Disclosures. Learning Objectives 4/16/2014

Transcription

1 2014 Winter Anesthesia Seminar Optimizing your EMR to Improve Legal, Financial, and Patient Outcome Efforts Getting the Most out of your shiny, brand new Anesthesia Information Management System David Robinowitz, MD MHS MS Associate Professor of Anesthesia and Perioperative Care Medical Director Anesthesia IT Medical Director Perioperative IT UCSF Disclosures No significant financial interests in any commercial products or services mentioned or discussed in this presentation. Our institution uses an Epic-based EHR and AIMS. We used to have a PICIS AIMS. Our PICIS data are archived at Legacy Data Archives. We are working on participation in MPOG and AQI. None of these entities are paying me. Learning Objectives At the end of this presentation you should be able to Describe the history and rationale to deploy an AIMS List some barriers to AIMS implementation Consider potential advantages and disadvantages of AIMS in clinical care Explain the potential benefits of an AIMS for improving patient safety, quality improvement, research, billing, compliance, and medicolegal risk reduction Describe the role that design decisions play in the success or failure of AIMS in the above domains 1

2 The Anesthetic Record What is the purpose of the anesthetic record? The goal of the anesthetic record is to capture a patient s response to anesthesia and surgery by recording the procedures, physiologic changes, key events, and pharmacologic administration that occur throughout the perioperative period Kadry, B., Feaster, W. W., Macario, A. & Ehrenfeld, J. M. Anesthesia information management systems: past, present, and future of anesthesia records. Mt Sinai J Med 79, (2012). History of Anesthesia Record Cushing 1 and Codman: 1895 (as Junior House Pupils ) Developed following a death during ether induction 1. Molnar, C., Nemes, C., Szabo, S. & Fulesdi, B. Harvey Cushing, a pioneer of neuroanesthesia. J Anesth 22, (2008). 2. Kadry, B., Feaster, W. W., Macario, A. & Ehrenfeld, J. M. Anesthesia information management systems: past, present, and future of anesthesia records. Mt Sinai J Med 79, (2012). 3. Beecher, H. K., Codman, E. A. & Cushing, H. The first anesthesia records 1940). History of Anesthesia Record Codman and Cushing challenged each other to improve delivery of anesthesia, 1 in part, through the review of anesthesia records like this one. Cushing s ether chart from 1895, 2 using chloroform instead of ether in effort to reduce surgical bleeding: best case ever had 1. Zeitlin, G. L. History of anesthesia records. ASA Newsletter APSF 25th Anniversary Edition, (2011). 2. Barker, F. G. The Massachusetts General Hospital. Early history and neurosurgery to J Neurosurg 79, (1993). 2

3 Limitations of Paper Records 1 Recall Bias Data analysis requires manual chart review Illegible (or impressionistic) records Lost/missing records Incomplete data issues with documentation of compliance and billing requirements Handwritten records may have less medicolegal heft (no audit trail) 1. Kadry, B., Feaster, W. W., Macario, A. & Ehrenfeld, J. M. Anesthesia information management systems: past, present, and future of anesthesia records. Mt Sinai J Med 79, (2012). History of AIMS First AIMS was mechanical device (E. I. McKesson 1930 s) 1. Featherstone, R. J., Adams, C. N. & Bacon, D. R. Physiological monitoring and record keeping in anaesthesia an unrecorded contribution. American Society of Anesthesiologists (2012). History of AIMS 1950 s 1990 s development of automated anesthesia record keepers Limited by availability of sophisticated, inexpensive, and reliable hardware and software 1 Pioneering Systems 2 DAME (Duke Anesthesia Monitoring Equipment) A few ORs at Duke ARKIVE (Anesthesia Record Keeper Integrating Voice Recognition) Installed at Vero Beach, Duke, and Fitchburg 1990 s slow rollout of commercial products 1. Shah, N. J., Tremper, K. K. & Kheterpal, S. Anatomy of an anesthesia information management system. Anesthesiol Clin 29, (2011). 2. Stonemetz, J. Anesthesia information management systems marketplace and current vendors. Anesthesiol Clin 29, (2011). 3

