Development of a set of patient reported outcome measures for patients with benign liver tumours and cysts:patient focus groups and systematic review

BACKGROUND: Patient reported outcome measures (PROMs) may be useful for patients with benign liver tumours and cysts (BLTC) to evaluate the impact of treatment and/or guide shared decision making. Yet, a set of PROMs relevant to patients with BLTC is currently unavailable. In this study, we selected a PROMs set for patients with BLTC. METHODS: Potentially relevant patient reported outcomes (PROs) were selected by psychologist-researchers based on keywords used or suggested by participants of two virtual focus groups meetings consisting of thirteen female BLTC patients with a median age of 50 years. Subsequently, patients were asked to report their most relevant PROs. PROMs identified by systematic literature review and computerized adaptive tests (CATs) in the Patient-Reported Outcomes Measurement Information System (PROMIS) were considered in selecting the final PROMs set to assess relevant outcomes. RESULTS: The most important PROs were: insecurity/anxiety (11/12 patients), pain (9/12 patients), fatigue (8/12 patients), and limitations in daily life (5/12 patients). The literature review included 23 studies, which used various generic and disease-specific PROMs, often not measuring (all) relevant PROs. The final selected PROMs set included numerical rating scales for pain, two questions on overall health and quality of life and four PROMIS CATs. CONCLUSIONS: A PROMs set generically and efficiently measuring outcomes relevant for patients with BLTC was developed and may be used in future research and clinical practice. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41687-022-00531-1

Patient's perspective on improving the quality of acute medical care:determining patient reported outcomes

Rationale: There is an increasing societal demand for quality assurance and transparency of medical care. The American National Academy of Medicine has determined patient centredness as a quality domain for improvement of healthcare. While many of the current quality indicators are disease specific, most emergency department (ED) patients present with undifferentiated complaints. Therefore, there is a need for generic outcome measures. Our objective was to determine relevant patient reported outcomes (PROs) for quality measurement of acute care. Methods: We conducted semistructured interviews in patients ≥18 years presenting at the ED for internal medicine. Patients with a cognitive impairment or language barrier were excluded. Interviews were analysed using qualitative content analysis. Results: Thirty patients were interviewed. Patients reported outcomes as relevant in five domains: relief of symptoms, understanding the diagnosis, presence and understanding of the diagnostic and/or therapeutic plan, reassurance and patient experiences. Experiences were often mentioned as relevant to the perceived quality of care and appeared to influence the domain reassurance. Conclusion: We determined five domains of relevant PROs in acute care. These domains will be used for developing generic patient reported measures for acute care. The patients' perspective will be incorporated in these measures with the ultimate aim of organising truly patient-centred care at the ED

Implementing artificial intelligence in clinical practice: a mixed-method study of barriers and facilitators

Background: Though artificial intelligence (AI) in healthcare has great potential, medicine has been slowto adopt AI tools. Barriers and facilitators to clinical AI implementation among healthcare professionals (theend-users) are ill defined, nor have appropriate implementation strategies to overcome them been suggested.Therefore, we aim to study these barriers and facilitators, and find general insights that could be applicableto a wide variety of AI-tool implementations in clinical practice.Methods: We conducted a mixed-methods study encompassing individual interviews, a focus group, and anationwide survey. End-users of AI in healthcare (physicians) from various medical specialties were included.We performed deductive direct content analysis, using the Consolidated Framework for ImplementationResearch (CFIR) for coding. CFIR constructs were entered into the Expert Recommendations forImplementing Change (ERIC) to find suitable implementation strategies. Quantitative survey data wasdescriptively analyzed.Results: We performed ten individual interviews, and one focus group with five physicians. The mostprominent constructs identified during the qualitative interim analyses were incorporated in the nationwidesurvey, which had 106 survey respondents. We found nine CFIR constructs important to AI implementation:evidence strength, relative advantage, adaptability, trialability, structural characteristics, tension for change,compatibility, access to knowledge and information, and knowledge and beliefs about the intervention.Consequently, the ERIC tool displayed the following strategies: identify and prepare champions, conducteducational meetings, promote adaptability, develop educational materials, and distribute educationalmaterials. (PDF) Implementing artificial intelligence in clinical practice: a mixed-method study of barriers and facilitators. Available from: https://www.researchgate.net/publication/366636295_Implementing_artificial_intelligence_in_clinical_practice_a_mixed-method_study_of_barriers_and_facilitators [accessed Jan 17 2023]

