Natural history of abdominal pain in family practice: Longitudinal study of electronic medical record data in southwestern Ontario.

OBJECTIVE: To examine the frequency, natural history, and outcomes of 3 subtypes of abdominal pain (general abdominal pain, epigastric pain, localized abdominal pain) among patients visiting Canadian family practices. DESIGN: Retrospective cohort study with a 4-year longitudinal analysis. SETTING: Southwestern Ontario. PARTICIPANTS: A total of 1790 eligible patients with International Classification of Primary Care codes for abdominal pain from 18 family physicians in 8 group practices. MAIN OUTCOME MEASURES: The symptom pathways, the length of an episode, and the number of visits. RESULTS: Abdominal pain accounted for 2.4% of the 15,149 patient visits and involved 14.0% of the 1790 eligible patients. The frequencies of each of the 3 subtypes were as follows: localized abdominal pain, 89 patients, 1.0% of visits, and 5.0% of patients; general abdominal pain, 79 patients, 0.8% of visits, and 4.4% of patients; and epigastric pain, 65 patients, 0.7% of visits, and 3.6% of patients. Those with epigastric pain received more medications, and patients with localized abdominal pain underwent more investigations. Three longitudinal outcome pathways were identified. Pathway 1, in which the symptom remains at the end of the visit with no diagnosis, was the most common among patients with all subtypes of abdominal symptoms at 52.8%, 54.4%, and 50.8% for localized, general, and epigastric pain, respectively, and the symptom episodes were relatively short. Less than 15% of patients followed pathway 2, in which a diagnosis is made and the symptom persists, and yet the episodes were long with 8.75 to 16.80 months' mean duration and 2.70 to 4.00 mean number of visits. Pathway 3, in which a diagnosis is made and there are no further visits for that symptom, occurred approximately one-third of the time, with about 1 visit over about 2 months. Prior chronic conditions were common across all 3 subtypes of abdominal pain ranging from 72.2% to 80.0%. Psychological symptoms consistently occurred at a rate of approximately one-third. CONCLUSION: The 3 subtypes of abdominal pain differed in clinically important ways. The most frequent pathway was that the symptom remained with no diagnosis, suggesting a need for clinical approaches and education programs for care of symptoms themselves, not merely in the service of coming to a diagnosis. The importance of prior chronic conditions and psychological conditions was highlighted by the results.

Is primary health care ready for artificial intelligence? Stakeholder perspectives: Worth the risk as long as you do it well.

Context: The effective deployment of artificial intelligence (AI) in primary health care requires a match between the AI tools that are being developed and the needs of primary health care practitioners and patients. Currently, the majority of AI development targeted toward potential application in primary care is being conducted without the involvement of these stakeholders. Objective: To identify key issues regarding the use of AI tools in primary health care by exploring the views of primary health care and digital health stakeholders. Study Design: A descriptive qualitative approach was taken in this study. Fourteen in-depth interviews were conducted with primary care and digital health stakeholders. Setting: Province of Ontario, Canada Population studied: Primary health care and digital health stakeholders Outcome Measures: N/A Results: Two main themes emerged from the data analysis: Worth the Risk as Long as You Do It Well; and, Mismatch Between Envisioned Uses and Current Reality. Participants noted that AI could have value if used for specific purposes, for example: supporting care for patients; reducing practitioner burden; analyzing existing evidence; managing patient populations; and, supporting operational efficiencies. Participants identified facilitators of AI being used for these purposes including: use of relevant case studies/success stories with realistic uses of AI highlighted; easy or low risk applications; and, end user involvement. However, barriers to the use of AI included: data quality; digital divide/equity; distrust of AI including security/privacy issues; for-profit motives; need for transparency about how AI works; and, fear about impact on practitioners regarding clinical judgement. Conclusion: AI will continue to become more prominent in primary health care. There is potential for positive impact, however there are many factors that need to be considered regarding the implementation of AI. The findings of this study can help to inform the development and deployment of AI tools in primary health care.

Identifying priorities for artificial intelligence and primary care in ontario: A multi-stakeholder engagement event.

