The Severe Illness Management System ( SIMS ) Platform: Initial Implementation in Western Uganda J. Lucian Davis, MD, MAS Epidemiology of Microbial Diseases Pulmonary, Critical Care, & Sleep Medicine Lucian.Davis@yale.edu 15 December 2016
Disclosures • I have no conflicts of interest to disclose. 2
Roadmap • Global implementation gap in severe illness care • Development of the SIMS platform in Uganda • SIMS 1.0 study findings 3
What is severe illness? • Life-threatening syndromes encountered in hospitals – In- hospital mortality ≥ 10% – Common physiologic responses to diverse diseases • Maternal & child illnesses • Injuries from man-made or natural disasters • Infectious diseases - HIV/AIDS & other emerging pandemics • Non-communicable diseases – A leading cause of death & disability in young adults 4
What are severe illness conditions? • Coma – Failure of central nervous system • Severe respiratory distress – Failure of oxygenation or ventilation • Shock – Failure of circulatory system • Sepsis – Dysregulated host response to infection 5
Why focus on severe illness care? • Preventable deaths can be avoided if clinicians have the tools to provide high-quality severe illness care • Front line clinicians are uniquely positioned to detect, report, and contain emerging public health threats presenting as undifferentiated severe illness. 6
What is the scope of the problem? Africa High-income countries (n=263) (n=44) Capable of implementing all Grade 1 5.7% 91% recommendations for sepsis? 7
“ Walimu aims to save the lives of severely ill patients in low-income countries by enhancing the quality of hospital care.” www.walimu.org
What can be done for severe illness? IMAI : Integrated Management of Adult & Adolescent Illness 9 www.walimu.org/imai
Quick Check Severe Illness Algorithm 10 www.walimu.org/imai
WHO IMAI Quick Check Training 11 13 of 26
Defining Target Behaviors: Process Map Measure Vital Signs & Diagnose Severe Illness Treat Severe Illness Perform Physical Exam 12
Link to a Theory of Behavior Change Measure Vital Signs & Diagnose Severe Illness Treat Severe Illness Perform Physical Exam Michie et al Implement Sci 2011 Michie et al Implement Sci 2011 13
Formative assessment 14
COM-B: Barriers to severe illness care Capability Knowledge of severe illness care Skills to resuscitate severely ill Deliver high-quality care Motivation for severe illness Professional identity to deliver quality Belief in capability to change practice Belief in capability to change outcomes Intention to bring about change Opportunity Time & staff to provide severe illness care Supplies & equipment to deliver severe illness care Social influences allowing change 15 Michie et al Implement Sci 2011
SR: Health worker performance Rowe AK et al. USAID Seminar. 31 March 2015 16
BCW: Tailoring interventions to barriers Quick Check Severe Illness Management Support plus Training & (SIMS) mGuidelines Platform Capability Clinical Mentoring Audit & Deliver Feedback high-quality care Motivation for severe illness Supportive supervision & Opportunity Collaborative improvement meetings 17 Michie et al Implement Sci 2011
TIDieR: Specifying the SIMS intervention SIMS Clinical Mentoring Supportive Supervision Audit & Components & Collaborative Feedback Improvement Meetings Environmental context & Why Knowledge & skills gap Reinforcement gap resources gap Monitoring & What Teaching rounds Problem solving reinforcement Who provides Visiting expert clinician Local clinician champion Semi-automated How Shadowing at the bedside In person Email & SMS Where At the hospital At the hospital At the hospital When & Bi-monthly (Report) All day every 4 months Bi-monthly for one hour how much Weekly (SMS) Tailoring Education, Training, Modeling Environmental restructuring Enablement, Persuasion Added process Review in collaborative Modifications Add distance mentoring? improvement fund improvement meetings How well Twice About monthly at 3 sites As designed 18 Hoffman T et al BMJ 2014
SIMS 1.o Sites Kasese District Bwera Hospital Kagando Hospital Kilembe Mines Hospital St. Paul’s Health Centre 19
SIMS 1.0 Study Design Quasi-randomized, stepped-wedge design Kagando Kilembe St. Paul Bwera Months Aug Sep Oct Nov Dec Jan Feb Mar Apr May 2014 2015 Baseline Intervention Endline 20
Enrollment 5,759 patients admitted 1,633 4,126 Pre-Intervention Cohort Intervention Cohort Hospital 1 759 (46.5) 1336 (32.4) Hospital 2 117 (7.2) 1018 (24.7) Hospital 3 663 (40.6) 1516 (36.7) Hospital 4 94 (5.8) 256 (6.2) 21
Patient characteristics Characteristic Pre-intervention Intervention n (%) period period n=1633 n=4126 Women 59% 57% Age, median years 38 (24-55) 37 (23-58) HIV-seropositive 20% 14% Admitting diagnosis Malaria 34% 37% Pneumonia 5% 4% Heart failure 4% 3% Urinary tract infection 4% 5% Length of stay, median days (IQR) 3 (2-6) 3 (2-6) 22
Impact of SIMS on Vital Sign Collection Vital sign checked? Pre- Post- Change 95% CI P-value (n=5759) (%) (%) (%) Temperature 27 49 +22 +18 to +26 <0.001 Heart rate 10 32 +22 +11 to +32 <0.001 Pulse oximetry 0 19 +19 +18 to +20 <0.001 Blood pressure 56 69 +13 +9 to +16 <0.001 Respiratory rate 5 10 +5 +1 to +9 0.008 Mental status 11 15 +4 +1 to +7 0.004 HIV status 37 45 +8 -8 to +24 0.33 23
Inter-site variation: Pattern 1 Blood pressure Temperature 24
Inter-site variation: Pattern 2 Mental Status Assessment Respiratory Rate 25
Impact on Severe Illness Diagnosis Severe illness? Pre- Post- Risk Ratio 95% CI P-value (n=5759) (%) (%) Shock 11 17 1.53 0.9 – 2.5 0.090 Sepsis 0.4 4 10.1 2.3 – 31 <0.001 Respiratory distress 1 4 5 2.4 – 3.8 <0.001 Altered mental status 5 4 0.69 0.6 – 0.8 <0.001 26
In-hospital Mortality • Trend towards lower mortality in intervention period? – 4.3% vs 3.7%, -0.6%, 95% CI -2.3 to +1.1, p=0.48 • Presence of severe illness strongly predicted mortality – Risk Ratio 2.6, 95% CI 2.4-2.7, p<0.001 27
Conclusions • SIMS, a theory-informed intervention to improve health worker performance, was feasible & effective in a low-income country: – Improved vital sign collection – Increase in severe illness diagnoses – No definitive effects on treatment quality or mortality • Significant heterogeneity by site/vital sign/condition • Ongoing work on fidelity and adaptation in order to refine interventions / implementation strategy for future replication and scale-up 28
Acknowledgements Achilles Katamba, Matt Cummings, MD Shevin Jacob, MD, MPH Olive Kabajaasi, B.A. MBChB, PhD Savio Mwaka, B.A. Nathan Kenya-Mugisha, Adithya Cattamanchi, Elijah Goldberg, B.A. MBChB MD, MAS Funders World Health Organization US Defense Threat Reduction Agency Anonymous European Family Foundation 29 D43TW009607 (JLD)
And especially our patients and clinicians 30
Extra Slides 31
Inter-site variation: Pattern 3 Oxygen Saturation Heart Rate 32
Inter-site variation: Pattern 4 HIV Status Assessment 33
Mobile guidelines 34
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