4 AIMS Adoption Challenges Usability Concerns of decreased productivity Technology Adoption Model System will be used against me 1 Return on Investment ~$25,000 per anesthetic location 1. Stonemetz, J. Anesthesia information management systems marketplace and current vendors. Anesthesiol Clin 29, (2011). 2. Vigoda, M. M., Rothman, B. & Green, J. A. Shortcomings and challenges of information system adoption. Anesthesiol Clin 29, (2011). AIMS Prevalence: ~ 44% of US Academic Medical Centers had implemented an AIMS or were in process 1. Egger Halbeis, C. B., Epstein, R. H., Macario, A., Pearl, R. G. & Grunwald, Z. Adoption of anesthesia information management systems by academic departments in the United States. Anesth Analg 107, (2008). AIMS Prevalence: respondents out of 5000 randomly selected ASA members 24% of respondents had installed AIMS ~ 50% of survey respondents either using an AIMS or in process 1. Trentman, T. L., Mueller, J. T., Ruskin, K. J., Noble, B. N. & Doyle, C. A. Adoption of anesthesia information management systems by US anesthesiologists. J Clin Monit Comput 25, (2011). 4

5 AIMS Prevalence: 2013 & Beyond 1 Survey of US Academic medical centers By 2014, approximately 75% will have AIMS installed Logistic regression predicts that 84% will have AIMS by Academic medical centers are outpacing private practices, possible explanations include AIMS evangelists concentrated in academia Academic hospitals tend to be large, with more financial and IT resources Non clinical time is necessary for the requisite AIMS clinical champion Research and educational benefits more emphasized in academic settings 1. Stol, I. S., Ehrenfeld, J. M. & Epstein, R. H. Technology diffusion of anesthesia information management systems into academic anesthesia departments in the United States. Anesth Analg 118, (2014). AIMS Prevalence: 2013 & Beyond 1 Authors believe rate of AIMS adoption in US academic medical centers will increase even beyond 2012 regression curve due to incentives and hospitals adopting enterprise wide Electronic Medical Records (EMR) systems that include an anesthesia component. (technology diffusion model predicts imitation as major modality of AIMS spread) It is possible that within a few years, trainees will graduate from anesthesia residencies never having used a paper record. 1. Stol, I. S., Ehrenfeld, J. M. & Epstein, R. H. Technology diffusion of anesthesia information management systems into academic anesthesia departments in the United States. Anesth Analg 118, (2014). AARKs are more accurate than paper records 1 30 elective eye operations, commercial AARK vs. handwritten charts 2 For 5 physiologic variables (TV, RR, EtCO2, fio2, SpO2), 23-31% missing; 1-6% erroneous (> 20% variance of handwritten vs. automated), measured as amount of data/time recorded. For 3 variables (SBP, DBP, heart rate) 8-13% missing; 5-11% erroneous. First 15 minutes and last 10 minutes of case accounted for vast majority of missing or erroneous data. 1. Stabile, M. & Cooper, L. Review article: the evolving role of information technology in perioperative patient safety. Can J Anaesth 60, (2013). 2. Lerou, J. G., Dirksen, R., van Daele, M., Nijhuis, G. M. & Crul, J. F. Automated charting of physiological variables in anesthesia: a quantitative comparison of automated versus handwritten anesthesia records. J Clin Monit 4, (1988). 5

6 AARKs are more accurate than paper records 1 Simulator study of handwritten chart accuracy, with providers at various levels of experience/training anesthesia providers Med students, residents, community and academic attendings) Standardized cases in ideal conditions; 3 critical events introduced impacting BP, HR, EtCO 2, and SpO 2 Completeness of charting: ~ 27% No significant relationship to years of practice nor level of training (see Figure 3) Discrepancy No significant relationship to level of training Use of monitor trend function (by 49% of subjects) did not impact completeness nor discrepancy measures. 1. Stabile, M. & Cooper, L. Review article: the evolving role of information technology in perioperative patient safety. Can J Anaesth 60, (2013). 2. Devitt, J. H., Rapanos, T., Kurrek, M., Cohen, M. M. & Shaw, M. The anesthetic record: accuracy and completeness. Can J Anaesth 46, (1999). AARKs are more accurate than paper records 1 Early concerns 2 Reporting of artifacts Anesthesiologists often smooth electronic data 3 Manual recording may actually remove clinically actionable data, and AARK artifacts are detectable 4 10 anesthesiologists assessed data from 24 pairs of unstable anesthetics records selected from a large study: digitized manual records vs.. AARK of the same case Presented blinded arterial pressures, heart rate, EtCO2, and SpO2 Assessed charts for quality of anesthetic, artifacts, and need for interventions (such as give inotrope or decrease ventilation ) Artifacts: 1.05/automated record; 0.32 per digitized manual record Mean Interventions: 5.2 for automated; 4.0 for digitized manual records Legal liability See below Decreased vigilance? See below Early experience these concerns not significant 5 1. Stabile, M. & Cooper, L. Review article: the evolving role of information technology in perioperative patient safety. Can J Anaesth 60, (2013). 2. Devitt, J. H., Rapanos, T., Kurrek, M., Cohen, M. M. & Shaw, M. The anesthetic record: accuracy and completeness. Can J Anaesth 46, (1999). 3. Wax, D. B., Beilin, Y., Hossain, S., Lin, H. M. & Reich, D. L. Manual editing of automatically recorded data in an anesthesia information management system. Anesthesiology 109, (2008). 4. van Schalkwyk, J. M., Lowes, D., Frampton, C. & Merry, A. F. Does manual anaesthetic record capture remove clinically important data? Br J Anaesth 107, (2011). 5. Lanza, V. Automatic record keeping in anaesthesia a nine year Italian experience. Int J Clin Monit Comput 13, (1996). Benefits of AIMS Improved Quality of Care? Anesthesiologists can focus on higher level tasks in lieu of charting data vs. Loss of vigilance or situational awareness 6

