Successful Change Management Strategies for Improving Diabetes Care Delivery Among High-Performing Practices.

PURPOSE: To learn how the highest-performing primary care practices manage change when implementing improvements to diabetes care delivery. METHODS: We ranked a total of 330 primary care practices submitting practice management assessments and diabetes reports to the Understanding Infrastructure Transformation Effects on Diabetes study in 2017 and 2019 by Optimal Diabetes Care performance. We ranked practices from the top quartile by greatest annual improvement to capture dynamic change. Starting with the top performers, we interviewed practice leaders to identify their most effective strategies for managing change. Interview transcripts were qualitatively analyzed to identify change management strategies. Saturation occurred when no new strategies were identified over 2 consecutive interviews. RESULTS: Ten of the top 13 practices agreed to interviews. We identified 199 key comments representing 48 key care management concepts. We also categorized concepts into 6 care management themes and 37 strategic approaches. We categorized strategic approaches into 13 distinct change management strategies. The most common strategies identified were (1) standardizing the care process, (2) performance awareness, (3) enhancing care teams, (4) health care organization participation, (5) improving reporting systems, (6) engaging staff and clinicians, (7) accountability for tasks, (8) engaging leadership, and (9) tracking change. Care management themes identified by most practices included proactive care, improving patient relationships, and previsit planning. CONCLUSIONS: Top-performing primary care practices identify a similar group of strategies as important for managing change during quality improvement activities. Practices involved in diabetes improvement activities, and perhaps other chronic conditions, should consider adopting these change management strategies.

Whole genome analysis for 163 gRNAs in Cas9-edited mice reveals minimal off-target activity.

Genome editing with CRISPR-associated (Cas) proteins holds exceptional promise for "correcting" variants causing genetic disease. To realize this promise, off-target genomic changes cannot occur during the editing process. Here, we use whole genome sequencing to compare the genomes of 50 Cas9-edited founder mice to 28 untreated control mice to assess the occurrence of S. pyogenes Cas9-induced off-target mutagenesis. Computational analysis of whole-genome sequencing data detects 26 unique sequence variants at 23 predicted off-target sites for 18/163 guides used. While computationally detected variants are identified in 30% (15/50) of Cas9 gene-edited founder animals, only 38% (10/26) of the variants in 8/15 founders validate by Sanger sequencing. In vitro assays for Cas9 off-target activity identify only two unpredicted off-target sites present in genome sequencing data. In total, only 4.9% (8/163) of guides tested have detectable off-target activity, a rate of 0.2 Cas9 off-target mutations per founder analyzed. In comparison, we observe ~1,100 unique variants in each mouse regardless of genome exposure to Cas9 indicating off-target variants comprise a small fraction of genetic heterogeneity in Cas9-edited mice. These findings will inform future design and use of Cas9-edited animal models as well as provide context for evaluating off-target potential in genetically diverse patient populations.

Care Management Processes Important for High-Quality Diabetes Care.

OBJECTIVE: Identify the improvement in diabetes performance measures and population-based clinical outcomes resulting from changes in care management processes (CMP) in primary care practices over 3 years. RESEARCH DESIGN AND METHODS: This repeated cross-sectional study tracked clinical performance measures for all diabetes patients seen in a cohort of 330 primary care practices in 2017 and 2019. Unit of analysis was patient-year with practice-level CMP exposures. Causal inference is based on dynamic changes in individual CMPs between years by practice. We used the Bayesian method to simultaneously estimate a five-outcome model: A1c, systolic and diastolic blood pressure, guideline-based statin use, and Optimal Diabetes Care (ODC). We control for unobserved time-invariant practice characteristics and secular change. We modeled correlation of errors across outcomes. Statistical significance was identified using 99% Bayesian credible intervals (analogous to P < 0.01). RESULTS: Implementation of 18 of 62 CMPs was associated with statistically significant improvements in patient outcomes. Together, these resulted in 12.1% more patients meeting ODC performance measures. Different CMPs affected different outcomes. Three CMPs accounted for 47% of the total ODC improvement, 68% of A1c decrease, 21% of SBP reduction, and 55% of statin use increase: 1) systems for identifying and reminding patients due for testing, 2) after-visit follow-up by a nonclinician, and 3) guideline-based clinician reminders for preventive services during a clinic visit. CONCLUSIONS: Effective quality improvement in primary care focuses on practice redesign that clearly improves diabetes outcomes. Tailoring CMP adoption in primary care provides effective improvement in ODC performance through focused changes in diabetes outcomes.

