Use of electronic patient data storage for evaluating and setting the risk category of late effects in childhood cancer survivors.

BACKGROUND: Many of the late effects of cancer treatment in childhood may occur even decades after the treatment, and only a minority of the survivors remain as healthy as their peers. Providing appropriate long-term care for childhood cancer survivors after transition to primary health care is a challenge. Both survivors and primary care providers need information on potential late effects. The lack of a systematic late effect follow-up plan may lead to excessive use of health care services or delayed intervention. While manual compilation of individual follow-up plans is time consuming for experienced clinicians, electronic algorithms may be feasible. PROCEDURE: In Finland, international guidelines for determining the risk of late effects have been implemented. Nationally, Turku University Hospital was asked with developing an automatized system for calculating the risk of late effects, based on electronic patient records saved in the hospital data lake. An electronic algorithm that uses details from exposure-based health screening guidelines published by the Children's Oncology Group was created. The results were compared with those manually extracted by an experienced clinician. RESULTS: Significant concordance between the manual and algorithm-based risk classification was found. A total of 355 patients received a classification using the algorithm, and 325 of those matched with the manual categorization, producing a Cohen's coefficient of 0.91 (95% confidence interval 0.88-0.95). CONCLUSION: Automated algorithms can be used to categorize childhood cancer survivors efficiently and reliably into late effect risk groups. This further enables automatized compilation of appropriate individual late effect follow-up plan for all survivors.

Towards improved health service quality in Tanzania: contribution of a supportive supervision approach to increased quality of primary healthcare.

BACKGROUND: Universal Health Coverage only leads to the desired health outcomes if quality of health services is ensured. In Tanzania, quality has been a major concern for many years, including the problem of ineffective and inadequate routine supportive supervision of healthcare providers by council health management teams. To address this, we developed and assessed an approach to improve quality of primary healthcare through enhanced routine supportive supervision. METHODS: Mixed methods were used, combining trends of quantitative quality of care measurements with qualitative data mainly collected through in-depth interviews. The former allowed for identification of drivers of quality improvements and the latter investigated the perceived contribution of the new supportive supervision approach to these improvements. RESULTS: The results showed that the new approach managed to address quality issues that could be solved either solely by the healthcare provider, or in collaboration with the council. The new approach was able to improve and maintain crucial primary healthcare quality standards across different health facility level and owner categories in various contexts. CONCLUSION: Together with other findings reported in companion papers, we could show that the new supportive supervision approach not only served to assess quality of primary healthcare, but also to improve and maintain crucial primary healthcare quality standards. The new approach therefore presents a powerful tool to support, guide and drive quality improvement measures within council. It can thus be considered a suitable option to make routine supportive supervision more effective and adequate.

Towards improved health service quality in Tanzania: appropriateness of an electronic tool to assess quality of primary healthcare.

BACKGROUND: Progress in health service quality is vital to reach the target of Universal Health Coverage. However, in order to improve quality, it must be measured, and the assessment results must be actionable. We analyzed an electronic tool, which was developed to assess and monitor the quality of primary healthcare in Tanzania in the context of routine supportive supervision. The electronic assessment tool focused on areas in which improvements are most effective in order to suit its purpose of routinely steering improvement measures at local level. METHODS: Due to the lack of standards regarding how to best measure quality of care, we used a range of different quantitative and qualitative methods to investigate the appropriateness of the quality assessment tool. The quantitative methods included descriptive statistics, linear regression models, and factor analysis; the qualitative methods in-depth interviews and observations. RESULTS: Quantitative and qualitative results were overlapping and consistent. Robustness checks confirmed the tool's ability to assign scores to health facilities and revealed the usefulness of grouping indicators into different quality dimensions. Focusing the quality assessment on processes and structural adequacy of healthcare was an appropriate approach for the assessment's intended purpose, and a unique key feature of the electronic assessment tool. The findings underpinned the accuracy of the assessment tool to measure and monitor quality of primary healthcare for the purpose of routinely steering improvement measures at local level. This was true for different level and owner categories of primary healthcare facilities in Tanzania. CONCLUSION: The electronic assessment tool demonstrated a feasible option for routine quality measures of primary healthcare in Tanzania. The findings, combined with the more operational results of companion papers, created a solid foundation for an approach that could lastingly improve services for patients attending primary healthcare. However, the results also revealed that the use of the electronic assessment tool outside its intended purpose, for example for performance-based payment schemes, accreditation and other systematic evaluations of healthcare quality, should be considered carefully because of the risk of bias, adverse effects and corruption.