7 AIMS and Situational Awareness The act of recording information on the chart forces the anesthesiologist to be aware of the time course and detail of anesthetic events. This awareness is the most important factor in anticipating future events, and correcting untoward events. A mechanically created record has the capacity to be formed without ever passing through the consciousness of the anesthesiologist The effort to create automated anesthetic records, while interesting technical exercises, are dangerous, because they bypass the anesthesiologist, making it easier for essential information to go unrecognized. - Theodore Noel, Noel, T. A. Computerized anesthesia records may be dangerous. Anesthesiology 64(2), 300 (1986). Deeper limitation of graphical paper records (and AARK): What s the story? Both traditional written anesthesia record and the AARK have been criticized as poor vehicles for telling the story of an anesthetic. The current anesthesia record whether handwritten, or automatic, is mindless. 1 Answered in part by Case Summary Note and ongoing free text notes in which commentary and narrative can be stored? Or increased sophistication of meta-data? (more later) 1. Zeitlin, G. L. History of anesthesia records. ASA Newsletter APSF 25th Anniversary Edition, (2011). AIMS and Clinical Practice Task Analysis: Manual Recording Analysis of 3 CABG cases in a teaching hospital, % of time spent logging data on anesthetic record. Recommended adoption of electronic system to record data automatically 1. Kennedy, P. J., Feingold, A., Wiener, E. L. & Hosek, R. S. Analysis of tasks and human factors in anesthesia for coronary artery bypass. Anesth Analg 55, (1976). 7

8 AIMS and Clinical Practice Task Analysis: Manual Recording 2 time studies 1980 and cases performed by CRNAs or residents at The Ohio State University Also recommended that electronic record keeping might reallocate the 10 12% of time required for record keeping McDonald, J. S., Dzwonczyk, R., Gupta, B. & Dahl, M. A second time study of the anaesthetist s intraoperative period. Br J Anaesth 64, (1990). AIMS and Clinical Practice Task Analysis: Manual Recording UCSD 1994 task analysis, workload, and vigilance study 11 GETA cases by new junior residents vs. 11 GETA cases by senior residents and experienced CRNAs Pre intubation 0.9 vs. 0.4 mins mean time spent on recording Post intubation 13.9 vs. 9.4 mins 1. Weinger, M. B. et al. An objective methodology for task analysis and workload assessment in anesthesia providers. Anesthesiology 80, (1994). AIMS and Clinical Practice Vigilance: Electronic vs. Manual Recording Is writing in the paper record necessary or beneficial for anesthesia provider vigilance? 1995 UC Davis Study 1 of anesthesia residents Manual recording vs. human scribe/assistant 36 GA outpatient cases, ASA 1 and 2 Vigilance assessed by detection rate and response time to simulated abnormal value on monitor Similar response rates and times for both groups 1. Loeb, R. G. Manual record keeping is not necessary for anesthesia vigilance. J Clin Monit 11, 9 13 (1995). 8

