor, Richard Thomas, Jacob-Ramsdale, Bronwen, Janjuha, Ryan, Jawad, Saleh, Jelani, Muzzamil, Johnston, David, Jones, Mike, Kalidindi, Sadhana, Kalsi, Savraj, Kalyanasundaram, Asanish, Kane, Anna, Kaur, Sahaj, Al-Othman, Othman Khaled, Khan, Qaisar, Khullar, Sajan, Kirkland, Priscilla, Lawrence-Smith, Hannah, Leeson, Charlotte, Lenaerts, Julius Elisabeth Richard, Long, Kerry, Lubbock, Simon, Burrell, Jamie Mac Donald, Maguire, Rachel, Mahendran, Praveen, Majeed, Saad, Malhotra, Prabhjot Singh, Mandagere, Vinay, Mantelakis, Angelos, McGovern, Sophie, Mosuro, Anjola, Moxley, Adam, Mustoe, Sophie, Myers, Sam, Nadeem, Kiran, Nasseri, Reza, Newman, Tom, Nzewi, Richard, Ogborne, Rosalie, Omatseye, Joyce, Paddock, Sophie, Parkin, James, Patel, Mohit, Pawar, Sohini, Pearce, Stuart, Penrice, Samuel, Purdy, Julian, Ramjan, Raisa, Randhawa, Ratan, Rasul, Usman, Raymond-Taggert, Elliot, Razey, Rebecca, Razzaghi, Carmel, Reel, Eimear, Revell, Elliot John, Rigbye, Joanna, Rotimi, Oloruntobi, Said, Abdelrahman, Sanders, Emma, Sangal, Pranoy, Grandal, Nora Sangvik, Shah, Aadam, Shah, Rahul Atul, Shotton, Oliver, Sims, Daniel, Smart, Katie, Smith, Martha Amy, Smith, Nick, Aninditya Salma Sopian, South, Matthew, Speller, Jessica, Syer, Tom J., Ta, Ngan Hong, Tadross, Daniel, Thompson, Benjamin, Trevett, Jess, Tyler, Matthew, Ullah, Roshan, Utukuri, Mrudula, Vadera, Shree, Tooren, Harriet Van Den, Venturini, Sara, Vijayakumar, Aradhya, Vine, Melanie, Wellbelove, Zoe, Wittner, Liora, Yong, Geoffrey Hong Kiat, Ziyada, Farris, and Devine, Oliver Patrick

Abstract

Additional file 4. Graphs 1 to 210 (pages 1 to 35).

Published
2020
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22. Additional file 7 of Exploring UK medical school differences: the MedDifs study of selection, teaching, student and F1 perceptions, postgraduate outcomes and fitness to practise

Author

McManus, I. C., Harborne, Andrew Christopher, Horsfall, Hugo Layard, Joseph, Tobin, Smith, Daniel T., Marshall-Andon, Tess, Samuels, Ryan, Kearsley, Joshua William, Abbas, Nadine, Baig, Hassan, Beecham, Joseph, Benons, Natasha, Caird, Charlie, Clark, Ryan, Cope, Thomas, Coultas, James, Debenham, Luke, Douglas, Sarah, Eldridge, Jack, Hughes-Gooding, Thomas, Jakubowska, Agnieszka, Jones, Oliver, Lancaster, Eve, MacMillan, Calum, McAllister, Ross, Merzougui, Wassim, Phillips, Ben, Phillips, Simon, Risk, Omar, Sage, Adam, Sooltangos, Aisha, Spencer, Robert, Tajbakhsh, Roxanne, Adesalu, Oluseyi, Aganin, Ivan, Ahmed, Ammar, Aiken, Katherine, Alimatu-Sadia Akeredolu, Alam, Ibrahim, Ali, Aamna, Anderson, Richard, Ang, Jia Jun, Anis, Fady Sameh, Sonam Aojula, Arthur, Catherine, Ashby, Alena, Ashraf, Ahmed, Aspinall, Emma, Awad, Mark, Yahaya, Abdul-Muiz Azri, Badhrinarayanan, Shreya, Bandyopadhyay, Soham, Barnes, Sam, Bassey-Duke, Daisy, Boreham, Charlotte, Braine, Rebecca, Brandreth, Joseph, Carrington, Zoe, Cashin, Zoe, Shaunak Chatterjee, Chawla, Mehar, Chean, Chung Shen, Clements, Chris, Clough, Richard, Coulthurst, Jessica, Curry, Liam, Daniels, Vinnie Christine, Davies, Simon, Davis, Rebecca, Waal, Hanelie De, Desai, Nasreen, Douglas, Hannah, Druce, James, Ejamike, Lady-Namera, Esere, Meron, Eyre, Alex, Fazmin, Ibrahim Talal, Fitzgerald-Smith, Sophia, Ford, Verity, Freeston, Sarah, Garnett, Katherine, General, Whitney, Gilbert, Helen, Gowie, Zein, Grafton-Clarke, Ciaran, Gudka, Keshni, Gumber, Leher, Gupta, Rishi, Harlow, Chris, Harrington, Amy, Heaney, Adele, Ho, Wing Hang Serene, Holloway, Lucy, Hood, Christina, Houghton, Eleanor, Houshangi, Saba, Howard, Emma, Human, Benjamin, Hunter, Harriet, Ifrah Hussain, Hussain, Sami, Jackson-Taylor, Richard Thomas, Jacob-Ramsdale, Bronwen, Janjuha, Ryan, Jawad, Saleh, Jelani, Muzzamil, Johnston, David, Jones, Mike, Kalidindi, Sadhana, Kalsi, Savraj, Kalyanasundaram, Asanish, Kane, Anna, Kaur, Sahaj, Al-Othman, Othman Khaled, Khan, Qaisar, Khullar, Sajan, Kirkland, Priscilla, Lawrence-Smith, Hannah, Leeson, Charlotte, Lenaerts, Julius Elisabeth Richard, Long, Kerry, Lubbock, Simon, Burrell, Jamie Mac Donald, Maguire, Rachel, Mahendran, Praveen, Majeed, Saad, Malhotra, Prabhjot Singh, Mandagere, Vinay, Mantelakis, Angelos, McGovern, Sophie, Mosuro, Anjola, Moxley, Adam, Mustoe, Sophie, Myers, Sam, Nadeem, Kiran, Nasseri, Reza, Newman, Tom, Nzewi, Richard, Ogborne, Rosalie, Omatseye, Joyce, Paddock, Sophie, Parkin, James, Patel, Mohit, Pawar, Sohini, Pearce, Stuart, Penrice, Samuel, Purdy, Julian, Ramjan, Raisa, Randhawa, Ratan, Rasul, Usman, Raymond-Taggert, Elliot, Razey, Rebecca, Razzaghi, Carmel, Reel, Eimear, Revell, Elliot John, Rigbye, Joanna, Rotimi, Oloruntobi, Said, Abdelrahman, Sanders, Emma, Sangal, Pranoy, Grandal, Nora Sangvik, Shah, Aadam, Shah, Rahul Atul, Shotton, Oliver, Sims, Daniel, Smart, Katie, Smith, Martha Amy, Smith, Nick, Aninditya Salma Sopian, South, Matthew, Speller, Jessica, Syer, Tom J., Ta, Ngan Hong, Tadross, Daniel, Thompson, Benjamin, Trevett, Jess, Tyler, Matthew, Ullah, Roshan, Utukuri, Mrudula, Vadera, Shree, Tooren, Harriet Van Den, Venturini, Sara, Vijayakumar, Aradhya, Vine, Melanie, Wellbelove, Zoe, Wittner, Liora, Yong, Geoffrey Hong Kiat, Ziyada, Farris, and Devine, Oliver Patrick

Abstract

Additional file 7. Graphs 631 to 840 (pages 106 to 140).

Published
2020
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23. Additional file 6 of Exploring UK medical school differences: the MedDifs study of selection, teaching, student and F1 perceptions, postgraduate outcomes and fitness to practise