Adaptation of a Personalized Electronic Care Planning Tool for Cancer Follow-up Care: Formative Study.

BACKGROUND: Most patients diagnosed with colorectal cancer will survive for at least 5 years; thus, engaging patients to optimize their health will likely improve outcomes. Clinical guidelines recommend patients receive a comprehensive care plan (CP) when transitioning from active treatment to survivorship, which includes support for ongoing symptoms and recommended healthy behaviors. Yet, cancer care providers find this guideline difficult to implement. Future directions for survivorship care planning include enhancing information technology support for developing personalized CPs, using CPs to facilitate self-management, and assessing CPs in clinical settings. OBJECTIVE: We aimed to develop an electronic tool for colorectal cancer follow-up care (CFC) planning. METHODS: Incorporating inputs from health care professionals and patient stakeholders is fundamental to the successful integration of any tool into the clinical workflow. Thus, we followed the Integrate, Design, Assess, and Share (IDEAS) framework to adapt an existing application for stroke care planning (COMPASS-CP) to meet the needs of colorectal cancer survivors (COMPASS-CP CFC). Constructs from the Consolidated Framework for Implementation Research (CFIR) guided our approach. We completed this work in 3 phases: (1) gathering qualitative feedback from stakeholders about the follow-up CP generation design and workflow; (2) adapting algorithms and resource data sources needed to generate a follow-up CP; and (3) optimizing the usability of the adapted prototype of COMPASS-CP CFC. We also quantitatively measured usability (target average score ≥70; range 0-100), acceptability, appropriateness, and feasibility. RESULTS: In the first phase, health care professionals (n=7), and patients and caregivers (n=7) provided qualitative feedback on COMPASS-CP CFC that informed design elements such as selection, interpretation, and clinical usefulness of patient-reported measures. In phase 2, we built a minimal viable product of COMPASS-CP CFC. This tool generated CPs based on the needs identified by patient-completed measures (including validated patient-reported outcomes) and electronic health record data, which were then matched with resources by zip code and preference to support patients' self-management. Elements of the CFIR assessed revealed that most health care professionals believed the tool would serve patients' needs and had advantages. In phase 3, the average System Usability Scale score was above our target score for health care professionals (n=5; mean 71.0, SD 15.2) and patients (n=5; mean 95.5, SD 2.1). Participants also reported high levels of acceptability, appropriateness, and feasibility. Additional CFIR-informed feedback, such as desired format for training, will inform future studies. CONCLUSIONS: The data collected in this study support the initial usability of COMPASS-CP CFC and will inform the next steps for implementation in clinical care. COMPASS-CP CFC has the potential to streamline the implementation of personalized CFC planning to enable systematic access to resources that will support self-management. Future research is needed to test the impact of COMPASS-CP CFC on patient health outcomes.

Formative Provider Testing of a New Encounter Decision Aid for Smoking Cessation: Questionnaire Study.

BACKGROUND: Smoking cessation is an essential part of preventing and reducing the risk of smoking-associated morbidity and mortality. However, there is often little time to discuss smoking cessation in primary care. Decision aids (DAs) designed for clinic visits (encounter DAs) need to be clear, short, and concise to optimize therapeutic education, increase interaction, and improve the therapeutic alliance. Such a DA for smoking cessation could potentially improve counseling and increase the use of pharmacological treatments. OBJECTIVE: We aimed to collect feedback on an electronic encounter DA that facilitates physician-patient interaction and shared decision-making for smoking cessation in primary care. METHODS: We developed an electronic, encounter DA (howtoquit.ch) from a paper version created by our team in 2017 following user-centered design principles. The DA is a 1-page interactive website presenting and comparing medications for tobacco cessation and electronic cigarettes. Each smoking cessation medication has a drop down menu that presents additional information, a video demonstration, and prescribing information for physicians. To test the DA, we submitted a questionnaire to approximately 20 general practitioner residents of an academic general medicine department, 5 general practitioners, and 6 experts in the field of smoking cessation. The questionnaire consisted of 4 multiple-choice and 2 free-text questions assessing the usability or acceptability of the DA, the acquisition of new knowledge for practitioners, the perceived utility in supporting shared decision-making, perceived strengths and weaknesses, and whether the participants would recommend the tool to other clinicians. RESULTS: In all, 6 residents, 3 general practitioners in private practice, and 2 tobacco cessation experts completed the questionnaire (N=11), with 4 additional experts providing open-text feedback. On the 11 questionnaires, the DA was rated as practical and intuitive (mean 4.6/5), and providers felt it supported shared decision-making (mean 4.4/5), as comparisons were readily possible. Inclusion of explanatory videos was seen as a bonus. Several changes were suggested, like grouping together similar medications and adding a landing page to briefly explain the site. Changes were implemented according to end-user comments. CONCLUSIONS: The overall assessment of the encounter DA by a group of physicians and experts was positive. The ultimate objective is to have the tool deployed and easily accessible for all to use.

