Publications
74 results found
Larsen K, Akindele B, Head H, et al., 2022, Developing a User-Centered Digital Clinical Decision Support App for Evidence-Based Medication Recommendations for Type 2 Diabetes Mellitus: Prototype User Testing and Validation Study, JMIR HUMAN FACTORS, Vol: 9, ISSN: 2292-9495
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- Citations: 6
Aggarwal R, Visram S, Martin G, et al., 2021, Defining the enablers and barriers to the implementation of large-scale healthcare related mobile technology: a qualitative case study in a tertiary hospital setting, JMIR mHealth and uHealth, Vol: 10, Pages: 1-11, ISSN: 2291-5222
Background:The successful implementation of clinical smartphone applications in hospital settings requires close collaboration with industry partners. A large-scale hospital-wide implementation of a clinical mobile application for healthcare professionals developed in partnership with Google Health and academic partners was deployed on a Bring Your Own Device (BYOD) basis using mobile device management (MDM) at our UK academic hospital. As this was the first large-scale implementation of this type of innovation in the UK health system, important insights and lessons learned from the deployment may be useful to other organisations considering implementing similar technology in partnership with commercial companies.Objective:The aims of this study were to define the key enablers and barriers, and to propose a ‘roadmap’ for the implementation of a hospital-wide clinical mobile application developed in collaboration with an industry partner as a data processor and an academic partner for independent evaluation.Methods:Semi-structured interviews were conducted with high-level stakeholders from industry, academia and healthcare providers who had instrumental roles in the implementation of the application at our hospital. The interviews explored participant’s views on the enablers and barriers to the implementation process. Interviews were analysed using a broadly deductive approach to thematic analysis.Results:In total, 14 participants were interviewed. Key enablers identified were the establishment of a steering committee with high-level clinical involvement, well-defined roles and responsibilities between partners, effective communication strategies with end-users, safe information governance precautions and increased patient engagement and transparency. Barriers identified were the lack of dedicated resources for mobile change at our hospital, risk aversion, unclear strategy and regulation, and the implications of BYOD and MDM policies. The key lesson
Larsen K, Akindele B, Head H, et al., 2021, Developing a User-Centered Digital Clinical Decision Support App for Evidence-Based Medication Recommendations for Type 2 Diabetes Mellitus: Prototype User Testing and Validation Study (Preprint)
<sec> <title>BACKGROUND</title> <p>Closing the gap between care recommended by evidence-based guidelines and care delivered in practice is an ongoing challenge across systems and delivery models. Clinical decision support systems (CDSSs) are widely deployed to augment clinicians in their complex decision-making processes. Despite published success stories, the poor usability of many CDSSs has contributed to fragmented workflows and alert fatigue.</p> </sec> <sec> <title>OBJECTIVE</title> <p>This study aimed to validate the application of a user-centered design (UCD) process in the development of a standards-based medication recommender for type 2 diabetes mellitus in a simulated setting. The prototype app was evaluated for effectiveness, efficiency, and user satisfaction.</p> </sec> <sec> <title>METHODS</title> <p>We conducted interviews with 8 clinical leaders with 8 rounds of iterative user testing with 2-8 prescribers in each round to inform app development. With the resulting prototype app, we conducted a validation study with 43 participants. The participants were assigned to one of two groups and completed a 2-hour remote user testing session. Both groups reviewed mock patient facts and ordered diabetes medications for the patients. The Traditional group used a mock electronic health record (EHR) for the review in Period 1 and used the prototype app in Period 2, while the Tool group used the prototype app during both time periods. The perceived cognitive load associated with task performance during each period was assessed with the National Aeronautics and Space Administration Task Load Index. Participants also completed the System Usability Scale (SUS
