Development and assessment of evidence-based strategies towards increased feasibility and transparency of investigator-initiated clinical trials in Switzerland

This work addresses the obligation to minimize research waste by identifying barriers and needs for support in important processes of clinical research and by proposing efficient strategies to improve the quality of research practice. Major sources of waste in clinical research have been identified by the “Increasing Value, Reducing Waste” series in The Lancet in 2014. Two considerations in this series address the problem of inefficient trial management and insufficient research transparency. Collected evidence suggests that inefficient management and monitoring of the procedural conduct of trials are a major source of waste even in well-designed studies addressing important questions. The absence of a continuous oversight of established trial processes endanger completion of trials in a set timeframe or even cause premature discontinuation. Increasing feasibility of clinical trials by providing an evidence-based strategy to effectively support the conduct of clinical trials at the University Hospital of Basel that has the potential to be transferred to the whole academic network for clinical research in Switzerland was aspired in this thesis. Along with feasibility, it is important that information of a trial including results is publicly available. In Switzerland, prospective registration of a clinical trial in a primary trial registry has been made mandatory by law in 2014 (Art 56 Human Research Act). We analyzed research transparency in terms of trial registration and results publication in a local setting in Switzerland to assess the successful implementation and enforcement of national efforts and identify potential barriers. In a first step, we systematically reviewed existing evidence on effective monitoring strategies both in the medical literature and across international clinical research stakeholder groups. Monitoring strategies varied in their methodological approach but the effectiveness of risk-based and triggered approaches could be shown with moderate certainty. However, we did not find evidence on the effect of these methods on the overall trial conduct. Based on these findings, we then engaged local, national and international stakeholder representatives in the creation of a comprehensive risk-tailored approach integrating monitoring in the broader context of trial management. We systematically reviewed information on risk indicators commonly used to guide monitoring in the academic setting and in industry and identified risk elements extended to the overall management of a clinical trial. In order to continuously visualize the status of identified risk elements throughout the study conduct, we initiated the user-centered development of a supporting study dashboard. The final risk-tailored approach consisted of the following components: A study-specific risk assessment prior to study start, selection and development of data based pathways addressing the identified risks, and the continuous visualization of the status of risk elements in a study dashboard. The generic content of the dashboard provides continuous information and support for risk indicators applicable to almost all clinical trials (Data quality, Recruitment, Retention, and Safety management) and the optional content is based on further study-specific items identified during the risk assessment (e.g. Follow-up visits, Re-consent process, Sampling management, Imaging quality). User-testing of the risk assessment and study dashboards developed on the basis of the assessment revealed that the continuous oversight of most critical elements and support of managing these elements efficiently supports the work routine of principle investigators, trial managers and trial monitors. In a second project of this thesis, we assessed current trial registration and publication for clinical intervention studies approved by the Ethics Committee North and Central Switzerland (EKNZ) in the last five years. Registration of all clinical trials would provide an overview of what research is being conducted at present and registries constitute an ideal platform for the publication and dissemination of research results.. Identifying factors influencing registration and potential barriers provides a basis for further initiatives to increase trial registration. Prospective trial registration has increased over the last five years and trials with higher risk category, multicenter trials and trials taking advantage of Clinical Trials Unit services were associated with higher registration rates. Although prospective trial registration prevalence has improved within the last five years within the EKNZ approved studies, a strong need for support in the registration process was identified in our qualitative evaluation. The impact of this work - and whether it eventually increases feasibility and transparency in clinical research critically depends on its implementation, evaluation, and refinement. Sharing current knowledge on effective monitoring strategies with trialists and monitors to choose evidence-based strategies for their trials constitutes a major support for investigator-initiated trials in the academic environment. The advancement of a risk-based trial monitoring approach into a comprehensive risk-tailored approach supporting the overall conduct of a trial and considering trial monitoring as an integrative part of trial management has the potential to efficiently optimize study processes. While an uptake of the study specific risk assessment and the use of a study dashboard as a standard process would be aspired for all RCTs in the future, improving the timeline and resources needed for the development of a study specific dashboard will be important to advance the generation of affordable and efficient dashboards for investigator-initiated trials. Sharing evidence on the registration behavior and perceived barriers by researchers in the local setting of the EKNZ helps to understand underlying processes and test measures for improvement. Supporting researchers in the process of trial registration and educating research institutes and investigators about the need and advantages of trial registration, has the potential to facilitate the implementation of automated processes and SOPs ensuring the registration of all clinical trials. Establishing trial registries as a primary platform for sharing research results should be aspired in the future