Author

McManus, I. C., Harborne, Andrew Christopher, Horsfall, Hugo Layard, Joseph, Tobin, Smith, Daniel T., Marshall-Andon, Tess, Samuels, Ryan, Kearsley, Joshua William, Abbas, Nadine, Baig, Hassan, Beecham, Joseph, Benons, Natasha, Caird, Charlie, Clark, Ryan, Cope, Thomas, Coultas, James, Debenham, Luke, Douglas, Sarah, Eldridge, Jack, Hughes-Gooding, Thomas, Jakubowska, Agnieszka, Jones, Oliver, Lancaster, Eve, MacMillan, Calum, McAllister, Ross, Merzougui, Wassim, Phillips, Ben, Phillips, Simon, Risk, Omar, Sage, Adam, Sooltangos, Aisha, Spencer, Robert, Tajbakhsh, Roxanne, Adesalu, Oluseyi, Aganin, Ivan, Ahmed, Ammar, Aiken, Katherine, Alimatu-Sadia Akeredolu, Alam, Ibrahim, Ali, Aamna, Anderson, Richard, Ang, Jia Jun, Anis, Fady Sameh, Sonam Aojula, Arthur, Catherine, Ashby, Alena, Ashraf, Ahmed, Aspinall, Emma, Awad, Mark, Yahaya, Abdul-Muiz Azri, Badhrinarayanan, Shreya, Bandyopadhyay, Soham, Barnes, Sam, Bassey-Duke, Daisy, Boreham, Charlotte, Braine, Rebecca, Brandreth, Joseph, Carrington, Zoe, Cashin, Zoe, Shaunak Chatterjee, Chawla, Mehar, Chean, Chung Shen, Clements, Chris, Clough, Richard, Coulthurst, Jessica, Curry, Liam, Daniels, Vinnie Christine, Davies, Simon, Davis, Rebecca, Waal, Hanelie De, Desai, Nasreen, Douglas, Hannah, Druce, James, Ejamike, Lady-Namera, Esere, Meron, Eyre, Alex, Fazmin, Ibrahim Talal, Fitzgerald-Smith, Sophia, Ford, Verity, Freeston, Sarah, Garnett, Katherine, General, Whitney, Gilbert, Helen, Gowie, Zein, Grafton-Clarke, Ciaran, Gudka, Keshni, Gumber, Leher, Gupta, Rishi, Harlow, Chris, Harrington, Amy, Heaney, Adele, Ho, Wing Hang Serene, Holloway, Lucy, Hood, Christina, Houghton, Eleanor, Houshangi, Saba, Howard, Emma, Human, Benjamin, Hunter, Harriet, Ifrah Hussain, Hussain, Sami, Jackson-Taylor, Richard Thomas, Jacob-Ramsdale, Bronwen, Janjuha, Ryan, Jawad, Saleh, Jelani, Muzzamil, Johnston, David, Jones, Mike, Kalidindi, Sadhana, Kalsi, Savraj, Kalyanasundaram, Asanish, Kane, Anna, Kaur, Sahaj, Al-Othman, Othman Khaled, Khan, Qaisar, Khullar, Sajan, Kirkland, Priscilla, Lawrence-Smith, Hannah, Leeson, Charlotte, Lenaerts, Julius Elisabeth Richard, Long, Kerry, Lubbock, Simon, Burrell, Jamie Mac Donald, Maguire, Rachel, Mahendran, Praveen, Majeed, Saad, Malhotra, Prabhjot Singh, Mandagere, Vinay, Mantelakis, Angelos, McGovern, Sophie, Mosuro, Anjola, Moxley, Adam, Mustoe, Sophie, Myers, Sam, Nadeem, Kiran, Nasseri, Reza, Newman, Tom, Nzewi, Richard, Ogborne, Rosalie, Omatseye, Joyce, Paddock, Sophie, Parkin, James, Patel, Mohit, Pawar, Sohini, Pearce, Stuart, Penrice, Samuel, Purdy, Julian, Ramjan, Raisa, Randhawa, Ratan, Rasul, Usman, Raymond-Taggert, Elliot, Razey, Rebecca, Razzaghi, Carmel, Reel, Eimear, Revell, Elliot John, Rigbye, Joanna, Rotimi, Oloruntobi, Said, Abdelrahman, Sanders, Emma, Sangal, Pranoy, Grandal, Nora Sangvik, Shah, Aadam, Shah, Rahul Atul, Shotton, Oliver, Sims, Daniel, Smart, Katie, Smith, Martha Amy, Smith, Nick, Aninditya Salma Sopian, South, Matthew, Speller, Jessica, Syer, Tom J., Ta, Ngan Hong, Tadross, Daniel, Thompson, Benjamin, Trevett, Jess, Tyler, Matthew, Ullah, Roshan, Utukuri, Mrudula, Vadera, Shree, Tooren, Harriet Van Den, Venturini, Sara, Vijayakumar, Aradhya, Vine, Melanie, Wellbelove, Zoe, Wittner, Liora, Yong, Geoffrey Hong Kiat, Ziyada, Farris, and Devine, Oliver Patrick

Abstract

Additional file 6. Graphs 421 to 630 (pages 71 to 105).

Published
2020
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24. Additional file 8 of Exploring UK medical school differences: the MedDifs study of selection, teaching, student and F1 perceptions, postgraduate outcomes and fitness to practise

Author

McManus, I. C., Harborne, Andrew Christopher, Horsfall, Hugo Layard, Joseph, Tobin, Smith, Daniel T., Marshall-Andon, Tess, Samuels, Ryan, Kearsley, Joshua William, Abbas, Nadine, Baig, Hassan, Beecham, Joseph, Benons, Natasha, Caird, Charlie, Clark, Ryan, Cope, Thomas, Coultas, James, Debenham, Luke, Douglas, Sarah, Eldridge, Jack, Hughes-Gooding, Thomas, Jakubowska, Agnieszka, Jones, Oliver, Lancaster, Eve, MacMillan, Calum, McAllister, Ross, Merzougui, Wassim, Phillips, Ben, Phillips, Simon, Risk, Omar, Sage, Adam, Sooltangos, Aisha, Spencer, Robert, Tajbakhsh, Roxanne, Adesalu, Oluseyi, Aganin, Ivan, Ahmed, Ammar, Aiken, Katherine, Alimatu-Sadia Akeredolu, Alam, Ibrahim, Ali, Aamna, Anderson, Richard, Ang, Jia Jun, Anis, Fady Sameh, Sonam Aojula, Arthur, Catherine, Ashby, Alena, Ashraf, Ahmed, Aspinall, Emma, Awad, Mark, Yahaya, Abdul-Muiz Azri, Badhrinarayanan, Shreya, Bandyopadhyay, Soham, Barnes, Sam, Bassey-Duke, Daisy, Boreham, Charlotte, Braine, Rebecca, Brandreth, Joseph, Carrington, Zoe, Cashin, Zoe, Shaunak Chatterjee, Chawla, Mehar, Chean, Chung Shen, Clements, Chris, Clough, Richard, Coulthurst, Jessica, Curry, Liam, Daniels, Vinnie Christine, Davies, Simon, Davis, Rebecca, Waal, Hanelie De, Desai, Nasreen, Douglas, Hannah, Druce, James, Ejamike, Lady-Namera, Esere, Meron, Eyre, Alex, Fazmin, Ibrahim Talal, Fitzgerald-Smith, Sophia, Ford, Verity, Freeston, Sarah, Garnett, Katherine, General, Whitney, Gilbert, Helen, Gowie, Zein, Grafton-Clarke, Ciaran, Gudka, Keshni, Gumber, Leher, Gupta, Rishi, Harlow, Chris, Harrington, Amy, Heaney, Adele, Ho, Wing Hang Serene, Holloway, Lucy, Hood, Christina, Houghton, Eleanor, Houshangi, Saba, Howard, Emma, Human, Benjamin, Hunter, Harriet, Ifrah Hussain, Hussain, Sami, Jackson-Taylor, Richard Thomas, Jacob-Ramsdale, Bronwen, Janjuha, Ryan, Jawad, Saleh, Jelani, Muzzamil, Johnston, David, Jones, Mike, Kalidindi, Sadhana, Kalsi, Savraj, Kalyanasundaram, Asanish, Kane, Anna, Kaur, Sahaj, Al-Othman, Othman Khaled, Khan, Qaisar, Khullar, Sajan, Kirkland, Priscilla, Lawrence-Smith, Hannah, Leeson, Charlotte, Lenaerts, Julius Elisabeth Richard, Long, Kerry, Lubbock, Simon, Burrell, Jamie Mac Donald, Maguire, Rachel, Mahendran, Praveen, Majeed, Saad, Malhotra, Prabhjot Singh, Mandagere, Vinay, Mantelakis, Angelos, McGovern, Sophie, Mosuro, Anjola, Moxley, Adam, Mustoe, Sophie, Myers, Sam, Nadeem, Kiran, Nasseri, Reza, Newman, Tom, Nzewi, Richard, Ogborne, Rosalie, Omatseye, Joyce, Paddock, Sophie, Parkin, James, Patel, Mohit, Pawar, Sohini, Pearce, Stuart, Penrice, Samuel, Purdy, Julian, Ramjan, Raisa, Randhawa, Ratan, Rasul, Usman, Raymond-Taggert, Elliot, Razey, Rebecca, Razzaghi, Carmel, Reel, Eimear, Revell, Elliot John, Rigbye, Joanna, Rotimi, Oloruntobi, Said, Abdelrahman, Sanders, Emma, Sangal, Pranoy, Grandal, Nora Sangvik, Shah, Aadam, Shah, Rahul Atul, Shotton, Oliver, Sims, Daniel, Smart, Katie, Smith, Martha Amy, Smith, Nick, Aninditya Salma Sopian, South, Matthew, Speller, Jessica, Syer, Tom J., Ta, Ngan Hong, Tadross, Daniel, Thompson, Benjamin, Trevett, Jess, Tyler, Matthew, Ullah, Roshan, Utukuri, Mrudula, Vadera, Shree, Tooren, Harriet Van Den, Venturini, Sara, Vijayakumar, Aradhya, Vine, Melanie, Wellbelove, Zoe, Wittner, Liora, Yong, Geoffrey Hong Kiat, Ziyada, Farris, and Devine, Oliver Patrick

Abstract

Additional file 8. Graphs 841 to 1050 (pages 141 to 175).