Aggarwal R, Visram S, Martin G, et al., 2021, Defining the Enablers and Barriers to the Implementation of Large-scale, Health Care–Related Mobile Technology: Qualitative Case Study in a Tertiary Hospital Setting (Preprint)
<sec> <title>BACKGROUND</title> <p>The successful implementation of clinical smartphone apps in hospital settings requires close collaboration with industry partners. A large-scale, hospital-wide implementation of a clinical mobile app for health care professionals developed in partnership with Google Health and academic partners was deployed on a bring-your-own-device basis using mobile device management at our UK academic hospital. As this was the first large-scale implementation of this type of innovation in the UK health system, important insights and lessons learned from the deployment may be useful to other organizations considering implementing similar technology in partnership with commercial companies.</p> </sec> <sec> <title>OBJECTIVE</title> <p>The aims of this study are to define the key enablers and barriers and to propose a <i>road map</i> for the implementation of a hospital-wide clinical mobile app developed in collaboration with an industry partner as a data processor and an academic partner for independent evaluation.</p> </sec> <sec> <title>METHODS</title> <p>Semistructured interviews were conducted with high-level stakeholders from industry, academia, and health care providers who had instrumental roles in the implementation of the app at our hospital. The interviews explored the participants’ views on the enablers and barriers to the implementation process. The interviews were analyzed using a broadly deductive approach to thematic analysis.</p> </sec> <sec> <title>RESULTS</title> <p&g
Connell A, Black G, Montgomery H, et al., 2019, Implementation of a Digitally Enabled Care Pathway (Part 2): Qualitative Analysis of Experiences of Health Care Professionals, JOURNAL OF MEDICAL INTERNET RESEARCH, Vol: 21, ISSN: 1438-8871
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- Citations: 19
Connell A, Raine R, Martin P, et al., 2019, Implementation of a Digitally Enabled Care Pathway (Part 1): Impact on Clinical Outcomes and Associated Health Care Costs, JOURNAL OF MEDICAL INTERNET RESEARCH, Vol: 21, ISSN: 1438-8871
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- Citations: 13
Ahmed I, Ahmad NS, Ali S, et al., 2018, Medication adherence apps: A review and content analysis, JMIR mHealth and uHealth, Vol: 6, ISSN: 2291-5222
Background:Medication adherence is a costly and damaging problem for both healthcare providers and patients alike. Patients adhere to only 50% of drugs prescribed for chronic diseases in developed nations. Digital health has paved the way for innovative smartphone solutions to tackle this challenge. However, despite the numerous applications (apps) available claiming to improve adherence, a thorough review of adherence applications has not been carried out to date.Objective:(i)To review medication adherence apps (otherwise known as mAdherence app) in the Apple App store and the Google Play repository in terms of their evidence base, medical professional involvement in development, and strategies used to facilitate behaviour change and improve adherence.(ii)To provide a system of classification for these apps. Methods:In April 2015, relevant mAdherence apps were identified by systematically searching the Apple and Google Play app stores using a combination of relevant search terms. Data extracted for each app included app store source, app price, documentation of healthcare professional (HCP) involvement during app development and evidence base for each respective app.Free apps were downloaded to explore the strategies used to promote medication adherence. Testing involved a standardised medication regimen of three reminders over a four-hour period. Non-adherence features designed to enhance user experience were also documented.Results:The App repository search identified a total of 5889 applications. 806 fulfilled the inclusion criteria initially and were tested. 682 applications were further analysed for data extraction. Of these, 61.7% were free for testing, 8.5% were inaccessible and 29.8% required payment. Of the 421 free applications, 13.8% were developed with HCP involvement and an evidence base was identified in only 0.95%. Of the paid apps, 4.4% had HCP involvement, 0.5% had a documented evidence base and 0.5% had both. 31% of inaccessible apps were produce
