Achieving the "triple aim" for inborn errors of metabolism: a review of challenges to outcomes research and presentation of a new practice-based evidence framework

Across all areas of health care, decision makers are in pursuit of what Berwick and colleagues have called the “triple aim”: improving patient experiences with care, improving health outcomes, and managing health system impacts. This is challenging in a rare disease context, as exemplified by inborn errors of metabolism. There is a need for evaluative outcomes research to support effective and appropriate care for inborn errors of metabolism. We suggest that such research should consider interventions at both the level of the health system (e.g., early detection through newborn screening, programs to provide access to treatments) and the level of individual patient care (e.g., orphan drugs, medical foods). We have developed a practice- based evidence framework to guide outcomes research for inborn errors of metabolism. Focusing on outcomes across the triple aim, this framework integrates three priority themes: tailoring care in the context of clinical heterogeneity; a shift from “urgent care” to “opportunity for improvement”; and the need to evaluate the comparative effectiveness of emerging and established therapies. Guided by the framework, a new Canadian research network has been established to generate knowledge that will inform the design and delivery of health services for patients with inborn errors of metabolism and other rare diseases.This work was supported by a CIHR Emerging Team Grant (“Emerging team in rare diseases: acheiving the ‘triple aim’ for inborn errors of metabolism,” B.K. Potter, P. Chakraborty, and colleagues, 2012– 2017, grant no. TR3–119195). Current investigators and collaborators in the Canadian Inherited Metabolic Diseases Research Network are: B.K. Potter, P. Chakraborty, J. Kronick, D. Coyle, K. Wilson, M. Brownell, R. Casey, A. Chan, S. Dyack, L. Dodds, A. Feigenbaum, D. Fell, M. Geraghty, C. Greenberg, S. Grosse, A. Guttmann, A. Khan, J. Little, B. Maranda, J. MacKenzie, A. Mhanni, F. Miller, G. Mitchell, J. Mitchell, M. Nakhla, M. Potter, C. Prasad, K. Siriwardena, K.N. Speechley, S. Stocker, L. Turner, H. Vallance, and B.J. Wilson. Members of our external advisory board are D. Bidulka, T. Caulfield, J.T.R. Clarke, C. Doiron, K. El Emam, J. Evans, A. Kemper, W. McCormack, and A. Stephenson Julian. J. Little is supported by a Canada Research Chair in Human Genome Epidemiology. K. Wilson is supported by a Canada Research Chair in Public Health Policy

Intelligent Telehealth in Pharmacovigilance: A Future Perspective

Pharmacovigilance improves patient safety by detecting and preventing adverse drug events. However, challenges exist that limit adverse drug event detection, resulting in many adverse drug events being underreported or inaccurately reported. One challenge includes having access to large data sets from various sources including electronic health records and wearable medical devices. Artificial intelligence, including machine learning methods, such as natural language processing and deep learning, can detect and extract information about adverse drug events, thus automating the pharmacovigilance process and improving the surveillance of known and documented adverse drug events. In addition, with the increased demand for telehealth services, for managing both acute and chronic diseases, artificial intelligence methods can play a role in detecting and preventing adverse drug events. In this review, we discuss two use cases of how artificial intelligence methods may be useful to improve the quality of pharmacovigilance and the role of artificial intelligence in telehealth practices

The Safety of Inpatient Health Care

BACKGROUND: Adverse events during hospitalization are a major cause of patient harm, as documented in the 1991 Harvard Medical Practice Study. Patient safety has changed substantially in the decades since that study was conducted, and a more current assessment of harm during hospitalization is warranted. METHODS: We conducted a retrospective cohort study to assess the frequency, preventability, and severity of patient harm in a random sample of admissions from 11 Massachusetts hospitals during the 2018 calendar year. The occurrence of adverse events was assessed with the use of a trigger method (identification of information in a medical record that was previously shown to be associated with adverse events) and from review of medical records. Trained nurses reviewed records and identified admissions with possible adverse events that were then adjudicated by physicians, who confirmed the presence and characteristics of the adverse events. RESULTS: In a random sample of 2809 admissions, we identified at least one adverse event in 23.6%. Among 978 adverse events, 222 (22.7%) were judged to be preventable and 316 (32.3%) had a severity level of serious (i.e., caused harm that resulted in substantial intervention or prolonged recovery) or higher. A preventable adverse event occurred in 191 (6.8%) of all admissions, and a preventable adverse event with a severity level of serious or higher occurred in 29 (1.0%). There were seven deaths, one of which was deemed to be preventable. Adverse drug events were the most common adverse events (accounting for 39.0% of all events), followed by surgical or other procedural events (30.4%), patient-care events (which were defined as events associated with nursing care, including falls and pressure ulcers) (15.0%), and health care-associated infections (11.9%). CONCLUSIONS: Adverse events were identified in nearly one in four admissions, and approximately one fourth of the events were preventable. These findings underscore the importance of patient safety and the need for continuing improvement. (Funded by the Controlled Risk Insurance Company and the Risk Management Foundation of the Harvard Medical Institutions.)