Published
2020
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25. Additional file 1 of Exploring UK medical school differences: the MedDifs study of selection, teaching, student and F1 perceptions, postgraduate outcomes and fitness to practise

Author

McManus, I. C., Harborne, Andrew Christopher, Horsfall, Hugo Layard, Joseph, Tobin, Smith, Daniel T., Marshall-Andon, Tess, Samuels, Ryan, Kearsley, Joshua William, Abbas, Nadine, Baig, Hassan, Beecham, Joseph, Benons, Natasha, Caird, Charlie, Clark, Ryan, Cope, Thomas, Coultas, James, Debenham, Luke, Douglas, Sarah, Eldridge, Jack, Hughes-Gooding, Thomas, Jakubowska, Agnieszka, Jones, Oliver, Lancaster, Eve, MacMillan, Calum, McAllister, Ross, Merzougui, Wassim, Phillips, Ben, Phillips, Simon, Risk, Omar, Sage, Adam, Sooltangos, Aisha, Spencer, Robert, Tajbakhsh, Roxanne, Adesalu, Oluseyi, Aganin, Ivan, Ahmed, Ammar, Aiken, Katherine, Alimatu-Sadia Akeredolu, Alam, Ibrahim, Ali, Aamna, Anderson, Richard, Ang, Jia Jun, Anis, Fady Sameh, Sonam Aojula, Arthur, Catherine, Ashby, Alena, Ashraf, Ahmed, Aspinall, Emma, Awad, Mark, Yahaya, Abdul-Muiz Azri, Badhrinarayanan, Shreya, Bandyopadhyay, Soham, Barnes, Sam, Bassey-Duke, Daisy, Boreham, Charlotte, Braine, Rebecca, Brandreth, Joseph, Carrington, Zoe, Cashin, Zoe, Shaunak Chatterjee, Chawla, Mehar, Chean, Chung Shen, Clements, Chris, Clough, Richard, Coulthurst, Jessica, Curry, Liam, Daniels, Vinnie Christine, Davies, Simon, Davis, Rebecca, Waal, Hanelie De, Desai, Nasreen, Douglas, Hannah, Druce, James, Ejamike, Lady-Namera, Esere, Meron, Eyre, Alex, Fazmin, Ibrahim Talal, Fitzgerald-Smith, Sophia, Ford, Verity, Freeston, Sarah, Garnett, Katherine, General, Whitney, Gilbert, Helen, Gowie, Zein, Grafton-Clarke, Ciaran, Gudka, Keshni, Gumber, Leher, Gupta, Rishi, Harlow, Chris, Harrington, Amy, Heaney, Adele, Ho, Wing Hang Serene, Holloway, Lucy, Hood, Christina, Houghton, Eleanor, Houshangi, Saba, Howard, Emma, Human, Benjamin, Hunter, Harriet, Ifrah Hussain, Hussain, Sami, Jackson-Taylor, Richard Thomas, Jacob-Ramsdale, Bronwen, Janjuha, Ryan, Jawad, Saleh, Jelani, Muzzamil, Johnston, David, Jones, Mike, Kalidindi, Sadhana, Kalsi, Savraj, Kalyanasundaram, Asanish, Kane, Anna, Kaur, Sahaj, Al-Othman, Othman Khaled, Khan, Qaisar, Khullar, Sajan, Kirkland, Priscilla, Lawrence-Smith, Hannah, Leeson, Charlotte, Lenaerts, Julius Elisabeth Richard, Long, Kerry, Lubbock, Simon, Burrell, Jamie Mac Donald, Maguire, Rachel, Mahendran, Praveen, Majeed, Saad, Malhotra, Prabhjot Singh, Mandagere, Vinay, Mantelakis, Angelos, McGovern, Sophie, Mosuro, Anjola, Moxley, Adam, Mustoe, Sophie, Myers, Sam, Nadeem, Kiran, Nasseri, Reza, Newman, Tom, Nzewi, Richard, Ogborne, Rosalie, Omatseye, Joyce, Paddock, Sophie, Parkin, James, Patel, Mohit, Pawar, Sohini, Pearce, Stuart, Penrice, Samuel, Purdy, Julian, Ramjan, Raisa, Randhawa, Ratan, Rasul, Usman, Raymond-Taggert, Elliot, Razey, Rebecca, Razzaghi, Carmel, Reel, Eimear, Revell, Elliot John, Rigbye, Joanna, Rotimi, Oloruntobi, Said, Abdelrahman, Sanders, Emma, Sangal, Pranoy, Grandal, Nora Sangvik, Shah, Aadam, Shah, Rahul Atul, Shotton, Oliver, Sims, Daniel, Smart, Katie, Smith, Martha Amy, Smith, Nick, Aninditya Salma Sopian, South, Matthew, Speller, Jessica, Syer, Tom J., Ta, Ngan Hong, Tadross, Daniel, Thompson, Benjamin, Trevett, Jess, Tyler, Matthew, Ullah, Roshan, Utukuri, Mrudula, Vadera, Shree, Tooren, Harriet Van Den, Venturini, Sara, Vijayakumar, Aradhya, Vine, Melanie, Wellbelove, Zoe, Wittner, Liora, Yong, Geoffrey Hong Kiat, Ziyada, Farris, and Devine, Oliver Patrick

Abstract

Additional file 1. Figures S1 to S10, and additional text on the causal ordering of variables.

Published
2020
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26. Additional file 5 of Exploring UK medical school differences: the MedDifs study of selection, teaching, student and F1 perceptions, postgraduate outcomes and fitness to practise

Author

McManus, I. C., Harborne, Andrew Christopher, Horsfall, Hugo Layard, Joseph, Tobin, Smith, Daniel T., Marshall-Andon, Tess, Samuels, Ryan, Kearsley, Joshua William, Abbas, Nadine, Baig, Hassan, Beecham, Joseph, Benons, Natasha, Caird, Charlie, Clark, Ryan, Cope, Thomas, Coultas, James, Debenham, Luke, Douglas, Sarah, Eldridge, Jack, Hughes-Gooding, Thomas, Jakubowska, Agnieszka, Jones, Oliver, Lancaster, Eve, MacMillan, Calum, McAllister, Ross, Merzougui, Wassim, Phillips, Ben, Phillips, Simon, Risk, Omar, Sage, Adam, Sooltangos, Aisha, Spencer, Robert, Tajbakhsh, Roxanne, Adesalu, Oluseyi, Aganin, Ivan, Ahmed, Ammar, Aiken, Katherine, Alimatu-Sadia Akeredolu, Alam, Ibrahim, Ali, Aamna, Anderson, Richard, Ang, Jia Jun, Anis, Fady Sameh, Sonam Aojula, Arthur, Catherine, Ashby, Alena, Ashraf, Ahmed, Aspinall, Emma, Awad, Mark, Yahaya, Abdul-Muiz Azri, Badhrinarayanan, Shreya, Bandyopadhyay, Soham, Barnes, Sam, Bassey-Duke, Daisy, Boreham, Charlotte, Braine, Rebecca, Brandreth, Joseph, Carrington, Zoe, Cashin, Zoe, Shaunak Chatterjee, Chawla, Mehar, Chean, Chung Shen, Clements, Chris, Clough, Richard, Coulthurst, Jessica, Curry, Liam, Daniels, Vinnie Christine, Davies, Simon, Davis, Rebecca, Waal, Hanelie De, Desai, Nasreen, Douglas, Hannah, Druce, James, Ejamike, Lady-Namera, Esere, Meron, Eyre, Alex, Fazmin, Ibrahim Talal, Fitzgerald-Smith, Sophia, Ford, Verity, Freeston, Sarah, Garnett, Katherine, General, Whitney, Gilbert, Helen, Gowie, Zein, Grafton-Clarke, Ciaran, Gudka, Keshni, Gumber, Leher, Gupta, Rishi, Harlow, Chris, Harrington, Amy, Heaney, Adele, Ho, Wing Hang Serene, Holloway, Lucy, Hood, Christina, Houghton, Eleanor, Houshangi, Saba, Howard, Emma, Human, Benjamin, Hunter, Harriet, Ifrah Hussain, Hussain, Sami, Jackson-Taylor, Richard Thomas, Jacob-Ramsdale, Bronwen, Janjuha, Ryan, Jawad, Saleh, Jelani, Muzzamil, Johnston, David, Jones, Mike, Kalidindi, Sadhana, Kalsi, Savraj, Kalyanasundaram, Asanish, Kane, Anna, Kaur, Sahaj, Al-Othman, Othman Khaled, Khan, Qaisar, Khullar, Sajan, Kirkland, Priscilla, Lawrence-Smith, Hannah, Leeson, Charlotte, Lenaerts, Julius Elisabeth Richard, Long, Kerry, Lubbock, Simon, Burrell, Jamie Mac Donald, Maguire, Rachel, Mahendran, Praveen, Majeed, Saad, Malhotra, Prabhjot Singh, Mandagere, Vinay, Mantelakis, Angelos, McGovern, Sophie, Mosuro, Anjola, Moxley, Adam, Mustoe, Sophie, Myers, Sam, Nadeem, Kiran, Nasseri, Reza, Newman, Tom, Nzewi, Richard, Ogborne, Rosalie, Omatseye, Joyce, Paddock, Sophie, Parkin, James, Patel, Mohit, Pawar, Sohini, Pearce, Stuart, Penrice, Samuel, Purdy, Julian, Ramjan, Raisa, Randhawa, Ratan, Rasul, Usman, Raymond-Taggert, Elliot, Razey, Rebecca, Razzaghi, Carmel, Reel, Eimear, Revell, Elliot John, Rigbye, Joanna, Rotimi, Oloruntobi, Said, Abdelrahman, Sanders, Emma, Sangal, Pranoy, Grandal, Nora Sangvik, Shah, Aadam, Shah, Rahul Atul, Shotton, Oliver, Sims, Daniel, Smart, Katie, Smith, Martha Amy, Smith, Nick, Aninditya Salma Sopian, South, Matthew, Speller, Jessica, Syer, Tom J., Ta, Ngan Hong, Tadross, Daniel, Thompson, Benjamin, Trevett, Jess, Tyler, Matthew, Ullah, Roshan, Utukuri, Mrudula, Vadera, Shree, Tooren, Harriet Van Den, Venturini, Sara, Vijayakumar, Aradhya, Vine, Melanie, Wellbelove, Zoe, Wittner, Liora, Yong, Geoffrey Hong Kiat, Ziyada, Farris, and Devine, Oliver Patrick

Abstract

Additional file 5. Graphs 211 to 420 (pages 36 to 70).