Implementing artificial intelligence in clinical practice: a mixed-method study of barriers and facilitators

Background: Though artificial intelligence (AI) in healthcare has great potential, medicine has been slow to adopt AI tools. Barriers and facilitators to clinical AI implementation among healthcare professionals (the end-users) are ill defined, nor have appropriate implementation strategies to overcome them been suggested. Therefore, we aim to study these barriers and facilitators, and find general insights that could be applicable to a wide variety of AI-tool implementations in clinical practice. Methods: We conducted a mixed-methods study encompassing individual interviews, a focus group, and a nationwide survey. End-users of AI in healthcare (physicians) from various medical specialties were included. We performed deductive direct content analysis, using the Consolidated Framework for Implementation Research (CFIR) for coding. CFIR constructs were entered into the Expert Recommendations for Implementing Change (ERIC) to find suitable implementation strategies. Quantitative survey data was descriptively analyzed. Results: We performed ten individual interviews, and one focus group with five physicians. The most prominent constructs identified during the qualitative interim analyses were incorporated in the nationwide survey, which had 106 survey respondents. We found nine CFIR constructs important to AI implementation: evidence strength, relative advantage, adaptability, trialability, structural characteristics, tension for change, compatibility, access to knowledge and information, and knowledge and beliefs about the intervention. Consequently, the ERIC tool displayed the following strategies: identify and prepare champions, conduct educational meetings, promote adaptability, develop educational materials, and distribute educational materials. Conclusions: The potential value of AI in healthcare is acknowledged by end-users, however, the current tension for change needs to be sparked to facilitate sustainable implementation. Strategies that should be used are: increasing the access to knowledge and information through educational meetings and materials with committed local leaders. A trial phase for end-users to test and compare AI algorithms. Lastly, algorithms should be tailored to be adaptable to the local context and existing workflows. Applying these implementation strategies will bring us one step closer to realizing the value of AI in healthcare

Implementing artificial intelligence in clinical practice: a mixed-method study of barriers and facilitators

Background: Though artificial intelligence (AI) in healthcare has great potential, medicine has been slow to adopt AI tools. Barriers and facilitators to clinical AI implementation among healthcare professionals (the end-users) are ill defined, nor have appropriate implementation strategies to overcome them been suggested. Therefore, we aim to study these barriers and facilitators, and find general insights that could be applicable to a wide variety of AI-tool implementations in clinical practice. Methods: We conducted a mixed-methods study encompassing individual interviews, a focus group, and a nationwide survey. End-users of AI in healthcare (physicians) from various medical specialties were included. We performed deductive direct content analysis, using the Consolidated Framework for Implementation Research (CFIR) for coding. CFIR constructs were entered into the Expert Recommendations for Implementing Change (ERIC) to find suitable implementation strategies. Quantitative survey data was descriptively analyzed. Results: We performed ten individual interviews, and one focus group with five physicians. The most prominent constructs identified during the qualitative interim analyses were incorporated in the nationwide survey, which had 106 survey respondents. We found nine CFIR constructs important to AI implementation: evidence strength, relative advantage, adaptability, trialability, structural characteristics, tension for change, compatibility, access to knowledge and information, and knowledge and beliefs about the intervention. Consequently, the ERIC tool displayed the following strategies: identify and prepare champions, conduct educational meetings, promote adaptability, develop educational materials, and distribute educational materials. Conclusions: The potential value of AI in healthcare is acknowledged by end-users, however, the current tension for change needs to be sparked to facilitate sustainable implementation. Strategies that should be used are: increasing the access to knowledge and information through educational meetings and materials with committed local leaders. A trial phase for end-users to test and compare AI algorithms. Lastly, algorithms should be tailored to be adaptable to the local context and existing workflows. Applying these implementation strategies will bring us one step closer to realizing the value of AI in healthcare

