SCSHP 2014 Annual Meeting The Stewardship Geek Squad Calling the Stewardship Geek Squad: Integrating Information Technology & Antimicrobial Stewardship Julie Ann Justo, PharmD, MS, BCPS, AAHIVP Clinical Assistant Professor South Carolina College of Pharmacy Antimicrobial Stewardship (AS) Biomedical Informatics Information Technology (IT) 1. http://www.acphospitalist.org/archives/2009/01/cover_sm.jpg 2. http://www.healthcare informatics.com/sites/healthcareinformatics.com/files/imagecache/100x100/doctornurselaptop_13033843_smaller.jpg 3. http://www.webster.edu/images/technology/desktop pc.jpg Disclosure I do have a vested interest in or affiliation with the following companies or organizations Cubist Pharmaceuticals: Grant/Research Support Antimicrobial Stewardship Goal: Optimize clinical outcomes and minimize unintended consequences of antimicrobial use Optimal Antimicrobial Use Prospective Audit Formulary Restriction Other Strategies Interaction and Feedback Prior Authorization Education Local guidelines Dose optimization De escalation 1. Dellit TH, et al. Clin Infect Dis 2007; 44: 159 177. Objectives (Pharmacist Track) Review the importance of clinical informatics in the development of antimicrobial stewardship initiatives in the hospital setting Compare various clinical decision support tools used to interface with electronic medical records Describe how novel rapid diagnostic tests can be integrated into stewardship activities to optimize antimicrobial use Antimicrobial Stewardship Infectious Diseases Society of America and Society of Healthcare Epidemiology of America (IDSA/SHEA) guidelines for developing AS programs specifically comment on technology 1 : Health care information technology in the form of electronic medical records (A III), computer physician order entry (B II), and clinical decision support (B II) can improve antimicrobial decisions through the incorporation of [patient specific] data... 1. Dellit TH, et al. Clin Infect Dis 2007; 44: 159 177. 1
Information Technology (IT) Primarily concerned with application of technology, i.e. computer science 1 Operational IT: Computers, servers, email, electronic medical record Research IT: Clinical data warehouse Importance of Clinical Informatics Improves connectivity between patient data and knowledge in order to aid in clinical decision making Training Vocational training Certifications Degrees, e.g. computer science or management information systems 1. Bernstam EV, et al. Acad Med 2009; 84: 964 970. 1. http://basedonobservations.files.wordpress.com/2011/10/emr1.jpg 2. http://www.nlm.nih.gov/pubs/techbull/ja08/graphics/drug_sensor_fig1.gif Biomedical Informatics Interdisciplinary Effort Primarily concerned with storage, retrieval, and optimum use of data, information, and knowledge for problem solving and decision making in biomedicine 1 Computers are tools for manipulating information Focus on applications in a given domain Clinical Informatics (CI) Public Health Informatics Research Informatics Bioinformatics Determines the destination Antimicrobial Stewardship Clinical Informatics Charts the course Steers the ship Information Technology 1. Bernstam EV, et al. Acad Med 2009; 84: 964 970. Biomedical Informatics Training Interdisciplinary Computer Science Biology Healthcare Most with graduate ±post graduate training Computer savvy, but not = IT support! Or I thought you knew how to navigate! ASP CI I thought you knew how to drive! IT I didn t know we were going off road! Can provide vital expertise for implementing antimicrobial stewardship initiatives (in conjunction with IT) 1. Bernstam EV, et al. Acad Med 2009; 84: 964 970. http://rogerkramercycling.org/blog/wp content/uploads/2012/04/stoogebike.jpg 2
Features of a Successful Clinical Decision Support System (CDSS) 1 Clinical Decision Support Systems (CDSSs) 5. Provides online feedback and documentation within the application 6. Offers evidence based clinical recommendations (emphasis on choice) 7. Fulfills the 6 generic uses: Alerting, interpreting, assisting, critiquing, diagnosing, and managing decision support 8. Adheres to standards for clinical terminology 1. Pestotnik SL, et al. Pharmacotherapy 2005; 25(8): 1116 1125. Clinical Decision Support Systems Example of applied clinical informatics Patient Data Evidence based Practices CDSSs Focusing on Antimicrobial Stewardship TheraDoc Software Vendor Logo Safety Surveillor MedMined Hospira Inc. Premier Inc. CareFusion Clinician at the point of care QC PathFinder Sentri7 Allscripts McKesson Vecna Pharmacy OneSource Inc. Allscripts McKesson 1. Kullar R, et al. Clin Infect Dis 2013; 57: 1005 1013. Features of a Successful Clinical Decision Support System (CDSS) 1 1. Makes clinician s job easier 2. Includes educational component (providing literature and important caveats) to foster user acceptance 3. Delivers patient specific, pertinent data 5 W s: Who to see? What data? What action? Why act? What to document? 