CLINICAL DECISION-SUPPORT SYSTEMS (ALERTS), WHAT ARE THEY AND CAN THEY HELP MY PATIENT?
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1 CLINICAL DECISION-SUPPORT SYSTEMS (ALERTS), WHAT ARE THEY AND CAN THEY HELP MY PATIENT? Robert Sherwin, MD Assistant Professor of Emergency Medicine Wayne State University School of Medicine Sinai Grace Hospital/Detroit Receiving Hospital Detroit, MI USA March 6 th, 2013 Park City, Utah
2 Learning Objectives 1. Understand the background, requirements and structure of clinical decision support systems 2. Understand specific CDSS applications and the evidence supporting their use. 3.Understand specific components that improve the success of CDSS implementations.
3 Disclosures None
4 The concept was first introduced > 60 years ago Ledley & Lusted Proceedings of the IRE, Dec 1959
5 Abbreviations CPR computerizes EMR - electronic medical record EHR - electronic health record CDDS - computer decision-support system CCDS Computerized clinical decision support KMSs knowledge management systems CPOE - computerized provider order entry NSO Nursing safety issues
6 Clinical Decision Support Systems (CDSS) The Health Information Technology & Clinical Health Act (HITECH; a component of ARRA 2009) provides incentive payments (and future penalties) to support EHR adoption. $27 billion in incentives over 10 years Graduated penalties in Medicaid & Medicare payments beginning in 2015 for for those failing to adopt EHR Hospitals & eligible providers must demonstrate meaningful use of their EHR One of the meaningful-use core objectives involves the use of a clinical decision support rule (CDSS) that addresses a hospitaldefined, high-priority condition. CDSS goals: to help physicians make better decisions & avoid preventable errors.
7 Computer Decision Support System Sucher et al, J of Trauma 2008
8 Clinical Decision Support Systems (CDSS) CDSS may be defined as information systems that aid providers in various aspects of clinical decision-making laminated pocket cards that outline CPR checklists more typically are paper/computer-based order-sets & reminder systems. PDA s, ipad, iphones etc Going to focus on EHR-based CDSSs
9 Clinical Decision Support Systems CDSS utilize the patient information in the EHR & existing medical evidence à runs through various algorithms to deliver computer-generated recommendations CDSS can provide a broad variety of support: reminders for overdue treatments alerts for abnormal values assistance with medication prescribing & medication allergies Critical lab values Interpretations of images or waveforms Generation of differential diagnoses Immunization reminders Prompts for missing clinical data or regulatory compliance
10 Clinical Decision Support Systems Active (also called executable ) CDSS are found in computer software that integrates predetermined rules with patient data to automatically deliver information to the clinician. Recommendation for care offered, clinician makes final decision Passive CDSS consists of optional links for the clinician to seek information Reference to links and evidence based content is made available if the clinician chooses to view
11 Types of CDSSs Sucher et al, J of Trauma 2008
12 STRUCTURE & DEVELOPMENT OF CDSS
13 Sucher et al, J of Trauma 2008
14 Garg et al JAMA 2005
15 Were recommended treatments ordered? Were recommended studies ordered? Bright et al Annals of Internal Med 2012
16 Outcome Evidence # Studies Results (95% CI) Length of stay Low 6 RR, 0.96 ( ) Morbidity Moderate 22 RR, 0.88 ( ) Mortality Low 6 OR, 0.79 ( ) Adverse events Low 5 RR, 1.01 ( ) Preventive care High 43 OR, 1.42 ( ) Clin. study ordered Moderate 29 OR, 1.72 ( ) Recommended Rx High 67 OR, 1.57 ( ) Clinical Workload Insufficient NA Efficiency Insufficient NA Cost effectiveness Insufficient NA Bright et al Annals of Internal Med 2012
17 Lobach et al. Evidence Report No AHRQ 2012
18 Features found to be associated with CDSS success: Integration with charting or order entry Promotion of action versus inaction No need for additional clinician entry Justification of decision suppclinort via research evidence Local user involvement Provision of decision support results to patients & providers Automatic decision support as part of clinician workflow Provision at the time and location of decision making Provision of a recommendation, not just an assessment Lobach et al. Evidence Report No AHRQ 2012
19 CDSS Successful Development 75% of CDSS were successful if integrated into the physicians workflow / 0% were if they required an interruption. Kawamoto et al BMJ 2005 Alerts should be sensitive and specific, difficult to ignore, contained within the physician workflow and automatically captured by a reporting system Guilano et al AACN 2009
20 CDSS Successful Development Studies of home grown CDSS reported better performance w/studies w/independent CDSS development process (74% vs 28%; P=0.001) 7 / 100 CDSS trials reported any patient outcome improvement Garg et al JAMA 2005 Physicians are more likely to follow recommendations if they understand the rationale Schiffman et al JAMIA 1999
21 CDSS Successful Development CDSSs with these 9 features favorably impact health care processes No or limited evidence to suggest improvements in: morbidity clinical outcomes economics, workload or work efficiency patient length of stay Locally developed or commercial product Academic or community environment Ambulatory or hospital-setting Single or multiple sets or conditions