Identification of a heterogeneous and dynamic ciliome during embryonic development and cell differentiation.

Primary cilia are nearly ubiquitous organelles that transduce molecular and mechanical signals. Although the basic structure of the cilium and the cadre of genes that contribute to ciliary formation and function (the ciliome) are believed to be evolutionarily conserved, the presentation of ciliopathies with narrow, tissue-specific phenotypes and distinct molecular readouts suggests that an unappreciated heterogeneity exists within this organelle. Here, we provide a searchable transcriptomic resource for a curated primary ciliome, detailing various subgroups of differentially expressed genes within the ciliome that display tissue and temporal specificity. Genes within the differentially expressed ciliome exhibited a lower level of functional constraint across species, suggesting organism and cell-specific function adaptation. The biological relevance of ciliary heterogeneity was functionally validated by using Cas9 gene-editing to disrupt ciliary genes that displayed dynamic gene expression profiles during osteogenic differentiation of multipotent neural crest cells. Collectively, this novel primary cilia-focused resource will allow researchers to explore longstanding questions related to how tissue and cell-type specific functions and ciliary heterogeneity may contribute to the range of phenotypes associated with ciliopathies.

COVID-19 Impacts on Primary Care Clinic Care Management Processes.

PURPOSE: To learn whether the COVID-19 pandemic's disruptions and associated reduced health outcomes for people with chronic conditions might have been caused by a decrease in care management processes (CMPs) in primary care clinics METHODS: Longitudinal cohort design with repeated survey-based measures of CMPs from 2017, 2019, and 2021 in 269 primary care clinics in Minnesota. RESULTS: There were only small differences in organizational characteristics and no differences in overall CMPs between the 269 clinics analyzed and the 287 that only completed surveys in 1 or 2 years. Overall CMP scores rose by similar amounts (1.6% and 2.1%) from 2017 to 2019 and from 2019 to 2021. In 2021, CMP scores were lower in small medical groups than in large medical groups in 2017 (66.1% vs 78.5%, P <.001), a similar difference to that in 2017. Care management process scores were also lower in clinics in urban areas compared with rural areas (73.9% vs 79.0%, P <.001), but overall scores in all subgroups were higher in 2021 than in 2017. This improvement occurred despite reports from 55% of clinic leaders that the pandemic had been very or extremely disruptive. CONCLUSIONS: Although quite disrupted by the pandemic, care management processes for chronic disease care in these resilient primary care clinics actually increased from 2019 to 2021, at least in clinics that were part of large organizations. However, that was not true for clinics from smaller groups and perhaps for other areas of care.

Quality and team care response to the pandemic stresses in high performing primary care practices: A qualitative study.

OBJECTIVE: To learn how high performing primary care practices organized care for patients with diabetes during the initial months of the COVID-19 pandemic. PARTICIPANTS AND METHODS: Semi-structured interviews were conducted between August 10 and December 10, 2020 with 16 leaders from 11 practices that had top quartile performance measures for diabetes outcomes pre-COVID. Each clinic had completed a similar interview and a survey about the existence of care management systems associated with quality outcomes before the pandemic. Transcript analysis utilized a theoretical thematic analysis at the semantic level. RESULTS: The pandemic disrupted the primary care practices' operations and processes considered important for quality prior to the pandemic, particularly clinic reliance on proactive patient care. Safety concerns resulted from the shift to virtual visits, which produced documentation gaps and led practices to reorder their use of proactive patient care processes. Informal interactions with patients also declined. These practices' challenges were mitigated by technical, informational and operational help from the larger organizations of which they were a part. Care management processes had to accommodate both in-person and virtual visits. CONCLUSION: These high performing practices demonstrated an ability to adapt their use of proactive patient care processes in pursuing quality outcomes for patients with diabetes during the pandemic. Continued clinic transformation and improvements in quality within primary care depend on the ability to restructure the responsibilities of care team members and their interactions with patients.

