Undergoing Transformation to the Patient Centered Medical Home in Safety Net Health Centers: Perspectives from the Front Lines

Objectives—Safety Net Health Centers (SNHCs), which include Federally Qualified Health Centers (FQHCs) provide primary care for underserved, minority and low income patients. SNHCs across the country are in the process of adopting the Patient Centered Medical Home (PCMH) model, based on promising early implementation data from demonstration projects. However, previous demonstration projects have not focused on the safety net and we know little about PCMH transformation in SNHCs. Design—This qualitative study characterizes early PCMH adoption experiences at SNHCs. Setting and Participants—We interviewed 98 staff,(administrators, providers, and clinical staff) at 20 of 65 SNHCs, from five states, who were participating in the first of a five-year PCMH collaborative, the Safety Net Medical Home Initiative. Main Measures—We conducted 30-45 minute, semi-structured telephone interviews. Interview questions addressed benefits anticipated, obstacles encountered, and lessons learned in transition to PCMH. Results—Anticipated benefits for participating in the PCMH included improved staff satisfaction and patient care and outcomes. Obstacles included staff resistance and lack of financial support for PCMH functions. Lessons learned included involving a range of staff, anticipating resistance, and using data as frequent feedback. Conclusions—SNHCs encounter unique challenges to PCMH implementation, including staff turnover and providing care for patients with complex needs. Staff resistance and turnover may be ameliorated through improved healthcare delivery strategies associated with the PCMH. Creating predictable and continuous funding streams may be more fundamental challenges to PCMH transformation

The human syndrome of dendritic cell, monocyte, B and NK lymphoid deficiency

Human immunodeficiency syndrome with loss of DCs, monocytes, and T reg cells; preservation of Langerhans cells; associated loss of BM multilymphoid progenitors; and overproduction of Flt3 ligand

Inhibition of AMP-activated protein kinase at the allosteric drug-binding site promotes islet insulin release

The AMP-activated protein kinase (AMPK) is a metabolic stress-sensing αβγ heterotrimer responsible for energy homeostasis. Pharmacological inhibition of AMPK is regarded as a therapeutic strategy in some disease settings including obesity and cancer; however, the broadly used direct AMPK inhibitor compound C suffers from poor selectivity. We have discovered a dihydroxyquinoline drug (MT47-100) with novel AMPK regulatory properties, being simultaneously a direct activator and inhibitor of AMPK complexes containing the β1 or β2 isoform, respectively. Allosteric inhibition by MT47-100 was dependent on the β2 carbohydrate-binding module (CBM) and determined by three non-conserved CBM residues (Ile81, Phe91, Ile92), but was independent of β2-Ser108 phosphorylation. Whereas MT47-100 regulation of total cellular AMPK activity was determined by β1/β2 expression ratio, MT47-100 augmented glucose-stimulated insulin secretion from isolated mouse pancreatic islets via a β2-dependent mechanism. Our findings highlight the therapeutic potential of isoform-specific AMPK allosteric inhibitors

Clinical Analytics Prediction Engine (CAPE): Development, electronic health record integration and prospective validation of hospital mortality, 180-day mortality and 30-day readmission risk prediction models

Background: Numerous predictive models in the literature stratify patients by risk of mortality and readmission. Few prediction models have been developed to optimize impact while sustaining sufficient performance. Objective: We aimed to derive models for hospital mortality, 180-day mortality and 30-day readmission, implement these models within our electronic health record and prospectively validate these models for use across an entire health system. Materials & methods: We developed, integrated into our electronic health record and prospectively validated three predictive models using logistic regression from data collected from patients 18 to 99 years old who had an inpatient or observation admission at NorthShore University HealthSystem, a four-hospital integrated system in the United States, from January 2012 to September 2018. We analyzed the area under the receiver operating characteristic curve (AUC) for model performance. Results: Models were derived and validated at three time points: retrospective, prospective at discharge, and prospective at 4 hours after presentation. AUCs of hospital mortality were 0.91, 0.89 and 0.77, respectively. AUCs for 30-day readmission were 0.71, 0.71 and 0.69, respectively. 180-day mortality models were only retrospectively validated with an AUC of 0.85 Discussion: We were able to retain good model performance while optimizing potential model impact by also valuing model derivation efficiency, usability, sensitivity, generalizability and ability to prescribe timely interventions to reduce underlying risk. Measuring model impact by tying prediction models to interventions that are then rapidly tested will establish a path for meaningful clinical improvement and implementation.</p

Clinical Analytics Prediction Engine (CAPE): Development, electronic health record integration and prospective validation of hospital mortality, 180-day mortality and 30-day readmission risk prediction models.

BackgroundNumerous predictive models in the literature stratify patients by risk of mortality and readmission. Few prediction models have been developed to optimize impact while sustaining sufficient performance.ObjectiveWe aimed to derive models for hospital mortality, 180-day mortality and 30-day readmission, implement these models within our electronic health record and prospectively validate these models for use across an entire health system.Materials & methodsWe developed, integrated into our electronic health record and prospectively validated three predictive models using logistic regression from data collected from patients 18 to 99 years old who had an inpatient or observation admission at NorthShore University HealthSystem, a four-hospital integrated system in the United States, from January 2012 to September 2018. We analyzed the area under the receiver operating characteristic curve (AUC) for model performance.ResultsModels were derived and validated at three time points: retrospective, prospective at discharge, and prospective at 4 hours after presentation. AUCs of hospital mortality were 0.91, 0.89 and 0.77, respectively. AUCs for 30-day readmission were 0.71, 0.71 and 0.69, respectively. 180-day mortality models were only retrospectively validated with an AUC of 0.85.DiscussionWe were able to retain good model performance while optimizing potential model impact by also valuing model derivation efficiency, usability, sensitivity, generalizability and ability to prescribe timely interventions to reduce underlying risk. Measuring model impact by tying prediction models to interventions that are then rapidly tested will establish a path for meaningful clinical improvement and implementation