9 AIMS and Clinical Practice Vigilance: Electronic vs. Manual Recording Woods and Cognitive Science colleagues at The Ohio State University criticism of the UC Davis study 1 The automation simulator the human scribe functioned as a team player: responsive, directable, intelligent, nonintrusive In contrast, automated systems are typically not team players when they are Strong (act autonomously) Silent (provide poor feedback) Clumsy (interrupt human teammates during high workload or critical periods; or add mental burdens during these periods) Difficult to direct (costly for human to instruct the automatic system re: how to change as circumstances change) 1. Woods, D. D., Cook, R. I. & Billings, C. E. The impact of technology on physician cognition and performance. J Clin Monit 11, 5 8 (1995). AIMS and Clinical Practice Task Analysis, Workload, Vigilance: Electronic vs. Manual Recording 1997 Study of senior residents providing anesthesia for 20 CABG cases 1 Randomized to an actual automated system an electronic anesthesia record keeper (EARK) or manual recording Two groups had similar task distributions by task analysis EARK group spent slightly less time record keeping after intubation and before bypass (more time observing monitors and talking with attending) No significant differences between two groups in self reported workload scores, workload density (weighted scores of tasks/minute), or vigilance latency (measured as response time to randomly activated light) (Only 4/20 cases had any record keeping prior to intubation) <charting comes last> 1. Weinger, M. B., Herndon, O. W. & Gaba, D. M. The effect of electronic record keeping and transesophageal echocardiography on task distribution, workload, and vigilance during cardiac anesthesia. Anesthesiology 87, ; discussion 29A (1997). From AARK to AIMS In 1990 s and beyond, exponential increase in inexpensive and reliable computer software and hardware Proliferation of LANs, internet Digital vital sign monitors HL7 and other communication protocols Most important factor: demand for data that paper couldn t satisfy 9

10 Motivation for AIMS 1 Automated anesthesia record keeper (AARK) is only one component of an AIMS AIMS also includes metadata not necessarily captured by an AARK Case events (e.g. in-room, cross-clamp-on) Medication administration i ti A full AIMS is an AARK interfaced with numerous systems such as Pharmacy Admit/Discharge/Transfer systems Laboratory Billing Perioperative Scheduling 1. Kadry, B., Feaster, W. W., Macario, A. & Ehrenfeld, J. M. Anesthesia information management systems: past, present, and future of anesthesia records. Mt Sinai J Med 79, (2012). One word of caution Not all AIMS are created equal Different vendors/products Different configurations Different hardware Different workflows Different case mixes Different cultures Design matters! Interpret literature in this context AIMS will impact care processes, but alone cannot fix all problems AIMS wanted for more than record keeping Motivations to adopt AIMS: 1 the Black Box Quality/Safety group endorsements APSF (2001): endorses and advocates the use of automated record keeping in the perioperative period and the subsequent retrieval and analysis of the data to improve patient safety. 2 Analyze incidents and outcomes data Demonstrate compliance 1 Streamline billing processes 1 1. Stonemetz, J. & Lagasse, R. Rationale for purchasing an AIMS, in Anesthesia informatics (eds Stonemetz, J. & Ruskin, K.) 7 22 (Springer Verlag London Limited, 2008). 2. APSF Board of Directors. American Society of Anesthesiologist Annual Meeting (New Orleans), 2001, Board of Directors Motion. APSF Newsletter Winter,

11 AIMS as AARK Straightforward AARK serves as clinical tool Decreases charting burden Allows the anesthesiologist to face the field and have greater immediate situational awareness Has this fundamental goal been overwhelmed by the use of AIMS for research, compliance, billing, and other purposes? Caveat 1: AIMS They will expect more The Max Robinowitz Principle Computer automation may make you more efficient, but it will not free you of work. (efficiency is work/time or work/cost) They will increase the work expected of you. (but more work will get done) AIMS Here to Stay Who wants (or demands) anesthesia/peri-op data? SCIP (CMS, CDC) NSQIP The Joint Commission PQRS APSF MOCA P4P Private Insurance Third party payers Research and Quality Consortia/Registries Who else? Your medical center, department, colleagues Consumers/potential patients ACGME And don t forget Benefits of Meaningful use 1 Non-hospital based anesthesiologists if EHR by 2015 up to $44,000-$63,750; after 2015 avoid payment reduction Or, support hospital meaningful use and share in benefit? 1. Lai, M. & Kheterpal, S. Creating a real returnon investment for information system implementation: life after HITECH. Anesthesiol Clin 29, (2011). 11