Diabetes care quality: do large medical groups perform better?

OBJECTIVES: To compare primary care management processes (CMPs) and outcome measures for diabetes quality among large, medium, and small medical groups. STUDY DESIGN: Observational comparison of differences in processes and outcomes over time among 329 primary care practices that agreed to participate and returned completed surveys in both 2017 and 2019. METHODS: We used a standardized composite measure of diabetes quality along with its 5 components and a survey measure of the presence of systematic CMPs to compare the outcomes and processes of care among clinics that were in large (≥ 12 sites), medium (4-11 sites), and small (1-3 sites) medical groups. RESULTS: Practices from large groups had better performance than those in medium and small groups on the composite measure of diabetes outcomes in 2017 (46.5 vs 40.6 and 34.4, respectively; P < .001), as well as on each of the 5 component measures. They also had more CMPs in place (74.2% vs 66.9% and 61.4%; P < .001), including the 10 CMPs that are associated with the highest level of performance (84.2% vs 77.9% and 72.2%; P < .001). However, repeated measures in 2019 showed that the smaller groups had gained on both quality and CMP measures. There was also substantial overlap on both CMPs and performance among practices in groups of different sizes. CONCLUSIONS: On average, primary care practices that are part of large well-established medical groups outperformed smaller-sized groups in diabetes care quality, probably because they have the resources, leadership, and infrastructure to provide more consistent care through more organized CMPs.

Genetic resiliency associated with dominant lethal TPM1 mutation causing atrial septal defect with high heritability.

Analysis of large-scale human genomic data has yielded unexplained mutations known to cause severe disease in healthy individuals. Here, we report the unexpected recovery of a rare dominant lethal mutation in TPM1, a sarcomeric actin-binding protein, in eight individuals with large atrial septal defect (ASD) in a five-generation pedigree. Mice with Tpm1 mutation exhibit early embryonic lethality with disrupted myofibril assembly and no heartbeat. However, patient-induced pluripotent-stem-cell-derived cardiomyocytes show normal beating with mild myofilament defect, indicating disease suppression. A variant in TLN2, another myofilament actin-binding protein, is identified as a candidate suppressor. Mouse CRISPR knock-in (KI) of both the TLN2 and TPM1 variants rescues heart beating, with near-term fetuses exhibiting large ASD. Thus, the role of TPM1 in ASD pathogenesis unfolds with suppression of its embryonic lethality by protective TLN2 variant. These findings provide evidence that genetic resiliency can arise with genetic suppression of a deleterious mutation.

Progress towards completing the mutant mouse null resource.

The generation of a comprehensive catalog of null alleles covering all protein-coding genes is the goal of the International Mouse Phenotyping Consortium. Over the past 20 years, significant progress has been made towards achieving this goal through the combined efforts of many large-scale programs that built an embryonic stem cell resource to generate knockout mice and more recently employed CRISPR/Cas9-based mutagenesis to delete critical regions predicted to result in frameshift mutations, thus, ablating gene function. The IMPC initiative builds on prior and ongoing work by individual research groups creating gene knockouts in the mouse. Here, we analyze the collective efforts focusing on the combined null allele resource resulting from strains developed by the research community and large-scale production programs. Based upon this pooled analysis, we examine the remaining fraction of protein-coding genes focusing on clearly defined mouse-human orthologs as the highest priority for completing the mutant mouse null resource. In summary, we find that there are less than 3400 mouse-human orthologs remaining in the genome without a targeted null allele that can be further prioritized to achieve our overall goal of the complete functional annotation of the protein-coding portion of a mammalian genome.

Spatial regulation by multiple Gremlin1 enhancers provides digit development with cis-regulatory robustness and evolutionary plasticity.