Visualization Literacy and Decision-making in Healthcare

The ability of workers in the healthcare industry to analyze, interpret and communicate with health data is critical to decision-making and impacts both health and business outcomes. Optimal decision-making requires having real-time access to information that provides useful insights and that lends itself to collaborative decision-making. Data visualizations have the potential to facilitate decision-making in healthcare when presented as a dashboard. However, dashboards have shown varying results in both effectiveness and adoption. Data or graphical literacy challenges experienced by health team members could complicate strategic decision-making through an inability to correctly interpret or summarize the information presented in a dashboard. One assumption is that visualization literacy and its impact on how people process health data visualizations play a part in the effective interpretation of information to support decision-making. To determine the impact of visualization literacy on the process of decision-making in a healthcare setting, we first developed and deployed a dashboard designed to provide important information for decision-makers on a clinical trial management team. We engaged Project Managers and Medical Managers in the project as key decision-makers on the team. The dashboard was integrated into the normal workflow of a clinical trial management team and designated as the tool used in the workflow to report on the trial status within the organization. Next, we administered a series of assessments to the key decision-makers. The assessments were designed to evaluate numeracy, visualization literacy, and the impact of both on the decision-making ability of participants. Decision-making was assessed using a common workflow scenario supported by visualizations from the deployed dashboard. Additionally, we were interested in exploring indicators related to job satisfaction that was collected during the project period through a formal engagement survey. We performed a general linear model to assess the relationship between the assessments and decision-making. Results of our project show a significant and clear relationship between visualization literacy and decision-making ability and an insignificant relationship between numeracy and decision-making ability. Job satisfaction scores for the participant group obtained through the engagement survey suggest favorable results. However, areas of opportunity for improvement illuminated through the survey included better tools and additional resources to support the execution of tasks, a better workload balance, and improvements in collaboration across departments and functions. The results of this project contribute to the informatics discipline by demonstrating that information obtained from data visualizations produced through the aggregation of multiple sources of data can be effective decision-support tools if they are designed with user skills and abilities in mind. The results of the project suggest an opportunity to develop more useful and usable tools to improve job satisfaction as well as organizational business objectives related to workforce staffing, job competencies, and learning and development initiatives

A Quality Improvement Program in a Safety Net Clinic Serving Vulnerable Populations

The Patient Protection and Affordable Care Act of 2010 resulted in major changes to healthcare infrastructure in the United States, with two main areas of concentration: healthcare financing and population health management. Quality improvement programs focus on improving healthcare quality for populations with conscious efforts to decrease healthcareassociated expenditures. Quality improvement interventions can include patient-reported outcomes, clinical decision support systems, and clinical dashboards. The purpose of the Doctor of Nursing Practice project was to formally implement a quality improvement program for chronic disease management in a safety net clinic serving vulnerable populations. The Donabedian model served as the conceptual model to frame the formal quality improvement program. The Plan-Do-Study-Act model guided the implementation of the formal quality improvement program. Despite the lack of statistically significant differences between pre- and post-implementation outcome measures, the Doctor of Nursing Practice project established a standard documentation process for several chronic diseases supported by a procedure manual, volunteer education modules, and clinical dashboards. Limitations of the project included the brief evaluation period, the low daily volume of patients with the selected chronic diseases, and the inadequate volunteer survey response rate. Recommendations for sustainability and future iterations involve an investigation into the documentation process of underperforming outcome measures, the identification of an effective process to solicit volunteer feedback on training modules, and the continuation of the clinical dashboard process to generate monthly compliance data to monitor documentation variation over time. The formalization of the quality improvement program in the safety net clinic during this Plan-Do-Study-Act cycle provided a strong foundation from which to launch the next Plan-Do-Study-Act cycle focusing on improved volunteer involvement

On people, data and systems : perspectives on routine health data processing and its digitalization in Tanzania