Patel B, Johnston M, Cookson N, et al., 2016, Interprofessional Communication of Clinicians Using a Mobile Phone App: A Randomized Crossover Trial Using Simulated Patients, JOURNAL OF MEDICAL INTERNET RESEARCH, Vol: 18, ISSN: 1438-8871
Background: Most hospitals use paging systems as the principal communication system, despite general dissatisfaction by end users. To this end, we developed an app-based communication system (called Hark) to facilitate and improve the quality of interpersonal communication.Objective: The objectives of our study were (1) to assess the quality of information transfer using pager- and app-based (Hark) communication systems, (2) to determine whether using mobile phone apps for escalation of care results in additional delays in communication, and (3) to determine how end users perceive mobile phone apps as an alternative to pagers.Methods: We recruited junior (postgraduate year 1 and 2) doctors and nurses from a range of specialties and randomly assigned them to 2 groups who used either a pager device or the mobile phone-based Hark app. We asked nurses to hand off simulated patients while doctors were asked to receive handoff information using these devices. The quality of information transfer, time taken to respond to messages, and users’ satisfaction with each device was recorded. Each participant used both devices with a 2-week washout period in between uses.Results: We recruited 22 participants (13 nurses, 9 doctors). The quality of the referrals made by nurses was significantly better when using Hark (Hark median 118, range 100–121 versus pager median 77, range 39–104; P=.001). Doctors responded to messages using Hark more quickly than when responding to pagers, although this difference was not statistically significant (Hark mean 86.6 seconds, SD 96.2 versus pager mean 136.5 seconds, SD 201.0; P=.12). Users rated Hark as significantly better on 11 of the 18 criteria of an information transfer device (P<.05) These included “enhances interprofessional efficiency,” “results in less disturbance,” “performed desired functions reliably,” and “allows me to clearly transfer information.”Conclusions: Hark
King D, Zaman S, Zaman SS, et al., 2015, Identifying Quality Indicators Used by Patients to Choose Secondary Health Care Providers: A Mixed Methods Approach, JMIR MHEALTH AND UHEALTH, Vol: 3, ISSN: 2291-5222
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- Citations: 10
Johnston M, Arora S, Anderson O, et al., 2015, Escalation of care in surgery: a systematic risk assessment to prevent avoidable harm in hospitalized patients, Annals of Surgery, Vol: 261, Pages: 831-838, ISSN: 0003-4932
Objective: To systematically risk assess and analyze the escalation of care process in surgery so as to identify problems and provide recommendations for intervention.Background: The ability to escalate care appropriately when managing deteriorating patients is a hallmark of surgical competence and safe postoperative care. Healthcare-Failure-Mode-Effects-Analysis (HFMEA) is a methodology adapted from safety-critical industries, which allows for hazardous process failures to be prospectively identified and solutions to be recommended.Methods: Forty-two hours of ethnographic observations on surgical wards in 3 London hospitals (phase 1) formed the basis of an escalation process diagram. A risk-assessment survey identified failures associated with process steps and attributed hazard scores (phase 2). Patient safety and clinical risk experts validated hazard scores through a group consensus meeting (phase 3). Hazardous failures were taken forward to multidisciplinary HFMEA where cause analysis was applied and interventions were recommended (phase 4).Results: Observations identified 33 steps in the escalation process. The risk-assessment survey (30 surgical staff members, 100% response) and expert consensus group identified 18 hazardous failures associated with these steps. The HFMEA team identified 3 adequately controlled failures; therefore, 15 were subjected to cause analysis. Outdated communication technology, understaffing, and hierarchical barriers were identified as root causes of failure. Participants recommended interventions based on these findings including defined escalation protocols, human factors education, enhanced communication technology, and improved clinical supervision.Conclusions: Failures in the escalation process amenable to intervention were systematically identified. This mapping of the escalation process will allow tailored interventions to enhance surgical training and patient safety.