Precise cis-regulatory control of gene expression is essential for normal embryogenesis and tissue development. The BMP antagonist Gremlin1 (Grem1) is a key node in the signalling system that coordinately controls limb bud development. Here, we use mouse reverse genetics to identify the enhancers in the Grem1 genomic landscape and the underlying cis-regulatory logics that orchestrate the spatio-temporal Grem1 expression dynamics during limb bud development. We establish that transcript levels are controlled in an additive manner while spatial regulation requires synergistic interactions among multiple enhancers. Disrupting these interactions shows that altered spatial regulation rather than reduced Grem1 transcript levels prefigures digit fusions and loss. Two of the enhancers are evolutionary ancient and highly conserved from basal fishes to mammals. Analysing these enhancers from different species reveal the substantial spatial plasticity in Grem1 regulation in tetrapods and basal fishes, which provides insights into the fin-to-limb transition and evolutionary diversification of pentadactyl limbs.

A Computational Method for Learning Disease Trajectories From Partially Observable EHR Data.

Diseases can show different courses of progression even when patients share the same risk factors. Recent studies have revealed that the use of trajectories, the order in which diseases manifest throughout life, can be predictive of the course of progression. In this study, we propose a novel computational method for learning disease trajectories from EHR data. The proposed method consists of three parts: first, we propose an algorithm for extracting trajectories from EHR data; second, three criteria for filtering trajectories; and third, a likelihood function for assessing the risk of developing a set of outcomes given a trajectory set. We applied our methods to extract a set of disease trajectories from Mayo Clinic EHR data and evaluated it internally based on log-likelihood, which can be interpreted as the trajectories' ability to explain the observed (partial) disease progressions. We then externally evaluated the trajectories on EHR data from an independent health system, M Health Fairview. The proposed algorithm extracted a comprehensive set of disease trajectories that can explain the observed outcomes substantially better than competing methods and the proposed filtering criteria selected a small subset of disease trajectories that are highly interpretable and suffered only a minimal (relative 5%) loss of the ability to explain disease progression in both the internal and external validation.

Strategies and Factors Associated With Top Performance in Primary Care for Diabetes: Insights From a Mixed Methods Study.

PURPOSE: The aim of this study was to determine what strategies and factors are most important for high performance in the primary care of patients with diabetes. METHODS: We performed a mixed-methods, cross-sectional, observational analysis of interviews and characteristics of primary care clinics in Minnesota and bordering areas. We compared strategies, facilitators, and barriers identified by 31 leaders of 17 clinics in high-, middle-, and low-performance quartiles on a standardized composite measure of diabetes outcomes for 416 of 586 primary care clinics. Semistructured interview data were combined with quantitative data regarding clinic performance and a survey of the presence of care management processes. RESULTS: The interview analysis identified 10 themes providing unique insights into the factors and strategies characterizing the 3 performance groups. The main difference was the degree to which top-performing clinics used patient data to guide proactive and outreach methods to intensify treatment and monitor effect. Top clinics also appeared to view visit-based care management processes as necessary but insufficient, whereas all respondents regarded being part of a large system as mostly helpful. CONCLUSIONS: Top-performing clinic approaches to diabetes care differ from lower-performing clinics primarily by emphasizing data-driven proactive outreach to patients to intensify treatment. Although confirmatory studies are needed, clinical leaders should consider the value of this paradigm shift in approach to care.

The Effect of Depression and Rurality on Diabetes Control.

BACKGROUND: Having depression and living in a rural environment have separately been associated with poor diabetes outcomes, but there little is known about the interaction between the 2 risk factors. This study investigates the association of depression and rurality with glycemic control in adults, as well as their interaction. METHODS: This is a repeated cross-sectional study with data collected from 2010 to 2017 (n = 1,697,173 patient-year observations), comprising a near-complete census of patients with diabetes in Minnesota. The outcome of interest was glycemic control defined as hemoglobin A1c under 8%. We used a logit model with clinic-level random effects to predict glycemic control as a function of depression, patient rurality, and their interaction, adjusted for differences in observed characteristics of the patient, clinic, and patient's neighborhood. RESULTS: Having depression was associated with lower probability of achieving glycemic control (P < .001). Although rurality alone had no association with glycemic control, significant interactions existed between depression and rurality. Living in a small rural town mitigated the negative association between depression and glycemic control (P < .001). CONCLUSION: Although patients with depression had poorer glycemic control, living in a small rural town reduced the negative association between depression and glycemic control.

Gli3 utilizes Hand2 to synergistically regulate tissue-specific transcriptional networks.