Published
2020
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27. Additional file 3 of Exploring UK medical school differences: the MedDifs study of selection, teaching, student and F1 perceptions, postgraduate outcomes and fitness to practise

Author

McManus, I. C., Harborne, Andrew Christopher, Horsfall, Hugo Layard, Joseph, Tobin, Smith, Daniel T., Marshall-Andon, Tess, Samuels, Ryan, Kearsley, Joshua William, Abbas, Nadine, Baig, Hassan, Beecham, Joseph, Benons, Natasha, Caird, Charlie, Clark, Ryan, Cope, Thomas, Coultas, James, Debenham, Luke, Douglas, Sarah, Eldridge, Jack, Hughes-Gooding, Thomas, Jakubowska, Agnieszka, Jones, Oliver, Lancaster, Eve, MacMillan, Calum, McAllister, Ross, Merzougui, Wassim, Phillips, Ben, Phillips, Simon, Risk, Omar, Sage, Adam, Sooltangos, Aisha, Spencer, Robert, Tajbakhsh, Roxanne, Adesalu, Oluseyi, Aganin, Ivan, Ahmed, Ammar, Aiken, Katherine, Alimatu-Sadia Akeredolu, Alam, Ibrahim, Ali, Aamna, Anderson, Richard, Ang, Jia Jun, Anis, Fady Sameh, Sonam Aojula, Arthur, Catherine, Ashby, Alena, Ashraf, Ahmed, Aspinall, Emma, Awad, Mark, Yahaya, Abdul-Muiz Azri, Badhrinarayanan, Shreya, Bandyopadhyay, Soham, Barnes, Sam, Bassey-Duke, Daisy, Boreham, Charlotte, Braine, Rebecca, Brandreth, Joseph, Carrington, Zoe, Cashin, Zoe, Shaunak Chatterjee, Chawla, Mehar, Chean, Chung Shen, Clements, Chris, Clough, Richard, Coulthurst, Jessica, Curry, Liam, Daniels, Vinnie Christine, Davies, Simon, Davis, Rebecca, Waal, Hanelie De, Desai, Nasreen, Douglas, Hannah, Druce, James, Ejamike, Lady-Namera, Esere, Meron, Eyre, Alex, Fazmin, Ibrahim Talal, Fitzgerald-Smith, Sophia, Ford, Verity, Freeston, Sarah, Garnett, Katherine, General, Whitney, Gilbert, Helen, Gowie, Zein, Grafton-Clarke, Ciaran, Gudka, Keshni, Gumber, Leher, Gupta, Rishi, Harlow, Chris, Harrington, Amy, Heaney, Adele, Ho, Wing Hang Serene, Holloway, Lucy, Hood, Christina, Houghton, Eleanor, Houshangi, Saba, Howard, Emma, Human, Benjamin, Hunter, Harriet, Ifrah Hussain, Hussain, Sami, Jackson-Taylor, Richard Thomas, Jacob-Ramsdale, Bronwen, Janjuha, Ryan, Jawad, Saleh, Jelani, Muzzamil, Johnston, David, Jones, Mike, Kalidindi, Sadhana, Kalsi, Savraj, Kalyanasundaram, Asanish, Kane, Anna, Kaur, Sahaj, Al-Othman, Othman Khaled, Khan, Qaisar, Khullar, Sajan, Kirkland, Priscilla, Lawrence-Smith, Hannah, Leeson, Charlotte, Lenaerts, Julius Elisabeth Richard, Long, Kerry, Lubbock, Simon, Burrell, Jamie Mac Donald, Maguire, Rachel, Mahendran, Praveen, Majeed, Saad, Malhotra, Prabhjot Singh, Mandagere, Vinay, Mantelakis, Angelos, McGovern, Sophie, Mosuro, Anjola, Moxley, Adam, Mustoe, Sophie, Myers, Sam, Nadeem, Kiran, Nasseri, Reza, Newman, Tom, Nzewi, Richard, Ogborne, Rosalie, Omatseye, Joyce, Paddock, Sophie, Parkin, James, Patel, Mohit, Pawar, Sohini, Pearce, Stuart, Penrice, Samuel, Purdy, Julian, Ramjan, Raisa, Randhawa, Ratan, Rasul, Usman, Raymond-Taggert, Elliot, Razey, Rebecca, Razzaghi, Carmel, Reel, Eimear, Revell, Elliot John, Rigbye, Joanna, Rotimi, Oloruntobi, Said, Abdelrahman, Sanders, Emma, Sangal, Pranoy, Grandal, Nora Sangvik, Shah, Aadam, Shah, Rahul Atul, Shotton, Oliver, Sims, Daniel, Smart, Katie, Smith, Martha Amy, Smith, Nick, Aninditya Salma Sopian, South, Matthew, Speller, Jessica, Syer, Tom J., Ta, Ngan Hong, Tadross, Daniel, Thompson, Benjamin, Trevett, Jess, Tyler, Matthew, Ullah, Roshan, Utukuri, Mrudula, Vadera, Shree, Tooren, Harriet Van Den, Venturini, Sara, Vijayakumar, Aradhya, Vine, Melanie, Wellbelove, Zoe, Wittner, Liora, Yong, Geoffrey Hong Kiat, Ziyada, Farris, and Devine, Oliver Patrick

Abstract

Additional file 3. Index and notes on supplementary graphs 1 to 205 (page 1 to 205).

Published
2020
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29. The True Colours Remote Symptom Monitoring System: A Decade of Evolution (Preprint)

Author

Goodday, Sarah M, primary, Atkinson, Lauren, additional, Goodwin, Guy, additional, Saunders, Kate, additional, South, Matthew, additional, Mackay, Clare, additional, Denis, Mike, additional, Hinds, Chris, additional, Attenburrow, Mary-Jane, additional, Davies, Jim, additional, Welch, James, additional, Stevens, William, additional, Mansfield, Karen, additional, Suvilehto, Juulia, additional, and Geddes, John, additional

Published
2019
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31. Acute aortic dissection during pregnancy: Trials and tribulations.

Author

Rimmer, Lara, Heyward‐Chaplin, Jessica, South, Matthew, Gouda, Mohamed, and Bashir, Mohamad

Subjects
AORTIC dissection, MAGNETIC resonance imaging, COMPUTED tomography, TRANSESOPHAGEAL echocardiography, MEDICAL personnel, PREGNANCY
Abstract

Background: Type A acute aortic dissection (TAAD) during pregnancy is a life‐threatening event for both the mother and the unborn baby. Pregnancy has been recognized as an independent risk factor for TAAD, postulated to be due to physiological changes that cause hyperdynamic circulation. This review seeks to outline the current controversies around this unique group. Methods: A comprehensive literature search was carried out across large databases to assimilate relevant papers regarding acute aortic dissection in pregnant women. Results: The presentation can be atypical in many cases and further concern from clinicians of fetal radiation exposure can result in missed or delayed diagnoses. Investigation via the quickest form of imaging, whether computed tomography, magnetic resonance imaging, or transesophageal echocardiography, should be carried out promptly due to the high risk of mortality. Surgical management of TAAD in pregnancy revolves primarily around the decision to deliver the fetus concomitantly or to perform the aortic repair with the fetus in utero. Conclusions: Management of this group includes rapid and dynamic assessment without delay. From conception to postpartum, there are multiple stages in which to manage these women. Challenges in carrying out management in the form of operative techniques and cardiopulmonary bypass place the fetus at risk and must be approached with caution, particularly as there is little evidence‐base for many of these decisions. Further research into reducing maternal and fetal mortality is necessary. [ABSTRACT FROM AUTHOR]

Published
2021
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32. Dementias Platform UK (DPUK) Data Portal - World-leading infrastructure facilitating innovative multi-modal research

Author

Orton, Christopher, primary, Gallacher, John, additional, Lyons, Ronan, additional, Ford, David, additional, Thompson, Simon, additional, Mackay, Clare, additional, Williams, Julie, additional, Bauermeister, Sarah, additional, Menzies, Georgina, additional, South, Matthew, additional, Biddle, Justin, additional, Bale, Michael, additional, and North, Laura, additional

Published
2018
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33. Making the most of the waiting room: Electronic patient engagement, a mixed methods study

Author

Coathup, Victoria, Finlay, Teresa, Teare, Harriet J.A., Kaye, Jane, South, Matthew, Watt, Fiona E., Luqmani, Raashid, Coathup, Victoria, Finlay, Teresa, Teare, Harriet J.A., Kaye, Jane, South, Matthew, Watt, Fiona E., and Luqmani, Raashid

Abstract

Objective. The purpose of this study was to explore whether patients with musculoskeletal conditions would agree to use digital technologies to learn about research registries and make a decision about signing up whilst in the clinic waiting room. Methods. Patients were recruited from four hospital clinics across Oxfordshire. We used an explanatory mixed methods design with two sequential phases comprising an exploratory, cross-sectional questionnaire (n = 84), followed by focus group interviews (n = 8) to provide context for the findings from the questionnaire. Multivariate ordinal logistic regression models were used to explore relationships between patient preferences and characteristics. Thematic analysis was used to understand the reasons for patient preferences regarding digital technologies and research registries. Results. As participants' age increased, they were more likely to report a preference for face-to-face recruitment methods compared to those using digital technologies. Findings from the focus groups indicated this was primarily due to a fear of technology and physical limitations associated with a patient's condition. Patients also reported a preference for making a decision about signing up at a later date, which was attributed to patients feeling distracted whilst in the waiting room due to anxieties related to their upcoming appointment. Conclusions. Many patients with musculoskeletal conditions in the UK may be interested in learning about opportunities to participate in research whilst using digital technologies within the waiting room. The results suggest the need for choice regarding the presentation and format of information and whether it can be accessed at a later date at home.

Published
2018

34. Argumentation-based inference and decision making - a medical perspective

Author

Fox, John, Glasspool, David, Grecu, Dan, Modgil, Sanjay, South, Matthew, and Patkar, Vivek

Subjects
Inference -- Models, Medical laboratories -- Technology application, Decision-making -- Models, Technology application, Business, Computers, Computers and office automation industries, Electronics
Abstract

Argumentation-based services for biomedical applications are studied. Algorithms for evaluation of arguments under grounded and credulous acceptability semantics are developed.