Background: Facility-based routine health information is captured in health management information systems by health care providers and is the main data source for health system planning and outcome monitoring in Tanzania and other low- and middle-income countries. While this system is fully digitalized in high-income countries, it is still partly paper-based in others. These use i) facility registers, ii) daily tally sheets and iii) monthly summary forms, which are later entered into the District Health Information System-2 software. These hybrid systems are prone to errors related to i) data entry, ii) calculation and iii) data transfer, with negative implications for data completeness and availability. The unavailability of data and lack of trust in its quality may lead to low data use for resource forecasting and planning, especially at subnational and facility levels. Through automatization of data processing, digital technology may be able to address these challenges, making it especially attractive in settings with high disease burdens and few resources. One example of a promising digital solution for low-resource settings is Smart Paper Technology, which produces automated electronic registers and summary reports by scanning bar-coded forms from individual service encounters. Implementation research, however, suggests a complex interplay between the implementation environment and the introduction and sustained institutionalization of technology. The aim of this thesis was to understand the social practices involved in generating and processing routine maternal and newborn health data, using paper-based and digital tools within the health management information system in Tanzania. Smart Paper Technology and the current health management information system with its different digital components are used for evaluation. Study I had the objective of understanding health care providers’ and facility/district managers’ perceptions of Smart Paper Technology and to assess time spent on documentation with the new system. A time-motion study, before and after the introduction of the technology, was applied together with eight focus group discussions with 18 health care providers from three health facilities and 11 in-depth interviews with healthcare managers from one district authority. Quantitative data was analyzed using descriptive statistics and bivariable modelling. Reflexive thematic analysis was used to analyze qualitative data. Findings illustrate challenges to Smart Paper Technology implementation related to pre-existing health system bottlenecks, e.g. lack of human resources, supervision and transport, but also a difference in values assigned to the new system by health care providers and their managers. Health care providers found Smart Paper Technology useful and applicable to their context with perceived benefits for documentation and clinical care. These experiences were confirmed by quantitative data, showing no significant difference between time spent on overall documentation pre- and post-introduction of Smart Paper Technology (27 vs 26 %, adjusted p 0.763) but an increase in time spent on clinical tasks (26.9 vs 37.1%, adjusted p 0.001). Health care managers, in contrast, found it difficult to identify benefits from the new technology for their own work related to national reporting, due to access problems with the digital dashboard and questionable quality of Smart Paper Technology data. They therefore continued to focus managerial efforts on the existing health management information system. Study II’s objective was to assess the quality of Smart Paper Technology data for maternal care services related to i) completeness and timeliness and ii) internal consistency. A cross-sectional survey over 12 months was performed in 13 health facilities using data from the Smart Paper Technology system and District Health Information System-2. Descriptive statistics were produced based on indicators derived from the World Health Organization’s Data Quality Review Toolkit. Results show that data quality of the Smart Paper Technology system was not superior to that of the pre-existing health management information system overall. This may be linked to the effects of duplicate data entry on health care provider performance and consequently on data completeness. Smart Paper Technology performed slightly better in some aspects of internal consistency: Fewer health facilities produced only one or two outliers with Smart Paper Technology in each month of the study period (antenatal care=4, care during labour = 6, postnatal care =4) than with the District Health Information System-2 (antenatal care= 7, care during labour= 9, postnatal care= 6). Smart Paper Technology also yielded higher consistency for the documented postpartum use of oxytocin in relation to the number of documented deliveries with 62% of facilities showing a less than 10% difference between these indicators as opposed to 38% for the District Health Information System-2. However, the pre-existing system demonstrated better data quality in all other quality dimensions, i.e. data completeness, timeliness and consistency of data trends over the study period. Study III: The objective was to improve understanding about the processes involved in health care providers’ data use; which type of information is used together with health management information system data and for what purposes. A constructivist grounded theory-based ethnographic approach was applied, consisting of i) 14 in-depth interviews with health care providers from maternity wards in two hospitals, as well as ii) 48 hours of observation in the maternity wards and ii) two focus group discussions with 11 health care providers from the same hospitals. Findings illustrate how health care providers appropriated numeric data from the official health management information system and narrative data that they had produced for clinical documentation to safeguard social relationships with superiors, patients and the community they served. While they identified themselves as data collectors and not users of the health management information system, they applied narrative clinical documentation systems to service improvement and to protect themselves against litigation or managerial reprimands. Study IV’s objective was to generate knowledge on experiences and perceptions of health care policymakers in Tanzania related to data, data systems and the implementation of digital technology to support health information management. 16 in-depth interviews with healthcare managers from national and subnational levels were conducted and analyzed using reflexive thematic analysis. Results suggest that the health management information system in Tanzania is governed using institutional and discretionary power. Institutional power was mainly used at the national level to conceptualize data collection and processing systems and the scale-up of digitalization. Discretionary power was mainly used for implementation at subnational level. The use of different power practices was influenced by available funding and health care managers’ perception that health care providers, the primary data collectors, lack motivation to perform and are unpredictable in their actions regarding the continuous production of good data quality. Conclusions: Acceptance or rejection of digital technology was influenced to a considerable extent by social practices at all levels of the health system. These included actors’ perceived benefits of maintaining existing social practices. These practices, which are part of an organization’s culture related to data and data processes, require attention during the conceptualization and implementation of health information systems. Numeric and contextual information is used concomitantly at various levels of Tanzania’s health management information system. The health management information system in Tanzania forms a complex adaptive system with inherently high levels of unpredictability, non-linearity, self-organization and adaptation over time. Health care managers’ power practices in the conceptualization and implementation of policies reflect this complexity. Contextual factors affect digital technology integration and have consequences for data quality and use of digital AND paper-based health management information systems. Context may therefore be even more important than the format and technology of data collection and processing