Despite a common understanding that Gli TFs are utilized to convey a Hh morphogen gradient, genetic analyses suggest craniofacial development does not completely fit this paradigm. Using the mouse model (Mus musculus), we demonstrated that rather than being driven by a Hh threshold, robust Gli3 transcriptional activity during skeletal and glossal development required interaction with the basic helix-loop-helix TF Hand2. Not only did genetic and expression data support a co-factorial relationship, but genomic analysis revealed that Gli3 and Hand2 were enriched at regulatory elements for genes essential for mandibular patterning and development. Interestingly, motif analysis at sites co-occupied by Gli3 and Hand2 uncovered mandibular-specific, low-affinity, 'divergent' Gli-binding motifs (dGBMs). Functional validation revealed these dGBMs conveyed synergistic activation of Gli targets essential for mandibular patterning and development. In summary, this work elucidates a novel, sequence-dependent mechanism for Gli transcriptional activity within the craniofacial complex that is independent of a graded Hh signal.

Human and mouse essentiality screens as a resource for disease gene discovery.

The identification of causal variants in sequencing studies remains a considerable challenge that can be partially addressed by new gene-specific knowledge. Here, we integrate measures of how essential a gene is to supporting life, as inferred from viability and phenotyping screens performed on knockout mice by the International Mouse Phenotyping Consortium and essentiality screens carried out on human cell lines. We propose a cross-species gene classification across the Full Spectrum of Intolerance to Loss-of-function (FUSIL) and demonstrate that genes in five mutually exclusive FUSIL categories have differing biological properties. Most notably, Mendelian disease genes, particularly those associated with developmental disorders, are highly overrepresented among genes non-essential for cell survival but required for organism development. After screening developmental disorder cases from three independent disease sequencing consortia, we identify potentially pathogenic variants in genes not previously associated with rare diseases. We therefore propose FUSIL as an efficient approach for disease gene discovery.

Differences in Diabetes Care With and Without Certification as a Medical Home.

PURPOSE: The purpose of this study was to assess whether primary care practices certified as medical homes differ in having the practice systems required for that designation and in attaining favorable outcomes for their patients with diabetes, and whether those systems are associated with better diabetes outcomes. METHODS: We undertook a cross-sectional observational study, Understanding Infrastructure Transformation Effects on Diabetes (UNITED), of 586 Minnesota adult primary care practices, comparing those that were certified vs uncertified as medical homes in 2017, with analyses supplemented by previously published studies of these practices. We collected survey information about the presence of medical home practice systems for diabetes care and obtained 6 standardized measures of diabetes care collected yearly from all Minnesota practices. RESULTS: Of 416 practices completing questionnaires (71% of all practices, 92% of participating practices), 394 had data on diabetes care measures. Uncertified practices (39%) were more likely than certified practices to be rural, but their patient populations were similar. Certified practices had more medical home practice systems (79.2% vs 74.9%, P =.01) and were more likely to meet a composite measure of optimal diabetes care (46.8% vs 43.2%, P <.001). A 1-SD increase in presence of practice systems was associated with a 1.4% higher probability of meeting that measure (P <.001). CONCLUSIONS: Practices certified as medical homes have more practice systems and higher performance on diabetes care than uncertified practices, but there is extensive overlap, and any differences may reflect self-selection for certification.

Predicting diabetes clinical outcomes using longitudinal risk factor trajectories.

BACKGROUND: The ubiquity of electronic health records (EHR) offers an opportunity to observe trajectories of laboratory results and vital signs over long periods of time. This study assessed the value of risk factor trajectories available in the electronic health record to predict incident type 2 diabetes. STUDY DESIGN AND METHODS: Analysis was based on a large 13-year retrospective cohort of 71,545 adult, non-diabetic patients with baseline in 2005 and median follow-up time of 8 years. The trajectories of fasting plasma glucose, lipids, BMI and blood pressure were computed over three time frames (2000-2001, 2002-2003, 2004) before baseline. A novel method, Cumulative Exposure (CE), was developed and evaluated using Cox proportional hazards regression to assess risk of incident type 2 diabetes. We used the Framingham Diabetes Risk Scoring (FDRS) Model as control. RESULTS: The new model outperformed the FDRS Model (.802 vs .660; p-values <2e-16). Cumulative exposure measured over different periods showed that even short episodes of hyperglycemia increase the risk of developing diabetes. Returning to normoglycemia moderates the risk, but does not fully eliminate it. The longer an individual maintains glycemic control after a hyperglycemic episode, the lower the subsequent risk of diabetes. CONCLUSION: Incorporating risk factor trajectories substantially increases the ability of clinical decision support risk models to predict onset of type 2 diabetes and provides information about how risk changes over time.