Presenting Evidence to Patients Online: What Do Web Users Think of Consumer Summaries of Cochrane Musculoskeletal Reviews?

The Internet has the potential to be an effective medium for delivering health care knowledge to consumers. While computer usability research makes recommendations about how to present Web-based information generally, there remains no clear guidance on how to present specific forms of health care research evidence online in a way that facilitates understanding and good health care decision making. The two goals of this study were to describe the Cochrane Musculoskeletal Group's (CMSG's) process for developing online patient-focused summaries of systematic reviews and to evaluate the impressions of these summaries formed by users. A process for summarizing the results of systematic reviews via consumer summaries has evolved over 15 years. An evaluation of this approach took the form of Internet surveys on the Arthritis Society of Canada website and surveys of members of the Canadian Arthritis Patient Alliance (CAPA). Respondents provided information on background, relationship to the decision, their satisfaction with and preparation for decision making, and suggestions for improvements to the summaries. Survey data were collected between August 1, 2005, and February 28, 2006. A total of 261 respondents completed the survey. The majority (226/261 or 87%) of respondents reported having an arthritis-related condition. The consumer summary approach was generally reviewed favorably by respondents, with most agreeing that the summary provided appropriate information (177/261 or 68%), would be useful to others (160/261 or 61%), was well laid out (159/261 or 61%), was easy to learn from (157/261 or 60%), and was useful to the reader (153/261 or 59%). Areas of potential improvement were indicated by relatively fewer respondents agreeing that they could easily find all the information they wanted (118/261 or 45%), by a substantial proportion being unable to judge whether the providers of the information are reliable (80/261 or 31%), and by a similar proportion being unable to determine whether the information presented was the best available (68/261 or 26%). The CMSG has developed an approach to summarizing the results of often-technical systematic reviews into public-friendly consumer summaries. Our online survey showed that this approach was generally well liked but identified specific areas for improvement. Feedback from this survey will help to reshape and improve the current template for consumer summaries used by the CMSG

Factors determining safety culture in hospitals: a scoping review

Objective To evaluate and synthesise the factors determining patient safety culture in hospitals.Methods The scoping review protocol was based on the criteria of the Joanna Briggs Institute. Eligibility criteria were as follows: (1) empirical study published in a peer-reviewed journal; (2) used methods or tools to assess, study or measure safety culture or climate; (3) data collected in the hospital setting and (4) studies published in English. Relevant literature was located using PubMed, CINAHL, Web of Science and PsycINFO databases. Quantitative and qualitative analyses were performed using RStudio and the R interface for multidimensional analysis of texts and questionnaires (IRaMuTeQ).Results A total of 248 primary studies were included. The most used instruments for assessing safety culture were the Hospital Survey on Patient Safety Culture (n=104) and the Safety Attitudes Questionnaire (n=63). The Maslach Burnout Inventory (n=13) and Culture Assessment Scales based on patient perception (n=9) were used in association with cultural instruments. Sixty-six articles were included in the qualitative analysis. In word cloud and similarity analyses, the words ‘communication’ and ‘leadership’ were most prominent. Regarding the descending hierarchical classification analysis, the content was categorised into two main classes, one of which was subdivided into five subclasses: class 1a: job satisfaction and leadership (15.56%), class 1b: error response (22.22%), class 1c: psychological and empowerment nurses (20.00%), class 1d: trust culture (22.22%) and class 2: innovation worker (20.00%).Conclusion The instruments presented elements that remained indispensable for assessing the safety culture, such as leadership commitment, open communication and learning from mistakes. There was also a tendency for research to assess patient and family engagement, psychological safety, nurses’ engagement in decision-making and innovation

Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases

Abstract Artificial intelligence (AI) represents a valuable tool that could be widely used to inform clinical and public health decision-making to effectively manage the impacts of a pandemic. The objective of this scoping review was to identify the key use cases for involving AI for pandemic preparedness and response from the peer-reviewed, preprint, and grey literature. The data synthesis had two parts: an in-depth review of studies that leveraged machine learning (ML) techniques and a limited review of studies that applied traditional modeling approaches. ML applications from the in-depth review were categorized into use cases related to public health and clinical practice, and narratively synthesized. One hundred eighty-three articles met the inclusion criteria for the in-depth review. Six key use cases were identified: forecasting infectious disease dynamics and effects of interventions; surveillance and outbreak detection; real-time monitoring of adherence to public health recommendations; real-time detection of influenza-like illness; triage and timely diagnosis of infections; and prognosis of illness and response to treatment. Data sources and types of ML that were useful varied by use case. The search identified 1167 articles that reported on traditional modeling approaches, which highlighted additional areas where ML could be leveraged for improving the accuracy of estimations or projections. Important ML-based solutions have been developed in response to pandemics, and particularly for COVID-19 but few were optimized for practical application early in the pandemic. These findings can support policymakers, clinicians, and other stakeholders in prioritizing research and development to support operationalization of AI for future pandemics