Johnston MJ, King D, Arora S, et al., 2015, Smartphones let surgeons know WhatsApp: an analysis of communication in emergency surgical teams, American Journal of Surgery, Vol: 209, Pages: 45-51, ISSN: 0002-9610
BackgroundOutdated communication technologies in healthcare can place patient safety at risk. This study aimed to evaluate implementation of the WhatsApp messaging service within emergency surgical teams.MethodsA prospective mixed-methods study was conducted in a London hospital. All emergency surgery team members (n = 40) used WhatsApp for communication for 19 weeks. The initiator and receiver of communication were compared for response times and communication types. Safety events were reported using direct quotations.ResultsMore than 1,100 hours of communication pertaining to 636 patients were recorded, generating 1,495 communication events. The attending initiated the most instruction-giving communication, whereas interns asked the most clinical questions (P < .001). The resident was the speediest responder to communication compared to the intern and attending (P < .001). The participants felt that WhatsApp helped flatten the hierarchy within the team.ConclusionsWhatsApp represents a safe, efficient communication technology. This study lays the foundations for quality improvement innovations delivered over smartphones.
Mobasheri MH, Johnston M, King D, et al., 2014, Smartphone breast applications - What's the evidence?, BREAST, Vol: 23, Pages: 683-689, ISSN: 0960-9776
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- Citations: 83
King D, Thompson P, Darzi A, 2014, Enhancing health and wellbeing through 'behavioural design', JOURNAL OF THE ROYAL SOCIETY OF MEDICINE, Vol: 107, Pages: 336-337, ISSN: 0141-0768
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- Citations: 3
Johnston MJ, King D, Arora S, et al., 2014, Requirements of a new communication technology for handover and the escalation of patient care: a multi-stakeholder analysis, Journal of Evaluation in Clinical Practice, Vol: 20, Pages: 486-497, ISSN: 1356-1294
Rationale, aims and objectivesIn order to enable safe and efficient information transfer between health care professionals during clinical handover and escalation of care, existing communication technologies must be updated. This study aimed to provide a user‐informed guide for the development of an application‐based communication system (ABCS), tailored for use in patient handover and escalation of care.MethodsCurrent methods of inter‐professional communication in health care along with information system needs for communication technology were identified through literature review. A focus group study was then conducted according to a topic guide developed by health innovation and safety researchers. Fifteen doctors and 11 nurses from three London hospitals participated in a mixture of homogeneous and heterogeneous sessions. The sessions were recorded and transcribed verbatim before being subjected to thematic analysis.ResultsSeventeen information system needs were identified from the literature review. Participants identified six themes detailing user perceptions of current communication technology, attitudes to smartphone technology and anticipated requirements of an application produced for handover and escalation of care. Participants were in favour of an ABCS over current methods and expressed enthusiasm for a system with integrated patient information and group‐messaging functions.ConclusionDespite concerns regarding confidentiality and information governance a robust guide for development and implementation of an ABCS was produced, taking input from multiple stakeholders into account. Handover and escalation of care are vital processes for patient safety and communication within these must be optimized. An ABCS for health care professionals would be a welcome innovation and may lead to improvements in patient safety.
Johnston M, Arora S, King D, et al., 2014, Escalation of care and failure to rescue: a multicenter, multiprofessional qualitative study, Surgery, Vol: 155, Pages: 989-994, ISSN: 0039-6060
BackgroundThe escalation of care process has not been explored in surgery, despite the role of communication failures in adverse events. This study aimed to develop a conceptual framework of the influences on escalation of care in surgery allowing solutions to facilitate management of sick patients to be developed.MethodsA multicenter qualitative study was conducted in three hospitals in London, UK. A total of 41 participants were recruited, including 16 surgeons, 11 surgical PGY1s, six surgical nurses, four intensivists, and four critical care outreach team members. Participants were submitted to semistructured interviews that were analyzed using grounded theory methodology.ResultsA decision to escalate was based upon five key themes: patient, individual, team, environmental, and organizational factors. Most participants felt that supervision and escalation of care were problematic in their hospital, with unclear escalation protocols and poor availability of senior surgical staff the most common concerns. Mobile phones and direct conversation were identified to be more effective when escalating care than hospital pager systems. Transparent escalation protocols, increased senior clinician supervision, and communication skills training were highlighted as strategies to improve escalation of care.ConclusionThis is the first study to describe escalation of care in surgery, a key process for protecting the safety of deteriorating surgical patients. Factors affecting the decision to escalate are complex, involving clinical and professional aspects of care. An understanding of this process could pave the way for interventions to facilitate escalation in order to improve patient outcome.