CONTEXT: Artificial intelligence (AI) is increasingly being recognized as having potential importance to primary care (PC). However, there is a gap in our understanding about where to focus efforts related to AI for PC settings, especially given the current COVID-19 pandemic. OBJECTIVE: To identify current priority areas for AI and PC in Ontario, Canada. STUDY DESIGN: Multi-stakeholder engagement event with facilitated small and large group discussions. A nominal group technique process was used to identify and rank challenges in PC that AI may be able to support. Mentimeter software was used to allow real-time, anonymous and independent ranking from all participants. A final list of priority areas for AI and PC, with key considerations, was derived based on ranked items and small group discussion notes. SETTING: Ontario, Canada. POPULATION STUDIED: Digital health and PC stakeholders. OUTCOME MEASURES: N/A. RESULTS: The event included 8 providers, 8 patient advisors, 4 decision makers, 3 digital health stakeholders, and 12 researchers. Nine priority areas for AI and PC were identified and ranked, which can be grouped into those intended to support physician (preventative care and risk profiling, clinical decision support, routine task support), patient (self-management of conditions, increased mental health care capacity and support), or system-level initiatives (administrative staff support, management and synthesis of information sources); and foundational areas that would support work on other priorities (improved communication between PC and AI stakeholders, data sharing and interoperability between providers). Small group discussions identified barriers and facilitators related to the priorities, including data availability, quality, and consent; legal and device certification issues; trust between people and technology; equity and the digital divide; patient centredness and user-centred design; and the need for funding to support collaborative research and pilot testing. Although identified areas do not explicitly mention COVID-19, participants were encouraged to think about what would be feasible and meaningful to accomplish within a few years, including considerations of the COVID-19 pandemic and recovery phases. CONCLUSIONS: A one-day multi-stakeholder event identified priority areas for AI and PC in Ontario. These priorities can serve as guideposts to focus near-term efforts on the planning, development, and evaluation of AI for PC.

Is primary health care ready for artificial intelligence? What do primary health care stakeholders say?

BACKGROUND: Effective deployment of AI tools in primary health care requires the engagement of practitioners in the development and testing of these tools, and a match between the resulting AI tools and clinical/system needs in primary health care. To set the stage for these developments, we must gain a more in-depth understanding of the views of practitioners and decision-makers about the use of AI in primary health care. The objective of this study was to identify key issues regarding the use of AI tools in primary health care by exploring the views of primary health care and digital health stakeholders. METHODS: This study utilized a descriptive qualitative approach, including thematic data analysis. Fourteen in-depth interviews were conducted with primary health care and digital health stakeholders in Ontario. NVivo software was utilized in the coding of the interviews. RESULTS: Five main interconnected themes emerged: (1) Mismatch Between Envisioned Uses and Current Reality-denoting the importance of potential applications of AI in primary health care practice, with a recognition of the current reality characterized by a lack of available tools; (2) Mechanics of AI Don't Matter: Just Another Tool in the Toolbox- reflecting an interest in what value AI tools could bring to practice, rather than concern with the mechanics of the AI tools themselves; (3) AI in Practice: A Double-Edged Sword-the possible benefits of AI use in primary health care contrasted with fundamental concern about the possible threats posed by AI in terms of clinical skills and capacity, mistakes, and loss of control; (4) The Non-Starters: A Guarded Stance Regarding AI Adoption in Primary Health Care-broader concerns centred on the ethical, legal, and social implications of AI use in primary health care; and (5) Necessary Elements: Facilitators of AI in Primary Health Care-elements required to support the uptake of AI tools, including co-creation, availability and use of high quality data, and the need for evaluation. CONCLUSION: The use of AI in primary health care may have a positive impact, but many factors need to be considered regarding its implementation. This study may help to inform the development and deployment of AI tools in primary health care.

Does spaced education improve clinical knowledge among Family Medicine residents? A cluster randomized controlled trial.

Spaced education is a learning strategy to improve knowledge acquisition and retention. To date, no robust evidence exists to support the utility of spaced education in the Family Medicine residency. We aimed to test whether alerts to encourage spaced education can improve clinical knowledge as measured by scores on the Canadian Family Medicine certification examination. METHOD: We conducted a cluster randomized controlled trial to empirically and pragmatically test spaced education using two versions of the Family Medicine Study Guide mobile app. 12 residency training programs in Canada agreed to participate. At six intervention sites, we consented 335 of the 654 (51%) eligible residents. Residents in the intervention group were sent alerts through the app to encourage the answering of questions linked to clinical cases. At six control sites, 299 of 586 (51%) residents consented. Residents in the control group received the same app but with no alerts. Incidence rates of case completion between trial arms were compared using repeated measures analysis. We linked residents in both trial arms to their knowledge scores on the certification examination of the College of Family Physicians of Canada. RESULTS: Over 67 weeks, there was no statistically significant difference in the completion of clinical cases by participants. The difference in mean exam scores and the associated confidence interval did not exceed the pre-defined limit of 4 percentage points. CONCLUSION: Further research is recommended before deploying spaced educational interventions in the Family Medicine residency to improve knowledge.