Published
2007

39. Transitioning Advanced Concept Technology Demonstrations to acquisition programs

Author

South, Matthew T., Snider, Keith, Owen, Wally, and Product Development

Abstract

This thesis evaluated the Department of Defense's Advanced Concept Technology Demonstration (ACTD) process and the challenges encountered in transitioning an ACTD to an acquisition program. The methodology included case analyses of two ACTD programs that transitioned to acquisition programs to determine their levels of success. The scope included a review of: 1) ACTD origins and processes as of July 2002, 2) past ACTD programs, 3) the established documentation criteria associated with ACTD selection and evaluation, 4) the two ACTD case programs selected for analysis, 5) the apparent ACTD transition areas prone to success or failure, and 6) potential process improvements that would aid in ACTD transition to acquisition programs. This thesis identified both the strengths and the weaknesses in the existing ACTD transition process. The results indicated that several of the existing ACTD criteria should be maintained. However, it was also determined that several enhancements could be incorporated into future processes. It concluded with recommended improvements that would enhance the insertion of technology to the warfighter via the acquisition process. With the combination of current practices and implementation of these recommendations, ACTDs could become the cornerstone of the Secretary of Defense's new acquisition process. http://archive.org/details/transitioningadv109456142 GM-15, Department of the Navy Approved for public release; distribution is unlimited.

Published
2003

40. Transitioning Advanced Concept Technology Demonstrations to acquisition programs

Author

Snider, Keith, Owen, Wally, Product Development, South, Matthew T., Snider, Keith, Owen, Wally, Product Development, and South, Matthew T.

Abstract

This thesis evaluated the Department of Defense's Advanced Concept Technology Demonstration (ACTD) process and the challenges encountered in transitioning an ACTD to an acquisition program. The methodology included case analyses of two ACTD programs that transitioned to acquisition programs to determine their levels of success. The scope included a review of: 1) ACTD origins and processes as of July 2002, 2) past ACTD programs, 3) the established documentation criteria associated with ACTD selection and evaluation, 4) the two ACTD case programs selected for analysis, 5) the apparent ACTD transition areas prone to success or failure, and 6) potential process improvements that would aid in ACTD transition to acquisition programs. This thesis identified both the strengths and the weaknesses in the existing ACTD transition process. The results indicated that several of the existing ACTD criteria should be maintained. However, it was also determined that several enhancements could be incorporated into future processes. It concluded with recommended improvements that would enhance the insertion of technology to the warfighter via the acquisition process. With the combination of current practices and implementation of these recommendations, ACTDs could become the cornerstone of the Secretary of Defense's new acquisition process., http://archive.org/details/transitioningadv109456142, GM-15, Department of the Navy, Approved for public release; distribution is unlimited.

Published
2012

41. Transitioning Advanced Concept Technology Demonstrations to Acquisition Programs

Author

NAVAL POSTGRADUATE SCHOOL MONTEREY CA, South, Matthew T., NAVAL POSTGRADUATE SCHOOL MONTEREY CA, and South, Matthew T.

Abstract

This thesis evaluated the Department of Defense's Advanced Concept Technology Demonstration (ACTD) process and the challenges encountered in transitioning an ACTD to an acquisition program. The methodology included case analyses of two ACTD programs that transitioned to acquisition programs to determine their levels of success. The scope included a review of: 1) ACTD origins and processes as of July 2002, 2) past ACTD programs, 3) the established documentation criteria associated with ACTD selection and evaluation, 4) the two ACTD case programs selected for analysis, 5) the apparent ACTD transition areas prone to success or failure, and 6) potential process improvements the would aid in ACTD transition to acquisition programs. This thesis identified both the strengths and the weaknesses in the existing ACTD transition process. The results indicated that several of the existing ACTD criteria should be maintained. However, it was also determined that several enhancements could be incorporated into future processes. It concluded with recommended improvements that would enhance the insertion of technology to the warfighter via the acquisition process. With the combination of current practices and implementation of these recommendations, ACTDs could become the cornerstone of the Secretary of Defense's new acquisition process., The original document contains color images.

Published
2003

42. Transitioning Advanced Concept Technology Demonstrations to acquisition programs

Author

Snider, Keith, Owen, Wally, Product Development, South, Matthew T., Snider, Keith, Owen, Wally, Product Development, and South, Matthew T.

Abstract

This thesis evaluated the Department of Defense's Advanced Concept Technology Demonstration (ACTD) process and the challenges encountered in transitioning an ACTD to an acquisition program. The methodology included case analyses of two ACTD programs that transitioned to acquisition programs to determine their levels of success. The scope included a review of: 1) ACTD origins and processes as of July 2002, 2) past ACTD programs, 3) the established documentation criteria associated with ACTD selection and evaluation, 4) the two ACTD case programs selected for analysis, 5) the apparent ACTD transition areas prone to success or failure, and 6) potential process improvements that would aid in ACTD transition to acquisition programs. This thesis identified both the strengths and the weaknesses in the existing ACTD transition process. The results indicated that several of the existing ACTD criteria should be maintained. However, it was also determined that several enhancements could be incorporated into future processes. It concluded with recommended improvements that would enhance the insertion of technology to the warfighter via the acquisition process. With the combination of current practices and implementation of these recommendations, ACTDs could become the cornerstone of the Secretary of Defense's new acquisition process.

Published
2003

43. From Guidelines to Careflows: Modelling and Supporting Complex Clinical Processes.

Author

Ten Teije, Annette, Miksch, Silvia, Lucas, Peter, Fox, John, Black, Elizabeth, Chronakis, Ioannis, Dunlop, Robert, Patkar, Vivek, South, Matthew, and Thomson, Richard

Abstract

Research on computer interpretable clinical guidelines has largely focused on individual points of care rather than processes of care. Whether we consider simple aids like clinical alerts and reminders or more sophisticated data interpretation and decision-making, guideline developers tend to focus on specific tasks rather than processes like care plans and pathways which are extended in time. In contrast, research on business process modelling has demonstrated notations and tools which deal directly with process modelling, but has not been concerned with problems like data interpretation and decision making. In this chapter we describe these two traditions, and compare some of their strengths and weaknesses. We also briefly discuss the distinct theoretical frameworks which have grown up around them, notably Petri nets for workflow modelling and mathematical logics for guidelines. We conclude that these offer complementary views of clinical processes and that a key research challenge is find a way of unifying them. We wish to thank Natalie Mulyar, Marco Montali, Annette ten Teije and Mor Peleg for their helpful comments on earlier drafts of this discussion. [ABSTRACT FROM AUTHOR]

Published
2008

45. Exploring UK medical school differences: the MedDifs study of selection, teaching, student and F1 perceptions, postgraduate outcomes and fitness to practise