Implementation and outcome of an electronic tool for detection of paracetamol overdose in a tertiary care hospital.

Background Paracetamol is a widely used analgesic and antipyretic drug in hospitals. The development and implementation of an electronic tool with algorithm-based alerts (e-agent) in a clinical information system could reduce the risk of overdose. Objective In this study, the performance of such an e-agent developed to detect paracetamol overdosing was analyzed. Setting Swiss tertiary care hospital. Method All patients ≥ 18 years old who had documented paracetamol administration in the used clinical information system during 2017 were retrospectively screened for an absolute and relative overdosing of paracetamol (> 4 g and > 60 mg/kg/24 h, respectively). This was compared with the patients for which the e-agent had, during the same period, prospectively made an alert for absolute or relative overdosing or for a dosing interval < 4 h (potentially leading to an absolute overdose). Main outcome measure E-agent performance defined as detection rate. Results of the 13,196 adult patients who received at least one dose of paracetamol, 2292 were exposed at least once to > 4 g/day (17.4%), 39 of these (0.3% of total) were given > 5 g paracetamol. None received more than 6 g. The e-agent detected 87.2% of cases with doses > 5 g. In most cases (87.9%), the cause of the absolute overdose was a switch from intravenous to oral paracetamol, resulting in an absolute overdose the day of the change. The maximal daily dose of 60 mg/kg was exceeded in 30.1% of patients weighing < 50 kg, as well as in 42.3% of patients weighing < 60 kg. The e-agent detected 73.4% and 75.5% of those cases. Multiple absolute overdoses were found in 204 patients. The e-agent detected 72.7% of those. 90 multiple overdoses occurred during the same hospital stay and 11 on consecutive days. Conclusion Paracetamol overdose is a common medication error in hospitalized patients, which may occur due to process failures such as wrong timing when changing administration route or when factors like comedication and low body weight are ignored. The e-agent detects cases of paracetamol overdose, and therefore, can help prevent this kind of medication error in the clinical setting.

Effectiveness of an Electronic Communication Tool on Transitions in Care From the Intensive Care Unit: Protocol for a Cluster-Specific Pre-Post Trial.

BACKGROUND: Transitions in care are vulnerable periods in health care that can expose patients to preventable errors due to incomplete or delayed communication between health care providers. Transitioning critically ill patients from intensive care units (ICUs) to other patient care units (PCUs) is particularly risky, due to the high acuity of the patients and the diversity of health care providers involved in their care. Instituting structured documentation to standardize written communication between health care providers during transitions has been identified as a promising means to reduce communication breakdowns. We developed an evidence-informed, computer-enabled, ICU-specific structured tool-an electronic transfer (e-transfer) tool-to facilitate and standardize the composition of written transfer summaries in the ICUs of one Canadian city. The tool consisted of 10 primary sections with a user interface combination of structured, automated, and free-text fields. OBJECTIVE: Our overarching goal is to evaluate whether implementation of our e-transfer tool will improve the completeness and timeliness of transfer summaries and streamline communications between health care providers during high-risk transitions. METHODS: This study is a cluster-specific pre-post trial, with randomized and staggered implementation of the e-transfer tool in four hospitals in Calgary, Alberta. Hospitals (ie, clusters) were allocated randomly to cross over every 2 months from control (ie, dictation only) to intervention (ie, e-transfer tool). Implementation at each site was facilitated with user education, point-of-care support, and audit and feedback. We will compare transfer summaries randomly sampled over 6 months postimplementation to summaries randomly sampled over 6 months preimplementation. The primary outcome will be a binary composite measure of the timeliness and completeness of transfer summaries. Secondary measures will include overall completeness, timeliness, and provider ratings of transfer summaries; hospital and ICU lengths of stay; and post-ICU patient outcomes, including ICU readmission, adverse events, cardiac arrest, rapid response team activation, and mortality. We will use descriptive statistics (ie, medians and means) to describe demographic characteristics. The primary outcome will be compared within each hospital pre- and postimplementation using separate logistic regression models for each hospital, with adjustment for patient characteristics. RESULTS: Participating hospitals were cluster randomized to the intervention between July 2018 and January 2019. Preliminary extraction of ICU patient admission lists was completed in September 2019. We anticipate that evaluation data collection will be completed by early 2021, with first results ready for publication in spring or summer 2021. CONCLUSIONS: This study will report the impact of implementing an evidence-informed, computer-enabled, ICU-specific structured transfer tool on communication and preventable medical errors among patients transferred from the ICU to other hospital care units. TRIAL REGISTRATION: ClinicalTrials.gov NCT03590002; https://www.clinicaltrials.gov/ct2/show/NCT03590002. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/18675.