4. Operates in real time CDSSs & Stewardship IDSA/SHEA guidelines 1 also comment: Computer based surveillance can facilitate good stewardship by more efficient targeting of antimicrobial interventions, tracking of antimicrobial resistance patterns, and identification of nosocomial infections and adverse drug events (B II). 1. Pestotnik SL, et al. Pharmacotherapy 2005; 25(8): 1116 1125. 1. Dellit TH, et al. Clin Infect Dis 2007; 44: 159 177. 3
Advantages of CDSSs Most are designed to interface with electronic medical records (EMRs), e.g. EPIC and Cerner Generally have greater capacity for AS specific activities than the EMR alone Alerts Reports Targeted Patient Lists Provide combined data that is typically unavailable without significant daily time and effort Cultures and susceptibilities, current antimicrobial regimens, hepatic and renal function, allergies, etc. Barriers to Implementation & Effectiveness of CDSSs 1,2 Cost $100,000 $500,000 per year per institution Time to implementation Committee approval, e.g. Pharmacy & Therapeutics Waiting in the IT queue or task list Time for maintenance Updates to formulary, clinical practice guidelines, etc. Informatics specialists for more advanced algorithms Integration into clinical workflow Alert fatigue 1. Kullar R, et al. Clin Infect Dis 2013; 57: 1005 1013. 2. Njoku JC and Hermsen ED. J Pharm Pract 2010; 23: 50 60. Real Time Alerts and Reporting Sample of targeted patient list by drug usage: CDSS Implementation for AS Pre /postimplementation study evaluating implementation of a CDSS (TheraDoc) at the Nebraska Medical Center 1 8 alert types utilized, including: Polyantibacterials, redundant anaerobic coverage, drug bug mismatch, vancomycin for CONS or MSSA, no positive cultures Of 10,545 alerts, 30% were actionable overall Significant increase in interventions in the postimplementation period ASP made interventions on 75 92% of actionable alerts Decentralized pharmacists made interventions on 12% of actionable alerts Overall acceptance rate: 88% 1. Hermsen ED, et al. Infect Control Hosp Epidemiol 2009; 84: 964 970. CDSS Implementation for AS 70% of alerts non actionable due to: No need for intervention (65%) Previously dismissed alert (16%) Alert based on old data (13%) Duplicate alert (5%) Other (0.2%) Authors encouraged the third party vendor to improve the algorithms to minimize alert fatigue 1. Hermsen ED, et al. Infect Control Hosp Epidemiol 2009; 84: 964 970. 4
CDSSs within the EMR Tools within EPIC 1 : 1. ivents 2. 96 Hour Stop Date 3. IV to PO Interchange 4. Antibiotic Order Forms and Dose Checking Alerts 5. Navigator and Best Practice Alerts 6. Patient Scoring and Monitoring Patient Scoring and Monitoring Custom scoring algorithm to help identify patients with higher priority interventions May significantly improve ASP efficiency Likely requires significantly more time and effort for IT to develop 1. Kullar R, et al. Clin Infect Dis 2013; 57: 1005 1013. 1. Kullar TH, et al. Clin Infect Dis 2007; 44: 159 177. Antibiotic Order Forms Required fields for each order, assists in education and research Stewardship at the point of order verification Rapid Diagnostic Tests (RDTs) 1. Kullar TH, et al. Clin Infect Dis 2007; 44: 159 177. Navigator and Best Practice Alerts ASP recommendations made as a Best Practice Alert Published evidence provided at the point of care Rapid Diagnostic Tests Novel tests that can significantly me to: Organism identification Susceptibility testing data 1. Kullar TH, et al. Clin Infect Dis 2007; 44: 159 177. 1. Goff DA, et al. Pharmacotherapy 2012; 32: 677 687. 5
Rapid Diagnostic Tests Technology Time (h) Trade Name(s) (Manufacturer) Polymerase chain reaction (PCR) 0.75 3, 6* Xpert (Cepheid), FilmArray (Biofire), BD GeneOhm (BD GeneOhm), Progastro Cd (Gen Probe desse), LightCycler SeptiFast Test MGRADE* (Roche Molecular Systems) Peptide nucleic acid fluorescence in situ hybridization (PNA FISH) 1.5 GNR Traffic Light PNA FISH (AdvanDx), Yeast Traffic Light PNA FISH (AdvanDx) PNA FISH Yeast Traffic Light PNA FISH Bacteriophage amplification 5.5 KeyPath MRSA/MSSA Blood Culture (MicroPhage) Nucleic acid 2.5 Verigene (Nanosphere) Loop mediated isothermal amplification (LAMP) Matrix assisted laser desorption ionization time of flight (MALDI TOF) mass spectrometry (MS) 1 illumigene C. difficile (Meridian Bioscience) 0.2 MALDI Biotyper (Bruker Daltonics) VITEK MS (biomérieux) * Direct from blood prior to culture 1. Goff DA, et al. Pharmacotherapy 2012; 32: 677 687. 