Author

Richard Nzewi, Luke Debenham, Calum MacMillan, Kiran Nadeem, Zoe Carrington, Omar Risk, Rebecca Razey, James Parkin, Sam Myers, Asanish Kalyanasundaram, Mark Awad, Zein Gowie, Julian Purdy, Simon E. V. Phillips, Zoe Wellbelove, Mike Jones, Priscilla Kirkland, Nadine Abbas, Oluseyi Adesalu, Roxanne Tajbakhsh, Oliver Shotton, Adele Heaney, Joseph Brandreth, Benjamin Human, Jamie Mac Donald Burrell, Benjamin Thompson, Wassim Merzougui, Chris Harlow, Matthew South, Angelos Mantelakis, Nora Sangvik Grandal, Samuel C. Barnes, Daniel Sims, Fady Sameh Anis, Hannah Lawrence-Smith, Thomas P. W. Cope, Farris Ziyada, Saad Majeed, Carmel Razzaghi, Jack Eldridge, Rachel Maguire, Amy Harrington, Simon Lubbock, Abdul Muiz Azri Yahaya, Roshan Ullah, Oliver P. Devine, Zoe Cashin, Usman Rasul, Daniel Tadross, Pranoy Sangal, Ngan Hong Ta, Ammar Ahmed, Mehar Chawla, Sarah Freeston, Hugo Layard Horsfall, Sohini Pawar, Eve Lancaster, Harriet Hunter, Richard Clough, Alex Eyre, Sonam Aojula, Prabhjot Singh Malhotra, Elliot Raymond-Taggert, Ciaran Grafton-Clarke, Abdelrahman Said, Katherine Aiken, Ryan Janjuha, Adam Sage, Wing Hang Serene Ho, Shree Vadera, Joyce Omatseye, Aninditya Salma Sopian, Daisy Bassey-Duke, Samuel Penrice, Tom Newman, Sarah Douglas, Helen Gilbert, Jia Jun Ang, Aisha Sooltangos, Leher Gumber, Raisa Ramjan, Nasreen Desai, Shaunak Chatterjee, Oloruntobi Rotimi, Shreya Badhrinarayanan, Chris Clements, Harriet Van Den Tooren, Bronwen Jacob-Ramsdale, Savraj Kalsi, Mrudula Utukuri, Soham Bandyopadhyay, Eleanor Houghton, Meron Esere, Sadhana Kalidindi, Chung Shen Chean, James Coultas, Emma Sanders, Jessica Coulthurst, Ahmed Ashraf, Nick Smith, Whitney General, Katherine Garnett, Emma L Howard, Joanna Rigbye, Rebecca Braine, Saba Houshangi, Christina Hood, Aadam Shah, Praveen Mahendran, Ifrah Hussain, Ben Phillips, Keshni Gudka, Alimatu Sadia Akeredolu, Reza Nasseri, Tobin Joseph, Vinay Mandagere, Charlotte Leeson, Jess Trevett, Anjola Mosuro, Othman Khaled Al-Othman, Thomas Hughes-Gooding, Eimear Reel, Vinnie Christine Daniels, Liora Wittner, Sophie Paddock, Verity Ford, Daniel Smith, Sara Venturini, Rahul Atul Shah, Martha Amy Smith, Matthew Tyler, Charlotte Boreham, Ratan Randhawa, Katie Smart, Richard Thomas Jackson-Taylor, Agnieszka Jakubowska, Andrew Christopher Harborne, Jessica Speller, Sophie Mustoe, Ibrahim Alam, Richard B. Anderson, Mohit Patel, Tess Marshall-Andon, Liam Curry, Elliot John Revell, Sami Hussain, Hassan Baig, Geoffrey Hong Kiat Yong, Emma Aspinall, Robert Spencer, Sajan Khullar, Ryan Samuels, Saleh Jawad, Oliver Jones, Ivan Aganin, Rebecca Davis, Hanelie De Waal, I. C. McManus, Ryan Clark, Adam Moxley, Charlie Caird, Ibrahim T. Fazmin, Natasha Benons, Rosalie Ogborne, Rishi K Gupta, Sahaj Kaur, Melanie Vine, Muzzamil Jelani, James Druce, Julius Elisabeth Richard Lenaerts, Anna Kane, Lucy Holloway, Lady Namera Ejamike, Tom Joseph Syer, Alena Ashby, Sophie McGovern, Simon Davies, Sophia Fitzgerald-Smith, Aradhya Vijayakumar, Stuart Pearce, Kerry Long, Joseph Beecham, Catherine Arthur, Qaisar Khan, Joshua William Kearsley, David Johnston, Ross McAllister, Hannah Douglas, Aamna Ali, McManus, I. C. [0000-0003-3510-4814], Harborne, Andrew Christopher [0000-0003-0937-1492], Horsfall, Hugo Layard [0000-0001-7848-5325], Joseph, Tobin [0000-0002-9283-9895], Smith, Daniel T. [0000-0003-1215-5811], Marshall-Andon, Tess [0000-0001-8909-1639], Samuels, Ryan [0000-0002-0886-7990], Abbas, Nadine [0000-0002-9116-2915], Baig, Hassan [0000-0001-8999-0474], Beecham, Joseph [0000-0002-4390-9164], Benons, Natasha [0000-0002-0750-6560], Caird, Charlie [0000-0001-7553-5585], Clark, Ryan [0000-0002-6143-9098], Cope, Thomas [0000-0003-0247-7411], Coultas, James [0000-0003-3381-8936], Debenham, Luke [0000-0002-3300-7802], Douglas, Sarah [0000-0003-3272-8154], Eldridge, Jack [0000-0001-6792-7714], Hughes-Gooding, Thomas [0000-0001-8483-5686], Jakubowska, Agnieszka [0000-0003-3153-8666], Jones, Oliver [0000-0003-4334-3567], Lancaster, Eve [0000-0001-5703-252X], MacMillan, Calum [0000-0002-8167-0051], McAllister, Ross [0000-0003-1686-1174], Merzougui, Wassim [0000-0001-7398-0095], Phillips, Ben [0000-0002-0489-6700], Risk, Omar [0000-0002-9226-9263], Sage, Adam [0000-0002-7763-404X], Sooltangos, Aisha [0000-0001-5675-1709], Spencer, Robert [0000-0001-9045-6260], Tajbakhsh, Roxanne [0000-0002-3753-6306], Adesalu, Oluseyi [0000-0001-8796-2701], Aganin, Ivan [0000-0002-3711-9430], Aiken, Katherine [0000-0002-3245-7772], Alam, Ibrahim [0000-0003-2853-623X], Ali, Aamna [0000-0003-1255-6113], Anderson, Richard [0000-0003-4734-0208], Anis, Fady Sameh [0000-0002-0950-3777], Arthur, Catherine [0000-0001-9551-6250], Ashraf, Ahmed [0000-0002-3921-8395], Aspinall, Emma [0000-0001-8307-1661], Awad, Mark [0000-0002-6570-010X], Yahaya, Abdul-Muiz Azri [0000-0002-7846-9322], Badhrinarayanan, Shreya [0000-0002-7159-098X], Bandyopadhyay, Soham [0000-0001-6553-3842], Barnes, Sam [0000-0001-6009-406X], Bassey-Duke, Daisy [0000-0002-0408-1042], Boreham, Charlotte [0000-0003-4828-0542], Braine, Rebecca [0000-0003-2751-3378], Brandreth, Joseph [0000-0002-6137-5928], Carrington, Zoe [0000-0002-0006-8334], Chawla, Mehar [0000-0003-0697-815X], Chean, Chung Shen [0000-0002-1625-4078], Clements, Chris [0000-0002-2567-5536], Clough, Richard [0000-0001-6941-7881], Coulthurst, Jessica [0000-0003-0714-9572], Curry, Liam [0000-0002-6906-5502], Daniels, Vinnie Christine [0000-0001-9461-8431], Davies, Simon [0000-0001-5556-7512], Davis, Rebecca [0000-0002-6960-5580], De Waal, Hanelie [0000-0002-9131-9521], Desai, Nasreen [0000-0003-4601-9084], Douglas, Hannah [0000-0001-8113-5465], Druce, James [0000-0001-6849-8560], Ejamike, Lady-Namera [0000-0001-6196-5584], Esere, Meron [0000-0002-1838-0756], Eyre, Alex [0000-0002-0474-5662], Fazmin, Ibrahim Talal [0000-0002-3300-6279], Ford, Verity [0000-0002-1364-8522], Freeston, Sarah [0000-0001-8768-5513], Garnett, Katherine [0000-0003-1135-6478], General, Whitney [0000-0002-9222-4233], Gilbert, Helen [0000-0002-5588-411X], Gowie, Zein [0000-0002-7597-7508], Grafton-Clarke, Ciaran [0000-0002-8537-0806], Gudka, Keshni [0000-0003-0880-6992], Gumber, Leher [0000-0003-0154-1207], Gupta, Rishi [0000-0003-3839-7982], Harlow, Chris [0000-0003-2912-0594], Heaney, Adele [0000-0002-0905-4342], Ho, Wing Hang Serene [0000-0002-2687-3198], Hood, Christina [0000-0001-6821-6324], Howard, Emma [0000-0001-8411-5734], Human, Benjamin [0000-0003-4445-0639], Hunter, Harriet [0000-0002-5204-2371], Hussain, Sami [0000-0002-8654-3617], Jackson-Taylor, Richard Thomas [0000-0002-6949-2977], Jacob-Ramsdale, Bronwen [0000-0001-6215-808X], Jawad, Saleh [0000-0001-8221-9871], Jelani, Muzzamil [0000-0003-3785-415X], Johnston, David [0000-0002-7078-7510], Kalidindi, Sadhana [0000-0002-4274-2913], Kalsi, Savraj [0000-0001-9103-5089], Kalyanasundaram, Asanish [0000-0003-4175-5998], Kane, Anna [0000-0002-6197-2511], Kaur, Sahaj [0000-0002-3446-6082], Al-Othman, Othman Khaled [0000-0001-5930-8593], Khan, Qaisar [0000-0002-5486-4205], Khullar, Sajan [0000-0001-6426-2892], Kirkland, Priscilla [0000-0003-2452-4207], Leeson, Charlotte [0000-0003-4735-2452], Lenaerts, Julius Elisabeth Richard [0000-0002-1626-7792], Long, Kerry [0000-0003-4546-5509], Burrell, Jamie Mac Donald [0000-0002-8175-3973], Maguire, Rachel [0000-0003-4855-5550], Mahendran, Praveen [0000-0002-4084-5222], Majeed, Saad [0000-0003-4289-5597], Malhotra, Prabhjot Singh [0000-0001-7463-3941], Mandagere, Vinay [0000-0002-2969-6457], Mantelakis, Angelos [0000-0001-7109-943X], McGovern, Sophie [0000-0002-6198-7553], Mosuro, Anjola [0000-0002-9013-9289], Moxley, Adam [0000-0001-9794-091X], Mustoe, Sophie [0000-0002-7870-1320], Myers, Sam [0000-0001-6050-4663], Nadeem, Kiran [0000-0001-5988-6780], Newman, Tom [0000-0002-7987-9948], Nzewi, Richard [0000-0002-8159-1302], Ogborne, Rosalie [0000-0003-3327-6030], Omatseye, Joyce [0000-0001-7472-4712], Parkin, James [0000-0002-1861-0822], Patel, Mohit [0000-0003-1168-2759], Pawar, Sohini [0000-0003-0224-5258], Pearce, Stuart [0000-0002-7482-0752], Penrice, Samuel [0000-0002-1142-6686], Ramjan, Raisa [0000-0002-2020-2123], Randhawa, Ratan [0000-0002-4749-4410], Rasul, Usman [0000-0001-5820-1313], Raymond-Taggert, Elliot [0000-0003-1514-6662], Razey, Rebecca [0000-0003-1971-3580], Razzaghi, Carmel [0000-0002-3264-8528], Revell, Elliot John [0000-0001-6025-094X], Rigbye, Joanna [0000-0003-4860-4084], Rotimi, Oloruntobi [0000-0002-1385-1993], Said, Abdelrahman [0000-0002-9801-4202], Sangal, Pranoy [0000-0001-5775-5859], Grandal, Nora Sangvik [0000-0002-2461-7852], Shah, Aadam [0000-0003-1841-5530], Shah, Rahul Atul [0000-0002-6981-7305], Shotton, Oliver [0000-0001-6684-3308], Sims, Daniel [0000-0002-6916-3719], Smith, Martha Amy [0000-0002-4652-3507], Smith, Nick [0000-0003-4478-2772], South, Matthew [0000-0003-2297-1089], Speller, Jessica [0000-0001-5503-4791], Ta, Ngan Hong [0000-0002-8747-5329], Tadross, Daniel [0000-0002-3593-0182], Thompson, Benjamin [0000-0002-2491-5235], Trevett, Jess [0000-0001-5008-1724], Ullah, Roshan [0000-0002-5140-6655], Utukuri, Mrudula [0000-0003-1510-469X], Vadera, Shree [0000-0001-9492-7541], Van Den Tooren, Harriet [0000-0001-9902-7147], Venturini, Sara [0000-0002-9541-0090], Vijayakumar, Aradhya [0000-0002-8702-1087], Vine, Melanie [0000-0002-7978-4375], Wittner, Liora [0000-0003-1271-4839], Yong, Geoffrey Hong Kiat [0000-0003-0103-5793], Ziyada, Farris [0000-0003-2117-7262], Devine, Oliver Patrick [0000-0001-7621-9699], Apollo - University of Cambridge Repository, McManus, IC [0000-0003-3510-4814], and Smith, Daniel T [0000-0003-1215-5811]