Measuring Atopic Dermatitis Disease Severity: The Potential for Electronic Tools to Benefit Clinical Care.

Severity of atopic dermatitis (AD) correlates with impact on health-related quality of life (HRQoL), work productivity, and burden on health systems. Tools to measure severity inform regulatory approval, drug access, and value- or measurement-based care. A core set of instruments for measuring AD has been established. Clinician-reported tools are divided broadly into multi-item global estimates or precise calculators that also weigh affected corporeal surfaces. Increasingly, subjective patient-reported outcomes are valued, with the potential to capture vast amounts of health-related data. Patient-reported outcomes can be disease-agnostic, skin-related, or AD-specific, and evaluate global disease, itch severity, long-term control, or overall HRQoL. Patient-reported outcomes are expansive in number; therefore, item banks and adaptive digital user interfaces will be increasingly needed, along with capacity to store and analyze data. Technologies for AD include tools for communication, severity assessment, or data exchange, as well as electronic health records (EHRs). For clinicians, a limited number of applications exist, with relatively poor interoperability with EHRs to date. For patients, a growing number of mobile health (mHealth) applications exhibit variable compliance with international guidelines for self-management. Data privacy and information security governance are key considerations in the development of information technologies for AD. Integrated and streamlined digital operational processes for disease measurements may build capacity for high value and efficient care of patients with AD across the globe.

Factors influencing implementation of a computerized, individualized, culturally tailored lupus decision aid in lupus clinics: a qualitative semi-structured interview study.

OBJECTIVE: To identify factors that might facilitate or impede the implementation of a shared decision-making in lupus electronic tool (SMILE) in clinics by assessing perspectives of clinicians, clinic champions, and patient advocacy organization leaders. METHODS: We conducted a series of semi-structured telephone interviews (25-45 minutes) about facilitators and barriers of implementing the SMILE decision-aid tool with 23 lupus care providers (18 physicians, 5 champions), and leaders of two patient advocacy organizations. Interviews were audio recorded, transcribed, coded, and analyzed. RESULTS: Physicians and clinic champions were from 18 geographically diverse US clinics. The patient advocacy leaders were from the Lupus Foundation of America and the Arthritis Foundation. Most of the clinics were rheumatology specialty (94%), at university-based academic centers (72%), located in urban areas (72%), had a specialized lupus clinic (72%), were very interested (72%) in the SMILE tool and were ready to implement it (89%). Several specific factors, composed as four themes, were identified that could either facilitate or impede the implementation of the SMILE tool: (1) patient-related theme: patient recruitment and education, and the clinic visit time; (2) clinic-related theme: staff work-load and time, and physical space to view and use the SMILE tool; (3) technology-related theme: Wi-Fi connection and iPad navigation; and (4) management-related theme: influence on the clinics' daily workflow, the need of a study champion and coordination, and leadership support. CONCLUSION: Physicians, staff, and patient advocacy leaders perceived the SMILE as a promising tool to facilitate patient-provider communication and quality improvement in lupus. Identification of the patient-, clinic-, technology-, and management-related barriers to the SMILE implementation will allow its integration into busy clinical practice workflow. Key Points • Physicians, staff and patient advocacy leaders perceived computerized lupus decision aid to be a promising tool to facilitate shared decision-making for lupus treatment. • Stakeholder identified patient-related, clinic-resource-related, technology-related and clinic-management related themes as barriers or facilitators to viewing computerized lupus decision aid during regular clinic visits.