22 What types of rules? Dichotomous rule Inexplicit/judgment rule Threshold rule with executable instruction Sucher et al, J of Trauma 2008
23 CRITICAL CARE APPLICATIONS Critical problem is how to consistently and efficiently implement evidence based guidelines for critically ill patients
24 Dr Peter Provonost, Vice President of Patient Safety at Johns Hopkins, and a nationally known safety expert On average patients receive a little more than half of the therapies that they are supposed to. ICUs have so many machines, and doctors and nurses have so much to keep track of, they simply can t keep up. I need a technology that makes it easy and efficient for me to do the right thing in medicine. Not think I m going to remember 200 things,
25 Nelson et al Ann Emerg Med 2011
26
27
28 Sawyer et al Crit Care Med 2011
29 Rana et al Crit Care Med 2006
30 17% à 4.5% (p < 0.001) Rana et al Crit Care Med 2006
31 Sepsis Alert & Diagnostic System (SADS) Johns Hopkins Hospital Research coordinator gets an alpha-numeric page whenever a patient has 3 criteria
32 Sepsis Alert & Diagnostic System (SADS) Johns Hopkins Hospital
33 Sepsis Alert & Diagnostic System (SADS) Johns Hopkins Hospital PPV 87.5% / NPV 93%
34 Following CDSS implementation: - 6 hour bundle compliance increased 29% à 79% - Mortality from intra abdominal sepsis 24% à 12% Moore et al. Am J of Surgery 2010
35 A Nursing CDSS and Potential Predictors of HOB Position for Patients Receiving Mechanical Ventilation Lyerla et at Am J of Critical Care 2010
36
37
38 Potential Pitfalls Overreliance of users on technology Ignoring important alerts due to alert fatigue Incorrect advice due to accidental selection of wrong patient Incorrect entry of (verbal) orders decreased communication between physicians and nurses and other healthcare providers Taking the physician or nurse away from the bedside
39 Do no harm In their PICU, mortality increased 2.80%à 6.57% after CPOE implementation (OR 3.28, 05% CI )
40 DMC Sepsis Alert 2 SIRS and 1 organ dysfunction within the last 48 hours SIRS Triggers Temperature < 35.3 or >38.3 C Respiratory Rate >20 bpm Heart Rate > 90 bpm WBC < 4 or >12 and No order for Neupogen Organ Dysfunction Triggers Lactic Acid >2.2 mmol/l Creatinine >2 gm/dl Total Bilirubin >2 mg/dl Platelet Count < 100,000 INR >1.5 aptt > 60 seconds Systolic Blood Pressure < 90 or MAP < 65 mmhg Pa02/Fi02 Ratio <3 00 mmhg Systolic Blood Pressure drop > 40 mmhg Lethargic, Confused, Agitated or Anxious New Two Tier Alert: 1. Fever plus one other SIRS 2. Original alert (to left) 3. Any Lactate > 4.0 mmol/l PLEASE USE THE SEPSIS POWERPLAN FOR ALL PTS W/ SUSPECTED SEPSIS Clinical Transformation/MIS_
41 DMC Sepsis Alert First Net Sepsis Alert sent to ED Tracking Board as a Bug Icon (DMC s CDSS) Sepsis Alert Bug meansà Go to Orders & open Sepsis Care Powerplan Clinical Transformation/MIS_
42 DMC Sepsis Alert
43 The Performance of an EMR Based Alert to Iden7fy ED Pa7ents with Severe Sepsis & Sep7c Shock Nelson G, Leisy A, Kamash T, Sherwin R ACEP 2012 Alert performance: Positive predictive value % Negative predictive value 99.0% Sensitivity..62.2% Specificity..98.6% No significant difference in time to abx administration b/w patients who did (n = 28) or did not (n = 17) activate the sepsis alert system (87 +/- 49 vs /- 127 minutes; p = 0.417).
44 Factors associated with successful CDSS with respect to preventive care, clinical study adherence, treatment 1. Automatic provision of decision support as part of clinician workflow 2. Provision of decision support at time & location of decision making 3. Provision of a recommendation, not just an assessment 4. Integration w/charting or CPOE to support workflow integration 5. Promotion of action rather than inaction 6. No need for additional clinician data entry 7. Justification of decision support via provision of research evidence 8. Local user involvement in the development process 9. Provision of decision support results to patients as well as providers Lobach et al, ER No AHRQ Publication, April 2012 Also Consider home grown CDSS - may have better adoption CDSS should be automatically versus manually initiated Garg et al JAMA 2005
45 Titrate vasopressors & vasodilators Manage mechanical ventilation Insulin drips Hemodialysis Anesthesia IV fluid titration Oxygen titration Hanson et al Crit Care Med 2001
46 Artificial intelligence Machine intelligence Expert systems Fuzzy logic Bayesian (Belief) networks Case-based reasoning Data visualization Hanson et al Crit Care Med 2001
47 References Ledley, R, and L Lusted. "The Use of Electronic Computers to Aid in Medical Diagnosis." Proceedings of the IRE, Dec McCartney et al, Clinical Decision Support Systems, Then New Networking, Vol. 32 (1), 2007 Lobach et al, Enabling Health Care Decision-making Through Clinical Decision Support & Knowledge Management. Evidence Rpt. No (Duke Evidence Based Practice Center) AHRQ Publication, April Bright et al. Effect of Clinical Decision Support Systems, Annals of Internal Medicine 2012 Garg et al. Effects of CDSS on Physician Practices and Patient Outcomes. JAMA 2005 Hanson et al. Artificial intelligence applications in the intensive care unit. Critical Care Medicine Moore et al. Computerized clinical decision support improves mortality in intraabdominal surgical sepsis. Am J of Surgery 2010
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