Subjects
Male, Students, Medical, 020205 medical informatics, Preparedness, Problem-based learning, media_common.quotation_subject, education, Specialty, lcsh:Medicine, 02 engineering and technology, National Training Study, National Student Survey, 03 medical and health sciences, Patient safety, 0302 clinical medicine, Obstetrics and gynaecology, Perception, 0202 electrical engineering, electronic engineering, information engineering, Medicine, Sanctions, Humans, Quality (business), 030212 general & internal medicine, Fitness to practise, Schools, Medical, GMC sanctions, media_common, Medical education, Postgraduate qualifications, business.industry, lcsh:R, Institutional histories, General Medicine, United Kingdom, Female, Medical school differences, business, Teaching styles, Research Article
Abstract

BackgroundMedical schools differ, particularly in their teaching, but it is unclear whether such differences matter, although influential claims are often made. The Medical School Differences (MedDifs) study brings together a wide range of measures of UK medical schools, including postgraduate performance, fitness to practise issues, specialty choice, preparedness, satisfaction, teaching styles, entry criteria and institutional factors.MethodAggregated data were collected for 50 measures across 29 UK medical schools. Data includeinstitutional history(e.g. rate of production of hospital and GP specialists in the past),curricular influences(e.g. PBL schools, spend per student, staff-student ratio), selection measures(e.g. entry grades),teaching and assessment(e.g. traditional vs PBL, specialty teaching, self-regulated learning), student satisfaction, Foundation selection scores,Foundation satisfaction,postgraduate examination performance andfitness to practise(postgraduate progression, GMC sanctions). Six specialties (General Practice, Psychiatry, Anaesthetics, Obstetrics and Gynaecology, Internal Medicine, Surgery) were examined in more detail.ResultsMedical school differences are stable across time (median alpha = 0.835). The 50 measures were highly correlated, 395 (32.2%) of 1225 correlations being significant withp p Problem-based learning (PBL) schools differ on many measures, including lower performance on postgraduate assessments. While these are in part explained by lower entry grades, a surprising finding is that schools such as PBL schools which reportedgreaterstudent satisfaction with feedback also showedlowerperformance at postgraduate examinations.More medical school teaching of psychiatry, surgery and anaesthetics did not result in more specialist trainees. Schools that taught more general practice did have more graduates entering GP training, but those graduates performed less well in MRCGP examinations, the negative correlation resulting from numbers of GP trainees and exam outcomes being affected both by non-traditional teaching and by greater historical production of GPs.Postgraduate exam outcomes were also higher in schools with more self-regulated learning, but lower in larger medical schools.A path model for 29 measures found a complex causal nexus, most measures causing or being caused by other measures. Postgraduate exam performance was influenced by earlier attainment, at entry to Foundation and entry to medical school (the so-called academic backbone), and by self-regulated learning.Foundation measures of satisfaction, including preparedness, had no subsequent influence on outcomes. Fitness to practise issues were more frequent in schools producing more male graduates and more GPs.ConclusionsMedical schools differ in large numbers of ways that are causally interconnected. Differences between schools in postgraduate examination performance, training problems and GMC sanctions have important implications for the quality of patient care and patient safety.

Published
2020

46. The Analysis of Teaching of Medical Schools (AToMS) survey: an analysis of 47,258 timetabled teaching events in 25 UK medical schools relating to timing, duration, teaching formats, teaching content, and problem-based learning

Author

Anjola Mosuro, Liora Wittner, Reza Nasseri, Zoe Carrington, Emma L Howard, Ifrah Hussain, Ben Phillips, Asanish Kalyanasundaram, Nadine Abbas, Robert Spencer, Keshni Gudka, Zoe Wellbelove, Harriet Hunter, Richard Clough, Alex Eyre, Ahmed Ashraf, Nick Smith, Tobin Joseph, Benjamin Human, Sajan Khullar, Oloruntobi Rotimi, Abdul-Muiz Azri Yahaya, Shreya Badhrinarayanan, Whitney General, Martha Amy Smith, Adele Heaney, Ratan Randhawa, Tom Joseph Syer, Aninditya Salma Sopian, Christina Hood, Katie Smart, Richard Thomas Jackson-Taylor, Jia Jun Ang, Angelos Mantelakis, Shaunak Chatterjee, Bronwen Jacob-Ramsdale, Matthew Tyler, Saba Houshangi, Charlotte Boreham, Wassim Merzougui, Oliver P. Devine, Soham Bandyopadhyay, Tess Marshall-Andon, Liam Curry, Elliot John Revell, Andrew Christopher Harborne, Oliver Shotton, Natasha Benons, Samuel C. Barnes, Sophie McGovern, Daniel Sims, Stuart Pearce, Sami Hussain, Hassan Baig, Catherine Arthur, Tom Newman, Kerry Long, Farris Ziyada, Savraj Kalsi, Agnieszka Jakubowska, Julian Purdy, Rebecca Braine, Julius Elisabeth Richard Lenaerts, Eimear Reel, Joshua William Kearsley, Ross McAllister, Jess Trevett, Jessica Speller, Joseph Beecham, Roshan Ullah, Geoffrey Hong Kiat Yong, Mrudula Utukuri, Sophie Mustoe, Aradhya Vijayakumar, Mohit Patel, Sadhana Kalidindi, Mark Awad, Hugo Layard Horsfall, Ryan Samuels, Eve Lancaster, Carmel Razzaghi, Sonam Aojula, Priscilla Kirkland, Sophie Paddock, Elliot Raymond-Taggert, Omar Risk, Vinay Mandagere, Saleh Jawad, Rebecca Davis, Ngan Hong Ta, Abdelrahman Said, Aadam Shah, Othman Khaled Al-Othman, Usman Rasul, Mehar Chawla, Ryan Janjuha, Wing Hang Serene Ho, I. C. McManus, Sara Venturini, Thomas Hughes-Gooding, Alimatu-Sadia Akeredolu, Verity Ford, Charlie Caird, Aamna Ali, Saad Majeed, Jack Eldridge, Rachel Maguire, Prabhjot Singh Malhotra, Lady-Namera Ejamike, Sarah Freeston, Katherine Aiken, Mike Jones, Oluseyi Adesalu, Shree Vadera, Roxanne Tajbakhsh, Nasreen Desai, Daniel Tadross, Jessica Coulthurst, Vinnie Christine Daniels, Sam Myers, Joseph Brandreth, Chris Clements, Chung Shen Chean, Chris Harlow, Praveen Mahendran, Benjamin Thompson, Adam Moxley, Thomas P. W. Cope, James Druce, Matthew South, Emma Aspinall, Pranoy Sangal, Ammar Ahmed, Ciaran Grafton-Clarke, Daisy Bassey-Duke, Samuel Penrice, Nora Sangvik Grandal, Eleanor Houghton, Amy Harrington, Simon Lubbock, Leher Gumber, Sohini Pawar, Raisa Ramjan, Qaisar Khan, David Johnston, Aisha Sooltangos, Hannah Douglas, Meron Esere, Lucy Holloway, Alena Ashby, Simon Davies, Sophia Fitzgerald-Smith, Hanelie De Waal, Ibrahim T. Fazmin, Rosalie Ogborne, Sahaj Kaur, Melanie Vine, Muzzamil Jelani, Anna Kane, James Coultas, Ibrahim Alam, Jamie Mac Donald Burrell, Hannah Lawrence-Smith, Zoe Cashin, Joyce Omatseye, Sarah Douglas, Helen Gilbert, Harriet Van Den Tooren, Katherine Garnett, Joanna Rigbye, Rebecca Razey, James Parkin, Simon Phillips, Charlotte Leeson, Rahul Atul Shah, Richard Nzewi, Luke Debenham, Zein Gowie, Calum MacMillan, Kiran Nadeem, Fady Sameh Anis, Adam Sage, Emma Sanders, Oliver Jones, Ivan Aganin, Richard B. Anderson, Rishi K Gupta, Ryan Clark, Devine, Oliver Patrick [0000-0001-7621-9699], Harborne, Andrew Christopher [0000-0003-0937-1492], Horsfall, Hugo Layard [0000-0001-7848-5325], Joseph, Tobin [0000-0002-9283-9895], Marshall-Andon, Tess [0000-0001-8909-1639], Samuels, Ryan [0000-0002-0886-7990], Abbas, Nadine [0000-0002-9116-2915], Baig, Hassan [0000-0001-8999-0474], Beecham, Joseph [0000-0002-4390-9164], Benons, Natasha [0000-0002-0750-6560], Caird, Charlie [0000-0001-7553-5585], Clark, Ryan [0000-0002-6143-9098], Cope, Thomas [0000-0003-0247-7411], Coultas, James [0000-0003-3381-8936], Debenham, Luke [0000-0002-3300-7802], Douglas, Sarah [0000-0003-3272-8154], Eldridge, Jack [0000-0001-6792-7714], Hughes-Gooding, Thomas [0000-0001-8483-5686], Jakubowska, Agnieszka [0000-0003-3153-8666], Jones, Oliver [0000-0003-4334-3567], Lancaster, Eve [0000-0001-5703-252X], MacMillan, Calum [0000-0002-8167-0051], McAllister, Ross [0000-0003-1686-1174], Merzougui, Wassim [0000-0001-7398-0095], Phillips, Ben [0000-0002-0489-6700], Risk, Omar [0000-0002-9226-9263], Sage, Adam [0000-0002-7763-404X], Sooltangos, Aisha [0000-0001-5675-1709], Spencer, Robert [0000-0001-9045-6260], Tajbakhsh, Roxanne [0000-0002-3753-6306], Adesalu, Oluseyi [0000-0001-8796-2701], Aganin, Ivan [0000-0002-3711-9430], Aiken, Katherine [0000-0002-3245-7772], Alam, Ibrahim [0000-0003-2853-623X], Ali, Aamna [0000-0003-1255-6113], Anderson, Richard [0000-0003-4734-0208], Anis, Fady Sameh [0000-0002-0950-3777], Arthur, Catherine [0000-0001-9551-6250], Ashraf, Ahmed [0000-0002-3921-8395], Aspinall, Emma [0000-0001-8307-1661], Awad, Mark [0000-0002-6570-010X], Yahaya, Abdul-Muiz Azri [0000-0002-7846-9322], Badhrinarayanan, Shreya [0000-0002-7159-098X], Bandyopadhyay, Soham [0000-0001-6553-3842], Barnes, Sam [0000-0001-6009-406X], Bassey-Duke, Daisy [0000-0002-0408-1042], Boreham, Charlotte [0000-0003-4828-0542], Braine, Rebecca [0000-0003-2751-3378], Brandreth, Joseph [0000-0002-6137-5928], Carrington, Zoe [0000-0002-0006-8334], Chawla, Mehar [0000-0003-0697-815X], Chean, Chung Shen [0000-0002-1625-4078], Clements, Chris [0000-0002-2567-5536], Clough, Richard [0000-0001-6941-7881], Coulthurst, Jessica [0000-0003-0714-9572], Curry, Liam [0000-0002-6906-5502], Daniels, Vinnie Christine [0000-0001-9461-8431], Davies, Simon [0000-0001-5556-7512], Davis, Rebecca [0000-0002-6960-5580], De Waal, Hanelie [0000-0002-9131-9521], Desai, Nasreen [0000-0003-4601-9084], Douglas, Hannah [0000-0001-8113-5465], Druce, James [0000-0001-6849-8560], Ejamike, Lady-Namera [0000-0001-6196-5584], Esere, Meron [0000-0002-1838-0756], Eyre, Alex [0000-0002-0474-5662], Fazmin, Ibrahim Talal [0000-0002-3300-6279], Ford, Verity [0000-0002-1364-8522], Freeston, Sarah [0000-0001-8768-5513], Garnett, Katherine [0000-0003-1135-6478], General, Whitney [0000-0002-9222-4233], Gilbert, Helen [0000-0002-5588-411X], Gowie, Zein [0000-0002-7597-7508], Grafton-Clarke, Ciaran [0000-0002-8537-0806], Gudka, Keshni 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[0000-0003-1510-469X], Vadera, Shree [0000-0001-9492-7541], Van Den Tooren, Harriet [0000-0001-9902-7147], Venturini, Sara [0000-0002-9541-0090], Vijayakumar, Aradhya [0000-0002-8702-1087], Vine, Melanie [0000-0002-7978-4375], Wittner, Liora [0000-0003-1271-4839], Yong, Geoffrey Hong Kiat [0000-0003-0103-5793], Ziyada, Farris [0000-0003-2117-7262], McManus, IC [0000-0003-3510-4814], Apollo - University of Cambridge Repository, and McManus, I. C. [0000-0003-3510-4814]