Creation of an iOS and Android Mobile Application for Inferior Vena Cava (IVC) Filters: A Powerful Tool to Optimize Care of Patients with IVC Filters.

Owing to a myriad of inferior vena cava (IVC) filter types and their potential complications, rapid and correct identification may be challenging when encountered on routine imaging. The authors aimed to develop an interactive mobile application that allows recognition of all IVC filters and related complications, to optimize the care of patients with indwelling IVC filters. The FDA Premarket Notification Database was queried from 1980 to 2014 to identify all IVC filter types in the United States. An electronic search was then performed on MEDLINE and the FDA MAUDE database to identify all reported complications associated with each device. High-resolution photos were taken of each filter type and corresponding computed tomographic and fluoroscopic images were obtained from an institutional review board-approved IVC filter registry. A wireframe and storyboard were created, and software was developed using HTML5/CSS compliant code. The software was deployed using PhoneGap (Adobe, San Jose, CA), and the prototype was tested and refined. Twenty-three IVC filter types were identified for inclusion. Safety data from FDA MAUDE and 72 relevant peer-reviewed studies were acquired, and complication rates for each filter type were highlighted in the application. Digital photos, fluoroscopic images, and CT DICOM files were seamlessly incorporated. All data were succinctly organized electronically, and the software was successfully deployed into Android (Google, Mountain View, CA) and iOS (Apple, Cupertino, CA) platforms. A powerful electronic mobile application was successfully created to allow rapid identification of all IVC filter types and related complications. This application may be used to optimize the care of patients with IVC filters.

Using clinical variables to guide surgical site infection detection: a novel surveillance strategy.

BACKGROUND: Surgical site infections (SSIs) are a common and expensive health care-associated infection, and are used as a health care quality benchmark. As such, SSI detection is a major focus of infection prevention programs. In an effort to improve on conventional surveillance methods, a simple algorithm for SSI detection was developed using clinical variables not traditionally included in National Healthcare Safety Network definitions. METHODS: A case-control study was conducted among surgeries performed at the Veterans Affairs Boston Healthcare System between January 2008 and December 2009. SSI cases were matched to controls without SSI. Clinical variables (administrative, microbiological, pharmacy, radiology) were compared between the groups to determine those that best identified SSI. RESULTS: A total of 70 SSIs were matched to 70 controls. On multivariable analysis, variables significantly associated with SSI identification were wound culture order, computed tomography scan/magnetic resonance imaging order, antibiotic order within 30 days after surgery, and application of a relevant International Classification of Disease, Ninth Revision code. Among patients with no SSI identifiers, 98% were correctly classified as having no SSI. Among patients with multiple SSI identifiers, 97.1% were correctly identified as having SSI. The area under the curve for this model was 0.87. CONCLUSION: We have derived a novel surveillance algorithm for SSI detection with excellent operating characteristics. This algorithm could be automated to streamline infection control efforts.

Collaborative interaction points in post-discharge stroke care.

INTRODUCTION: Lack of appropriate electronic tools for supporting patient involvement and collaboration with care professionals is a problem in health care. METHODS: Care and rehabilitation processes of post-discharge stroke patients were analysed using the concept of interaction points where patients, next-of-kin and care professionals interact and exchange information. Thirteen interviews with care professionals and five non-participatory observations were performed. Data were analysed using content analysis and modelling of interaction points in the patient journey. RESULTS: Patient participation and interaction patterns vary; patients requiring home care have a passive role and next-of-kin or nurses become advocates by coordinating care on behalf of the patient, whereas patients who are able to visit primary care coordinate their own care by initiating interactions. Important categories of participation include the following: participation in care planning, in monitoring risk factors and in rehabilitation planning. CONCLUSIONS: Designing a supportive electronic tool requires understanding the interactions and patients' activity levels at each interaction point. A tool for patients with higher activity level should support them to coordinate their own care, whereas for a less-active patient group, the tool could focus on supporting next-of-kin and care professionals in motivating, guiding and including passive patients in their care and rehabilitation processes.