12 AIMS as Complex System As with motivation to develop original anesthetic record, major motivation to develop AIMS was to capture data that could improve anesthetic care. 1 * Collect more data (and create information) about events, interventions in OR, anesthetics for different diseases and types of patients, etc. (more than just vital signs) * Billing data * Compliance data * Quality related data for analysis * Interactivity clinical decision support Reduce ERROR 2,3 1. Stonemetz, J. & Lagasse, R. in Anesthesia informatics (eds Stonemetz, J. & Ruskin, K.) 7 22 (Springer Verlag London Limited, 2008). 2. To err is human: building a safer health system (National academy press, Washington, D.C., 2000). 3. Committee on Quality of Health Care in America and Institute of Medicine. Crossing the Quality Chasm: A New Health System for the 21st Century (national academy press, Washington, d.c., 2001). The Wing Leveler vs.. the Popular mechanics 2. accessed 12 March _N787BA_cockpit.jpg by Alex Beltyukov, Creative Commons The EHR Elephant EHR is different for different stakeholders. Courtesy Scott Springman, U. of Wisconsin 12

13 Caveat 2: AIMS Something for Everyone? Once there is an AIMS, many parties will have ideas of what it is: It s an AARK decrease anesthesiologist reporting burden! acompliance tool! adata acquisition tool for my clinical research project! an improved clinical record keeper to smooth transition to ICU care! the platform for my CQI project! AIMS and Quality AIMS are indispensible for quality management practices including 1 Quality assurance Performance improvement Regulatory and professional standards compliance AIMS improve quality and accessibility of perioperative data Also essential for Clinical Decision Support 1. Vakharia, S. B. & Rinehart, J. Using anesthesia AIMS data in quality management. Int Anesthesiol Clin 52, (2014). AIMS and Clinical Decision Support Passive CDS information available as you do your job Active CDS alerts, warnings based on your action or inaction 1 Effective 2 Distracting, interrupting? 3,4 1. Vakharia, S. B. & Rinehart, J. Using anesthesia AIMS data in quality management. Int Anesthesiol Clin 52, (2014). 2. Bright, T. J. et al. Effect of clinical decision support systems: a systematic review. Ann Intern Med 157, (2012). 3. Bates, D. W. et al. Ten commandments for effective clinical decision support: making the practice of evidencebased medicine a reality. J Am Med Inform Assoc 10, (2003). 4. Westbrook, J. I. et al. The impact of interruptions on clinical task completion. Qual Saf Health Care 19, (2010). 13

14 AIMS and Clinical Decision Support Types of Active vs Passive CDS Table from Vakharia, S. B. & Rinehart, J. Using anesthesia AIMS data in quality management. Int Anesthesiol Clin 52, (2014). AIMS Clinical Decision Support An alarming issue. Excessive AIMS alerts may be analogous to excessive monitor alarms and could lead to alarm/alert fatigue. A system for managing these alerts is recommended. See [ 1 ] for principles for alarm management 1. American Society of Anesthesiologists House of Delegates. Statement on principles for alarm management for anesthesia professionals. (2013). AIMS Clinical Decision Support Alert Criteria Matter - See [1] for theory Human Factors Engineering g Design Matters Hard Stop Modal Dialogue Box Audible Alert See [2] for overview Icon CDS on demand Messaging Photos 3 1. Raymer, K. E., Bergstrom, J. & Nyce, J. M. Anaesthesia monitor alarms: a theory driven approach. Ergonomics 55, (2012). 2. Weinger, M. B. & Gaba, D. M. Human factors engineering in patient safety. Anesthesiology 120, (2014). 3. Hyman, D., Laire, M., Redmond, D. & Kaplan, D. W. The use of patient pictures and verification screens to reduce computerized provider order entry errors. Pediatrics 130, e211 e219 (2012). 14

15 AIMS and Clinical Decision Support University of Washington AIMS interface to near real time decision support engine 1 Pop-Up screen alerts for hypotension in context of high MAC and hypertension in context of ongoing phenylephrine infusion Reduced d frequency and duration of hypotension-high MAC incidents Hypertension-phenylephrine frequency not changed but may have been affected by (retrospective) manual recording of phenylephrine infusion Recommended incorporation of CDS into AIMS, rather than as add-on to increase data sampling frequency 1. Nair, B. G. et al. Anesthesia information management system based near real time decision support to manage intraoperative hypotension and hypertension. Anesth Analg 118, (2014). AIMS and Clinical Decision Support University of Michigan AIMS alerting for patients with potential Acute Lung Injury (ALI) to promote low tidal volume ventilation strategy in OR 1 Just in time randomized controlled trial Enrolled patients with P a O 2 /f i O 2 < ,960 patients analyzed; 100 total patients met criteria for inclusion (had blood gas, > 18 years old, height recorded in system; and ventilator data received in AIMS) Intervention group received alert recommending Vt of 6 mm/kg of Predicted Body Weight Control group received conventional care with no alert Results: Vt/PBW: control group: 8 mm/kg vs.. intervention: 7.2 mm/kg 1. Blum, J. M. et al. Automated alerting and recommendations for the management of patients with preexisting hypoxia and potential acute lung injury: a pilot study. Anesthesiology 119, (2013). AIMS and Compliance/QI Many reports of AIMS CDS projects that improve completeness of compliance and QM related documentation. If these result in appropriate action they may also improve quality of care Design matters Some example uses of AIMS in these domains follow, but first 15