Subjects
Male, 020205 medical informatics, Problem-based learning, lcsh:Medicine, Timetables, Tutorials, 02 engineering and technology, 03 medical and health sciences, Self-regulated learning, 0302 clinical medicine, Clinical teaching, Surveys and Questionnaires, Curriculum mapping, 0202 electrical engineering, electronic engineering, information engineering, Medicine, Humans, 030212 general & internal medicine, Duration (project management), Student perceptions, Medical education, Academic year, business.industry, lcsh:R, General Medicine, United Kingdom, Lectures, Female, Curriculum, Medical school differences, business, Postgraduate training, Teaching styles, Education, Medical, Undergraduate, Research Article
Abstract

Background: \ud What subjects UK medical schools teach, what ways they teach subjects, and how much they teach those subjects is unclear. Whether teaching differences matter is a separate, important question. This study provides a detailed picture of timetabled undergraduate teaching activity at 25 UK medical schools, particularly in relation to problem-based learning (PBL).\ud \ud Method: \ud The Analysis of Teaching of Medical Schools (AToMS) survey used detailed timetables provided by 25 schools with standard 5-year courses. Timetabled teaching events were coded in terms of course year, duration, teaching format, and teaching content. Ten schools used PBL. Teaching times from timetables were validated against two other studies that had assessed GP teaching and lecture, seminar, and tutorial times.\ud \ud Results: \ud A total of 47,258 timetabled teaching events in the academic year 2014/2015 were analysed, including SSCs (student-selected components) and elective studies. A typical UK medical student receives 3960 timetabled hours of teaching during their 5-year course. There was a clear difference between the initial 2 years which mostly contained basic medical science content and the later 3 years which mostly consisted of clinical teaching, although some clinical teaching occurs in the first 2 years. Medical schools differed in duration, format, and content of teaching. Two main factors underlay most of the variation between schools, Traditional vs PBL teaching and Structured vs Unstructured teaching. A curriculum map comparing medical schools was constructed using those factors. PBL schools differed on a number of measures, having more PBL teaching time, fewer lectures, more GP teaching, less surgery, less formal teaching of basic science, and more sessions with unspecified content.\ud \ud Discussion: \ud UK medical schools differ in both format and content of teaching. PBL and non-PBL schools clearly differ, albeit with substantial variation within groups, and overlap in the middle. The important question of whether differences in teaching matter in terms of outcomes is analysed in a companion study (MedDifs) which examines how teaching differences relate to university infrastructure, entry requirements, student perceptions, and outcomes in Foundation Programme and postgraduate training.

Published
2019

47. Which computable biomedical knowledge objects will be regulated? Results of a UK workshop discussing the regulation of knowledge libraries and software as a medical device.

Author

Wyatt JC, Scott P, Ordish J, South M, Thomas M, Jones C, and Lacey-Bryant S

Abstract

Introduction: To understand when knowledge objects in a computable biomedical knowledge library are likely to be subject to regulation as a medical device in the United Kingdom., Methods: A briefing paper was circulated to a multi-disciplinary group of 25 including regulators, lawyers and others with insights into device regulation. A 1-day workshop was convened to discuss questions relating to our aim. A discussion paper was drafted by lead authors and circulated to other authors for their comments and contributions., Results: This article reports on those deliberations and describes how UK device regulators are likely to treat the different kinds of knowledge objects that may be stored in computable biomedical knowledge libraries. While our focus is the likely approach of UK regulators, our analogies and analysis will also be relevant to the approaches taken by regulators elsewhere. We include a table examining the implications for each of the four knowledge levels described by Boxwala in 2011 and propose an additional level., Conclusions: If a knowledge object is described as directly executable for a medical purpose to provide decision support, it will generally be in scope of UK regulation as "software as a medical device." However, if the knowledge object consists of an algorithm, a ruleset, pseudocode or some other representation that is not directly executable and whose developers make no claim that it can be used for a medical purpose, it is not likely to be subject to regulation. We expect similar reasoning to be applied by regulators in other countries., Competing Interests: Jeremy Wyatt and Philip Scott co‐chair the joint UK British Computer Society‐Faculty of Clinical Informatics special interest group on Mobilising Computable Biomedical Knowledge. Matthew South is a founder and shareholder of Deontics Ltd. The authors declare no other commercial or other competing interests that may have affected their views or interfered with their impartiality in carrying out this work., (© 2023 The Authors. Learning Health Systems published by Wiley Periodicals LLC on behalf of University of Michigan.)

Published
2023
Full Text
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48. From guidelines to careflows: modelling and supporting complex clinical processes.

Author

Fox J, Black E, Chronakis I, Dunlop R, Patkar V, South M, and Thomson R

Subjects
Decision Making, Computer-Assisted, Clinical Protocols standards, Decision Support Systems, Clinical organization & administration, Models, Theoretical, Practice Guidelines as Topic
Abstract

Research on computer interpretable clinical guidelines has largely focused on individual points of care rather than processes of care. Whether we consider simple aids like clinical alerts and reminders or more sophisticated data interpretation and decision-making, guideline developers tend to focus on specific tasks rather than processes like care plans and pathways which are extended in time. In contrast, research on business process modelling has demonstrated notations and tools which deal directly with process modelling, but has not been concerned with problems like data interpretation and decision making. In this chapter we describe these two traditions, and compare some of their strengths and weaknesses. We also briefly discuss the distinct theoretical frameworks which have grown up around them, notably Petri nets for workflow modelling and mathematical logics for guidelines. We conclude that these offer complementary views of clinical processes and that a key research challenge is find a way of unifying them.

Published
2008

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