The Severe Illness Management System (SIMS) Platform: Initial Implementation in Western Uganda

Transcription

1 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 15 December 2016

2 Disclosures I have no conflicts of interest to disclose. 2

3 Roadmap Global implementation gap in severe illness care Development of the SIMS platform in Uganda SIMS 1.0 study findings 3

4 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

5 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

6 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

7 What is the scope of the problem? Capable of implementing all Grade 1 recommendations for sepsis? Africa (n=263) High-income countries (n=44) 5.7% 91% 7

8 Walimu aims to save the lives of severely ill patients in low-income countries by enhancing the quality of hospital care.

9 What can be done for severe illness? 9 IMAI: Integrated Management of Adult & Adolescent Illness

10 Quick Check Severe Illness Algorithm 10

11 WHO IMAI Quick Check Training of 26

12 Defining Target Behaviors: Process Map Measure Vital Signs & Perform Physical Exam Diagnose Severe Illness Treat Severe Illness 12

13 Link to a Theory of Behavior Change Measure Vital Signs & Perform Physical Exam Diagnose Severe Illness Treat Severe Illness Michie et al Implement Sci 2011 Michie et al Implement Sci

14 Formative assessment 14

15 COM-B: Barriers to severe illness care Capability Knowledge of severe illness care Skills to resuscitate severely ill Opportunity Motivation Professional identity to deliver quality Belief in capability to change practice Belief in capability to change outcomes Intention to bring about change Deliver high-quality care for severe illness 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

16 SR: Health worker performance 16 Rowe AK et al. USAID Seminar. 31 March 2015

17 BCW: Tailoring interventions to barriers Quick Check Training & mguidelines Capability Opportunity plus Severe Illness Management Support (SIMS) Platform Clinical Mentoring Motivation Supportive supervision & Collaborative improvement meetings Audit & Feedback Deliver high-quality care for severe illness 17 Michie et al Implement Sci 2011

18 TIDieR: Specifying the SIMS intervention SIMS Components Why Clinical Mentoring Knowledge & skills gap Supportive Supervision & Collaborative Improvement Meetings Environmental context & resources gap What Teaching rounds Problem solving Audit & Feedback Reinforcement gap Monitoring & reinforcement Who provides Visiting expert clinician Local clinician champion Semi-automated How Shadowing at the bedside In person & SMS Where At the hospital At the hospital At the hospital When & how much All day every 4 months Bi-monthly for one hour Bi-monthly (Report) Weekly (SMS) Tailoring Education, Training, Modeling Environmental restructuring Enablement, Persuasion Modifications Add distance mentoring? Added process improvement fund Review in collaborative improvement meetings How well Twice About monthly at 3 sites As designed 18 Hoffman T et al BMJ 2014

19 SIMS 1.o Sites Kasese District Bwera Hospital Kagando Hospital Kilembe Mines Hospital St. Paul s Health Centre 19

20 SIMS 1.0 Study Design Quasi-randomized, stepped-wedge design Kagando Kilembe St. Paul Bwera Months Aug 2014 Sep Oct Nov Dec Jan 2015 Feb Mar Apr May Baseline Intervention Endline 20

21 Enrollment 5,759 patients admitted Hospital 1 Hospital 2 Hospital 3 Hospital 4 1,633 Pre-Intervention Cohort 759 (46.5) 117 (7.2) 663 (40.6) 94 (5.8) 4,126 Intervention Cohort 1336 (32.4) 1018 (24.7) 1516 (36.7) 256 (6.2) 21

22 Patient characteristics Characteristic n (%) Pre-intervention period n=1633 Intervention period 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

23 Impact of SIMS on Vital Sign Collection Vital sign checked? (n=5759) Pre- (%) Post- (%) Change (%) 95% CI P-value Temperature to +26 <0.001 Heart rate to +32 <0.001 Pulse oximetry to +20 <0.001 Blood pressure to +16 <0.001 Respiratory rate to Mental status to HIV status to

24 Inter-site variation: Pattern 1 Temperature Blood pressure 24

25 Inter-site variation: Pattern 2 Respiratory Rate Mental Status Assessment 25

26 Impact on Severe Illness Diagnosis Severe illness? (n=5759) Pre- (%) Post- (%) Risk Ratio 95% CI P-value Shock Sepsis <0.001 Respiratory distress <0.001 Altered mental status <

27 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 , p<

28 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

29 Acknowledgements Matt Cummings, MD Shevin Jacob, MD, MPH Achilles Katamba, MBChB, PhD Olive Kabajaasi, B.A. 29 Savio Mwaka, B.A. Nathan Kenya-Mugisha, Elijah Goldberg, B.A. MBChB Funders World Health Organization US Defense Threat Reduction Agency Anonymous European Family Foundation D43TW (JLD) Adithya Cattamanchi, MD, MAS

30 And especially our patients and clinicians 30

31 Extra Slides 31

32 Inter-site variation: Pattern 3 Heart Rate Oxygen Saturation 32

33 Inter-site variation: Pattern 4 HIV Status Assessment 33

34 Mobile guidelines 34