A multicenter before-after study on reducing unnecessary diagnostics by changing the attitude of caregivers : Protocol for the RODEO project

Background: Appropriate use of diagnostic laboratory tests is challenging, and estimates of 20% for overutilization and 45% for underutilization have been reported. Introducing effective and sustainable solutions to stimulate optimal use of laboratory testing in clinical practice is a challenge. A recent pilot study from our group, focusing on increasing the awareness about appropriate laboratory testing with the aim of changing the mindset of health care workers, has shown promising results. In this project, we aim to extend this multistep intervention to the internal medicine departments of 4 large Dutch hospitals. We aim to reduce unnecessary laboratory testing by 5%. Objective: Our primary objective is to determine the effect of our intervention on diagnostic laboratory test order volume. Our secondary objectives are to determine the effect of our intervention on laboratory expenditure and order volumes, expenditures for other diagnostic modalities, and clinical patient outcomes. We will also analyze the barriers and facilitators for deimplementation of unnecessary laboratory testing. Methods: The main interventions of this before-after study will be an intensified supervision of residents by experienced physicians regarding test ordering, creating awareness through education and monthly feedback on ordering patterns, and changes in (computerized) order entry systems. Results: At the time of publication of this protocol, the project is in the phase of data collection. We expect to present data on reduction early in the fourth quarter of 2018. Conclusions: In this project, we aim to reduce the unnecessary diagnostic testing in the internal medicine departments of 4 teaching hospitals. Although the main interventions will be similar, each clinic is given the opportunity to focus on the specific facets of the interventions as deemed useful according to the local situation. If effective, the study provides a framework for a nationwide initiative for reducing inappropriate laboratory testing

A multicenter before-after study on reducing unnecessary diagnostics by changing the attitude of caregivers : Protocol for the RODEO project

Background: Appropriate use of diagnostic laboratory tests is challenging, and estimates of 20% for overutilization and 45% for underutilization have been reported. Introducing effective and sustainable solutions to stimulate optimal use of laboratory testing in clinical practice is a challenge. A recent pilot study from our group, focusing on increasing the awareness about appropriate laboratory testing with the aim of changing the mindset of health care workers, has shown promising results. In this project, we aim to extend this multistep intervention to the internal medicine departments of 4 large Dutch hospitals. We aim to reduce unnecessary laboratory testing by 5%. Objective: Our primary objective is to determine the effect of our intervention on diagnostic laboratory test order volume. Our secondary objectives are to determine the effect of our intervention on laboratory expenditure and order volumes, expenditures for other diagnostic modalities, and clinical patient outcomes. We will also analyze the barriers and facilitators for deimplementation of unnecessary laboratory testing. Methods: The main interventions of this before-after study will be an intensified supervision of residents by experienced physicians regarding test ordering, creating awareness through education and monthly feedback on ordering patterns, and changes in (computerized) order entry systems. Results: At the time of publication of this protocol, the project is in the phase of data collection. We expect to present data on reduction early in the fourth quarter of 2018. Conclusions: In this project, we aim to reduce the unnecessary diagnostic testing in the internal medicine departments of 4 teaching hospitals. Although the main interventions will be similar, each clinic is given the opportunity to focus on the specific facets of the interventions as deemed useful according to the local situation. If effective, the study provides a framework for a nationwide initiative for reducing inappropriate laboratory testing

Development of a set of patient reported outcome measures for patients with benign liver tumours and cysts: patient focus groups and systematic review