Redesigning Primary Care to Improve Diabetes Outcomes (the UNITED Study).

OBJECTIVE: The effective redesign of primary care delivery systems to improve diabetes care requires an understanding of which particular components of delivery consistently lead to better clinical outcomes. We identified associations between common systems of care management (SysCMs) and the frequency of meeting standardized performance targets for Optimal Diabetes Care (NQF#0729) in primary care practices. RESEARCH DESIGN AND METHODS: A validated survey of 585 eligible family or general internal medicine practices seeing ≥30 adult patients with diabetes in or near Minnesota during 2017 evaluated the presence of 62 SysCMs. From 419 (72%) practices completing the survey, NQF#0729 was determined in 396 (95%) from electronic health records, including 215,842 patients with type 1 or type 2 diabetes. RESULTS: Three SysCMs were associated with higher rates of meeting performance targets across all practices: 1) a systematic process for shared decision making with patients (P = 0.001), 2) checklists of tests or interventions needed for prevention or monitoring of diabetes (P = 0.002), and 3) physician reminders of guideline-based age-appropriate risk assessments due at the patient visit (P = 0.002). When all three were in place, an additional 10.8% of the population achieved recommended performance measures. In subgroup analysis, 15 additional SysCMs were associated with better care in particular types of practices. CONCLUSIONS: Diabetes care outcomes are better in primary care settings that use a patient-centered approach to systematically engage patients in decision making, remind physicians of age-appropriate risk assessments, and provide checklists for recommended diabetes interventions. Practice size and location are important considerations when redesigning delivery systems to improve performance.

Genomic architecture of Shh-dependent cochlear morphogenesis.

The mammalian cochlea develops from a ventral outgrowth of the otic vesicle in response to Shh signaling. Mouse embryos lacking Shh or its essential signal transduction components display cochlear agenesis; however, a detailed understanding of the transcriptional network mediating this process is unclear. Here, we describe an integrated genomic approach to identify Shh-dependent genes and associated regulatory sequences that promote cochlear duct morphogenesis. A comparative transcriptome analysis of otic vesicles from mouse mutants exhibiting loss (Smoecko ) and gain (Shh-P1) of Shh signaling reveal a set of Shh-responsive genes partitioned into four expression categories in the ventral half of the otic vesicle. This target gene classification scheme provides novel insight into several unanticipated roles for Shh, including priming the cochlear epithelium for subsequent sensory development. We also mapped regions of open chromatin in the inner ear by ATAC-seq that, in combination with Gli2 ChIP-seq, identified inner ear enhancers in the vicinity of Shh-responsive genes. These datasets are useful entry points for deciphering Shh-dependent regulatory mechanisms involved in cochlear duct morphogenesis and establishment of its constituent cell types.

Evaluating the Impact of Data Representation on EHR-Based Analytic Tasks.

Different analytic techniques operate optimally with different types of data. As the use of EHR-based analytics expands to newer tasks, data will have to be transformed into different representations, so the tasks can be optimally solved. We classified representations into broad categories based on their characteristics, and proposed a new knowledge-driven representation for clinical data mining as well as trajectory mining, called Severity Encoding Variables (SEVs). Additionally, we studied which characteristics make representations most suitable for particular clinical analytics tasks including trajectory mining. Our evaluation shows that, for regression, most data representations performed similarly, with SEV achieving a slight (albeit statistically significant) advantage. For patients at high risk of diabetes, it outperformed the competing representation by (relative) 20%. For association mining, SEV achieved the highest performance. Its ability to constrain the search space of patterns through clinical knowledge was key to its success.