1. http://www.advandx.com/advandx/media/downloads/assay%20overviews/ytl PNA FISH Overview.png 2. http://www.advandx.com/products/pna fish tests/yeast traffic light Rapid Diagnostic Tests Microorganisms/Resistance Mechanisms Gram Positive Bacteria Staphylococcus spp.: aureus, epidermidis, lugdunensis Streptococcus spp.: anginosus Group, agalactiae, pneumoniae, pyogenes Enterococcus spp.: faecalis, faecium Clostridium difficile Resistance Mechanisms: meca, vana, vanb Gram Negative Bacteria Escherichia coli Klebsiella pneumoniae Klebsiella oxytoca Pseudomonas aeruginosa Serratia marcescens Resistance Mechanisms: KPC, NDM, CTX M, VIM, IMP, OXA Fungi Candida spp.: albicans, glabrata, krusei, parapsilosis, tropicalis Aspergillus fumigatus Viruses Adenovirus Norovirus Rotavirus 1. Goff DA, et al. Pharmacotherapy 2012; 32: 677 687. Acinetobacter spp. Proteus spp. Citrobacter spp. Enterobacter spp. Outcomes with PNA FISH for Candida Implementation significantly reduced time to: Identification of C. albicans Median 9.5 h vs. 44 h, p<0.001 1 Median 0.2 d vs. 4 d, p<0.001 2 Targeted therapy 2 Mean 0.6 vs. 2.3 d, p=0.0016 Culture clearance 2 Median 4 d vs. 5 d, p=0.01 Cost savings estimated at $415 1,837 per patient treated 1 3 Mainly due to decreased drug costs, i.e. switch from echinocandins to fluconazole 1. Forrest GN, et al. J Clin Microbiol 2006; 44: 3381 3383. 2. Heil EL, et al. Am J Health Syst Pharm 2012; 69: 1910 1914. 3. Alexander BD, et al. Diag Microbiol Infect Dis 2006; 54: 277 282. FilmArray Blood Culture Identification Panel FilmArray MALDI TOF MS MALDI TOF System 1. FilmArray Blood Culture Identification Panel. Biofire Diagnostics. http://www.biofiredx.com/media/info Sheet FilmArray BCID Panel 0069.pdf Automated Identification & Susceptibility Testing System http://www.laboratorytalk.com/pictures/633xany/2/7/2/3272_vitek.jpg 6
MALDI TOF MS 1. Deposit bacteria or yeast 2. Add matrix solution(s) 3. Analyze result within minutes 1 isolate per spot 48 spots per slide Outcomes with MALDI TOF & ASP in Bloodstream Infections (BSIs) Interven on showed significant in me to: Organism ID (55.9 vs. 84.0 h, p<0.001) Effective therapy (20.4 vs. 30.1 h, p=0.021) Optimal therapy (47.3 vs. 90.3 h, p< 0.001) 1 cartridge = 4 slides = 192 isolates per run 1. Huang AM, et al. Clin Infect Dis 2013; 57: 1237 1245. MALDI TOF MS Outcomes with MALDI TOF & ASP in Bloodstream Infections (BSIs) Intervention also showed significant in: 30 day all cause mortality (12.7% vs. 20.3%, p=0.021) Length of ICU stay (8.3 vs. 14.9 d, p=0.014) Recurrence of same BSI (2.0% vs. 5.9%, p=0.038) 1. Huang AM, et al. Clin Infect Dis 2013; 57: 1237 1245. Outcomes with MALDI TOF & ASP in Bloodstream Infections (BSIs) Pre post quasi experimental study at University of Michigan Hospitals and Health System Evaluated clinical outcomes in 501 adult patients with BSIs over 3 month periods before and after intervention Intervention: MALDI TOF identification (ID) reported 6:00am 11:30pm Blood subcultured and incubated overnight prior to analysis ASP activities Real time alerts for (+) blood cultures via TheraDoc Recommendations according to local guidelines at time of (1) Gram stain, (2) organism ID, and (3) susceptibility testing results Other Technologies Websites 1 Many ASP resources from leading academic medical center are publicly available Johns Hopkins Health System http://www.hopkinsmedicine.org/amp The Nebraska Medical Center http://www.nebraskamed.com/careers/education programs/asp Valuable local marketing tool Smartphone apps 2 iphone/ipad or Android Potential educational tool for ID topics 1. Huang AM, et al. Clin Infect Dis 2013; 57: 1237 1245. 1. Gauthier TP, et al. Clin Infect Dis 2014; 58: 445 446. 2. Moodley A, et al. Clin Infect Dis 2013; 57: 1145 1154. 7
Summary ASP initiatives should provide clinicians with pertinent patient data and evidence based practices at the point of care Cl and IT specialists are vital members of the stewardship geek squad CDSSs and RDTs offer unique opportunities to optimize antimicrobial therapy and affect patient outcomes ASPs are emerging as ideal models for integrating technology and informatics into healthcare SCSHP 2014 Annual Meeting Calling the Stewardship Geek Squad: Integrating Information Technology & Antimicrobial Stewardship Julie Ann Justo, PharmD, MS, BCPS, AAHIVP Clinical Assistant Professor South Carolina College of Pharmacy 8