Background: Patient reported outcome measures (PROMs) may be useful for patients with benign liver tumours and cysts (BLTC) to evaluate the impact of treatment and/or guide shared decision making. Yet, a set of PROMs relevant to patients with BLTC is currently unavailable. In this study, we selected a PROMs set for patients with BLTC. Methods: Potentially relevant patient reported outcomes (PROs) were selected by psychologist-researchers based on keywords used or suggested by participants of two virtual focus groups meetings consisting of thirteen female BLTC patients with a median age of 50 years. Subsequently, patients were asked to report their most relevant PROs. PROMs identified by systematic literature review and computerized adaptive tests (CATs) in the Patient-Reported Outcomes Measurement Information System (PROMIS) were considered in selecting the final PROMs set to assess relevant outcomes. Results: The most important PROs were: insecurity/anxiety (11/12 patients), pain (9/12 patients), fatigue (8/12 patients), and limitations in daily life (5/12 patients). The literature review included 23 studies, which used various generic and disease-specific PROMs, often not measuring (all) relevant PROs. The final selected PROMs set included numerical rating scales for pain, two questions on overall health and quality of life and four PROMIS CATs. Conclusions: A PROMs set generically and efficiently measuring outcomes relevant for patients with BLTC was developed and may be used in future research and clinical practice

Development of a set of patient reported outcome measures for patients with benign liver tumours and cysts: patient focus groups and systematic review

BACKGROUND: Patient reported outcome measures (PROMs) may be useful for patients with benign liver tumours and cysts (BLTC) to evaluate the impact of treatment and/or guide shared decision making. Yet, a set of PROMs relevant to patients with BLTC is currently unavailable. In this study, we selected a PROMs set for patients with BLTC. METHODS: Potentially relevant patient reported outcomes (PROs) were selected by psychologist-researchers based on keywords used or suggested by participants of two virtual focus groups meetings consisting of thirteen female BLTC patients with a median age of 50 years. Subsequently, patients were asked to report their most relevant PROs. PROMs identified by systematic literature review and computerized adaptive tests (CATs) in the Patient-Reported Outcomes Measurement Information System (PROMIS) were considered in selecting the final PROMs set to assess relevant outcomes. RESULTS: The most important PROs were: insecurity/anxiety (11/12 patients), pain (9/12 patients), fatigue (8/12 patients), and limitations in daily life (5/12 patients). The literature review included 23 studies, which used various generic and disease-specific PROMs, often not measuring (all) relevant PROs. The final selected PROMs set included numerical rating scales for pain, two questions on overall health and quality of life and four PROMIS CATs. CONCLUSIONS: A PROMs set generically and efficiently measuring outcomes relevant for patients with BLTC was developed and may be used in future research and clinical practice

The pharmacotherapy team: A novel strategy to improve appropriate in-hospital prescribing using a participatory intervention action method

Aims: Prescribing medication is a complex process that, when done inappropriately, can lead to adverse drug events, resulting in patient harm and hospital admissions. Worldwide cost is estimated at 42 billion USD each year. Despite several efforts in the past years, medication-related harm has not declined. The aim was to determine whether a prescriber-focussed participatory action intervention, initiated by a multidisciplinary pharmacotherapy team, is able to reduce the number of in-hospital prescriptions containing ≥1 prescribing error (PE), by identifying and reducing challenges in appropriate prescribing. Methods: A prospective single-centre before- and after study was conducted in an academic hospital in the Netherlands. Twelve clinical wards (medical, surgical, mixed and paediatric) were recruited. Results: Overall, 321 patients with a total of 2978 prescriptions at baseline were compared with 201 patients with 2438 prescriptions postintervention. Of these, m456 prescriptions contained ≥1 PE (15.3%) at baseline and 357 prescriptions contained ≥1 PEs (14.6%) postintervention. PEs were determined in multidisciplinary consensus. On some study wards, a trend toward a decreasing number of PEs was observed. The intervention was associated with a nonsignificant difference in PEs (incidence rate ratio 0.96, 95% confidence interval 0.83–1.10), which was unaltered after correction. The most important identified challenges were insufficient knowledge beyond own expertise, unawareness of guidelines and a heavy workload. Conclusion: The tailored interventions developed with and implemented by stakeholders led to a statistically nonsignificant reduction in inappropriate in-hospital prescribing after a 6-month intervention period. Our prescriber-focussed participatory action intervention identified challenges in appropriate in-hospital prescribing on prescriber- and organizational level