Understanding the roles of three academic communities in a prospective learning health ecosystem for diagnostic excellence

Creating a Learning Health System for Improving Diagnostic Safety: Pragmatic Insights from US Health Care Organizations

OBJECTIVE

To identify challenges and pragmatic strategies for improving diagnostic safety at an organizational level using concepts from learning health systems METHODS: We interviewed 32 safety leaders across the USA on how their organizations approach diagnostic safety. Participants were recruited through email and represented geographically diverse academic and non-academic settings. The interview included questions on culture of reporting and learning from diagnostic errors; data gathering and analysis activities; diagnostic training and educational activities; and engagement of clinical leadership, staff, patients, and families in diagnostic safety activities. We conducted an inductive content analysis of interview transcripts and two reviewers coded all data.

RESULTS

Of 32 participants, 12 reported having a specific program to address diagnostic errors. Multiple barriers to implement diagnostic safety activities emerged: serious concerns about psychological safety associated with diagnostic error; lack of infrastructure for measurement, monitoring, and improvement activities related to diagnosis; lack of leadership investment, which was often diverted to competing priorities related to publicly reported measures or other incentives; and lack of dedicated teams to work on diagnostic safety. Participants provided several strategies to overcome barriers including adapting trigger tools to identify safety events, engaging patients in diagnostic safety, and appointing dedicated diagnostic safety champions.

CONCLUSIONS

Several foundational building blocks related to learning health systems could inform organizational efforts to reduce diagnostic error. Promoting an organizational culture specific to diagnostic safety, using science and informatics to improve measurement and analysis, leadership incentives to build institutional capacity to address diagnostic errors, and patient engagement in diagnostic safety activities can enable progress.

Rethinking autism spectrum disorder assessment for children during COVID-19 and beyond

The COVID-19 pandemic has posed unique challenges for families and caregivers, as well as for autism-focused clinicians, who are faced with providing a thorough and accurate evaluation of children's specific needs and diagnoses in the absence of in-person assessment tools. The shift to telehealth assessments has challenged clinicians to reconsider approaches and assumptions that underlie the diagnostic assessment process, and to adopt new ways of individualizing standard assessments according to family and child needs. Mandates for physical distancing have uncovered deficiencies in diagnostic practices for suspected autism and have illuminated biases that have posed obstacles preventing children and families from receiving the services that they truly need. This Commentary outlines several considerations for improving diagnostic practices as we move forward from the current pandemic and continue to strive to build an adaptable, sustainable, equitable, and family-centered system of care. LAY SUMMARY: Physical distancing and the abrupt end to in-person services for many children on the autism spectrum has forced clinicians to examine the existing challenges with autism spectrum disorder (ASD) diagnostic assessment and consider things they want to keep and things that should be changed in the years ahead. New approaches such as telehealth both alleviated and exacerbated existing disparities, and brought into stark focus the importance of equitable and timely access to family-centered care. This commentary suggests ways of improving clinical practices related to ASD assessment to continue along this path.

The three numbers you need to know about healthcare: the 60-30-10 Challenge

BACKGROUND

Healthcare represents a paradox. While change is everywhere, performance has flatlined: 60% of care on average is in line with evidence- or consensus-based guidelines, 30% is some form of waste or of low value, and 10% is harm. The 60-30-10 Challenge has persisted for three decades.

MAIN BODY

Current top-down or chain-logic strategies to address this problem, based essentially on linear models of change and relying on policies, hierarchies, and standardisation, have proven insufficient. Instead, we need to marry ideas drawn from complexity science and continuous improvement with proposals for creating a deep learning health system. This dynamic learning model has the potential to assemble relevant information including patients' histories, and clinical, patient, laboratory, and cost data for improved decision-making in real time, or close to real time. If we get it right, the learning health system will contribute to care being more evidence-based and less wasteful and harmful. It will need a purpose-designed digital backbone and infrastructure, apply artificial intelligence to support diagnosis and treatment options, harness genomic and other new data types, and create informed discussions of options between patients, families, and clinicians. While there will be many variants of the model, learning health systems will need to spread, and be encouraged to do so, principally through diffusion of innovation models and local adaptations.

CONCLUSION

Deep learning systems can enable us to better exploit expanding health datasets including traditional and newer forms of big and smaller-scale data, e.g. genomics and cost information, and incorporate patient preferences into decision-making. As we envisage it, a deep learning system will support healthcare's desire to continually improve, and make gains on the 60-30-10 dimensions. All modern health systems are awash with data, but it is only recently that we have been able to bring this together, operationalised, and turned into useful information by which to make more intelligent, timely decisions than in the past.