Johnston M, Arora S, King DC, et al., 2014, Escalation of Care in Surgery: a Systematic, Pro-active Risk Assessment of Clinical Supervision, American College of Surgeons Accredited Educational Institutions
King DC, 2014, Imperial IdeasLab: Behaviour change through mHealth, World Economic Forum
Johnston M, Arora S, Anderson O, et al., 2014, Escalation of Care in Surgery: A Systematic Risk Assessment to Prevent Avoidable Harm in Hospitalized Patients., Annals of Surgery
King DC, 2014, Designing digital interventions using behavioural economics, Healthcare revolution: Big data and smart analytics
King D, Jabbar A, Charani E, et al., 2014, Redesigning the 'choice architecture' of hospital prescription charts: a mixed methods study incorporating in situ simulation testing, BMJ OPEN, Vol: 4, ISSN: 2044-6055
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- Citations: 9
Birnbach DJ, King D, Vlaev I, et al., 2013, Impact of environmental olfactory cues on hand hygiene behaviour in a simulated hospital environment: a randomized study, JOURNAL OF HOSPITAL INFECTION, Vol: 85, Pages: 79-81, ISSN: 0195-6701
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- Citations: 19
Lee H, Vlaev I, King D, et al., 2013, Subjective well-being and the measurement of quality in healthcare, Social Science & Medicine, Pages: 27-34
Johnston M, Arora S, King DC, et al., 2013, Preventing failure to rescue by improving the escalation of care process: an intervention study, The American College of Surgeons 99th Annual Clinical Congress, Pages: 124-125
King D, Greaves F, Vlaev I, et al., 2013, Approaches Based On Behavioral Economics Could Help Nudge Patients And Providers Toward Lower Health Spending Growth, Health Affairs, Vol: 32, Pages: 661-668
Darzi A, King D, 2013, Innovating to reduce the costs of healthcare, Health Affairs: The Triple Aim Goes Global
Kulendran M, Vlaev I, Sugden C, et al., 2013, Neuropsychological assessment as a predictor of weight loss in obese adolescents, International Journal of Obesity, Vol: forthcoming
King D, Greaves F, Exeter F, et al., 2013, ‘Gamification’: Influencing health behaviours with games, Journal of the Royal Society of Medicine, Pages: 76-78
King D, Ramirez-Cano D, Greaves F, et al., 2013, Twitter and the health reforms in the English National Health Service, Health Policy, Vol: 110, Pages: 291-297, ISSN: 0168-8510
Social media (for example Facebook and YouTube) uses online and mobile technologies to allow individuals to participate in, comment on and create user-generated content. Twitter is a widely used social media platform that lets users post short publicly available text-based messages called tweets that other users can respond to. Alongside traditional media outlets, Twitter has been a focus for discussions about the controversial and radical reforms to the National Health Service (NHS) in England that were recently passed into law by the current coalition Government. Looking at over 120,000 tweets made about the health reforms, we have investigated whether any insights can be obtained about the role of Twitter in informing, debating and influencing opinion in a specific area of health policy. In particular we have looked at how the sentiment of tweets changed with the passage of the Health and Social Care Bill through Parliament, and how this compared to conventional opinion polls taken over the same time period. We examine which users appeared to have the most influence in the ‘Twittersphere’ and suggest how a widely used metric of academic impact – the H-index – could be applied to measure context-dependent influence on Twitter.
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