Quantitative proteomics to study a small molecule targeting the loss of von Hippel-Lindau in renal cell carcinomas.

Inactivation of the tumor suppressor gene, von Hippel-Lindau (VHL), is known to play an important role in the development of sporadic clear cell renal cell carcinomas (ccRCCs). Even if available targeted therapies for metastatic RCCs (mRCCs) have helped to improve progression-free survival rates, they have no durable clinical response. We have previously shown the feasibility of specifically targeting the loss of VHL with the identification of a small molecule, STF-62247. Understanding its functionality is crucial for developing durable personalized therapeutic agents differing from those available targeting hypoxia inducible factor (HIF-) pathways. By using SILAC proteomics, we identified 755 deregulated proteins in response to STF-62247 that were further analyzed by ingenuity pathway analysis (IPA). Bioinformatics analyses predicted alterations in 37 signaling pathways in VHL-null cells in response to treatment. Validation of some altered pathways shows that STF-62247's selectivity is linked to an important inhibition of mTORC1 activation in VHL-null cells leading to protein synthesis arrest, a mechanism differing from two allosteric inhibitors Rapamycin and Everolimus. Altogether, our study identified signaling cascades driving STF-62247 response and brings further knowledge for this molecule that shows selectivity for the loss of VHL. The use of a global SILAC approach was successful in identifying novel affected signaling pathways that could be exploited for the development of new personalized therapeutic strategies to target VHL-inactivated RCCs.

A transgenic zebrafish model expressing KIT-D816V recapitulates features of aggressive systemic mastocytosis.

Systemic mastocytosis (SM) is a rare myeloproliferative disease without curative therapy. Despite clinical variability, the majority of patients harbour a KIT-D816V mutation, but efforts to inhibit mutant KIT with tyrosine kinase inhibitors have been unsatisfactory, indicating a need for new preclinical approaches to identify alternative targets and novel therapies in this disease. Murine models to date have been limited and do not fully recapitulate the most aggressive forms of SM. We describe the generation of a transgenic zebrafish model expressing the human KIT-D816V mutation. Adult fish demonstrate a myeloproliferative disease phenotype, including features of aggressive SM in haematopoeitic tissues and high expression levels of endopeptidases, consistent with SM patients. Transgenic embryos demonstrate a cell-cycle phenotype with corresponding expression changes in genes associated with DNA maintenance and repair, such as reduced dnmt1. In addition, epcam was consistently downregulated in both transgenic adults and embryos. Decreased embryonic epcam expression was associated with reduced neuromast numbers, providing a robust in vivo phenotypic readout for chemical screening in KIT-D816V-induced disease. This study represents the first zebrafish model of a mast cell disease with an aggressive adult phenotype and embryonic markers that could be exploited to screen for novel agents in SM.

Connecting artificial intelligence and primary care challenges: findings from a multi stakeholder collaborative consultation.

Despite widespread advancements in and envisioned uses for artificial intelligence (AI), few examples of successfully implemented AI innovations exist in primary care (PC) settings. OBJECTIVES: To identify priority areas for AI and PC in Ontario, Canada. METHODS: A collaborative consultation event engaged multiple stakeholders in a nominal group technique process to generate, discuss and rank ideas for how AI can support Ontario PC. RESULTS: The consultation process produced nine ranked priorities: (1) preventative care and risk profiling, (2) patient self-management of condition(s), (3) management and synthesis of information, (4) improved communication between PC and AI stakeholders, (5) data sharing and interoperability, (6-tie) clinical decision support, (6-tie) administrative staff support, (8) practitioner clerical and routine task support and (9) increased mental healthcare capacity and support. Themes emerging from small group discussions about barriers, implementation issues and resources needed to support the priorities included: equity and the digital divide; system capacity and culture; data availability and quality; legal and ethical issues; user-centred design; patient-centredness; and proper evaluation of AI-driven tool implementation. DISCUSSION: Findings provide guidance for future work on AI and PC. There are immediate opportunities to use existing resources to develop and test AI for priority areas at the patient, provider and system level. For larger scale, sustainable innovations, there is a need for longer-term projects that lay foundations around data and interdisciplinary work. CONCLUSION: Study findings can be used to inform future research and development of AI for PC, and to guide resource planning and allocation.