Development of an orientation toolkit for the Eastern Health patient monitoring program

Background: The Eastern Health (EH) Remote Patient Monitoring (RPM) Program identified the need to develop an electronic orientation toolkit for the orientation of newly hired staff members. Materials previously existed for orientation; however, a more robust toolkit was required as the original information was limited and not available electronically Purpose: To develop an electronic orientation toolkit for the EH RPM program. Methods: The four methods were: 1) integrated literature review, 2) consultations with the EH RPM team members via semi-structured interviews, 3) an environmental scan conducted with other RPM programs in Canada via email, and 4) development of an orientation toolkit. Results: The need for an orientation toolkit and the content to include was confirmed by completing an integrated literature review, an environmental scan, and consultations with EH RPM staff members. Specific competencies identified were enhanced communication skills, computer and technology skills. Orientation to these competencies was also identified as essential to fulfilling daily duties. Based on these findings, an orientation toolkit was developed. The toolkit consists of five modules: 1) Introduction to orientation, 2) Introduction to Remote Patient Monitoring, 3) Computer Technology, 4) Educational Components, and 5) Virtual Presence. Each module contains learning objectives, program information, checklists, and reflection exercises. All newly hired staff members must complete these modules, along with simulation activities and other learning activities with their mentors. Conclusion: The orientation toolkit will orient all newly hired staff members to the EH RPM program and promote a comprehensive and consistent orientation for all new employees

Predicting the Risk of Falling with Artificial Intelligence

Predicting the Risk of Falling with Artificial Intelligence Abstract Background: Fall prevention is a huge patient safety concern among all healthcare organizations. The high prevalence of patient falls has grave consequences, including the cost of care, longer hospital stays, unintentional injuries, and decreased patient and staff satisfaction. Preventing a patient from falling is critical in maintaining a patient’s quality of life and averting the high cost of healthcare expenses. Local Problem: Two hospitals\u27 healthcare system saw a significant increase in inpatient falls. The fall rate is one of the nursing quality indicators, and fall reduction is a key performance indicator of high-quality patient care. Methods: This quality improvement evidence-based observational project compared the rate of fall (ROF) between the experimental and control unit. Pearson’s chi-square and Fisher’s exact test were used to analyze and compare results. Qualtrics surveys evaluated the nurses’ perception of AI, and results were analyzed using the Mann-Whitney Rank Sum test. Intervention. Implementing an artificial intelligence-assisted fall predictive analytics model that can timely and accurately predict fall risk can mitigate the increase in inpatient falls. Results: The pilot unit (Pearson’s chi-square = p pp\u3c0.001). Conclusions: AI-assisted automatic fall predictive risk assessment produced a significant reduction if the number of falls, the ROF, and the use of fall countermeasures. Further, nurses’ perception of AI improved after the introduction of FPAT and presentation