16 AIMS and Billing Improvement in Billing Performance Complete and accurate clinical documentation Compliance with billing documentation regulations Critical event timing Attestations Coding AIMS and Compliance/QI/Billing University of Michigan randomized, controlled, 2 month trial of automated reminders to improve documentation of arterial line placement 1 At documentation of skin incision, if valid arterial line signal and no procedure note intervention group received pager reminder to document procedure. Control group received no reminders Further reminders at case end and on subsequent days Results: Reminder group 88% documented; Control group 75% (p < 0.001) 2 month Phase 2 trial: reminders for all: 99% compliance vs. historic 80% (p < 0.001) resulting in ~$40,500 increased reimbursement for an estimated development cost of $ Kheterpal, S. et al. Electronic reminders improve procedure documentation compliance and professional fee reimbursement. Anesth Analg 104, (2007). 16

17 AIMS and Billing Spring et al, Massachusetts General Hospital study 1 in 2003 transitioned from paper to AIMS. Found that after go-live, had numerous claims that could not be submitted due to missing or incorrect documentation. The absence of even a single element or the presence of a wrong element can lead to the rejection of a claim for services rendered. 1. Spring, S. F. et al. Automated documentation error detection and notification improves anesthesia billing performance. Anesthesiology 106, (2007). AIMS and Billing Spring et al, Massachusetts General Hospital study 1 Secondary AIMS Server: Anesthesia Billing Alert System (ABAS) Looks for missing ASA, missing key-times, time sequence errors, missing required attestations, etc. If found, automatically generates pager message to AIMS user logged in. 1. Spring, S. F. et al. Automated documentation error detection and notification improves anesthesia billing performance. Anesthesiology 106, (2007). AIMS and Billing Spring et al, Massachusetts General Hospital study 1 Results 1. Spring, S. F. et al. Automated documentation error detection and notification improves anesthesia billing performance. Anesthesiology 106, (2007). 17

18 AIMS and Billing Freundlich et al University of Michigan prospective, randomized trial of automated reminders for appropriate anesthesia start time documentation 1 Rules to detect compliance with CMS guidelines: start time 1-30 minutes prior to in room time (or > 30 mins, if care description documented in AIMS) Also showed persistence of change. 1. Freundlich, R. E. et al. A randomized trial of automated electronic alerts demonstrating improved reimbursable anesthesia time documentation. J Clin Anesth 25, (2013). AIMS and Billing Integration with Anesthesia Systems is Useful UCSF Compensation and Billing AIMS data extract to home-grown compensation database AIMS data evaluated by professional coders to generate bills Scheduling done on Departmental t database; integrates, somewhat, with AIMS to facilitate case assignment AIMS Quality/Compliance U. of Washington multi-modal program to improve compliance with pre-surgical antibiotic administration measures 1 AIMS Install feedback on antibiotic documentation failures Distribution of antibiotic compliance report SAM: Smart Anesthesia Messenger real time feedback on missing documentation If no antibiotic admin at time of Anesthesia Ready, and administration plan note indicated antibiotics were warranted. 1. Nair, B. G., Newman, S. F., Peterson, G. N., Wu, W. Y. & Schwid, H. A. Feedback mechanisms including real time electronic alerts to achieve near 100% timely prophylactic antibiotic administration in surgical cases. Anesth Analg 111, (2010). 18

19 AIMS Quality/Compliance University of Washington program to improve antibiotic administration measures 1 1. Nair, B. G., Newman, S. F., Peterson, G. N., Wu, W. Y. & Schwid, H. A. Feedback mechanisms including real time electronic alerts to achieve near 100% timely prophylactic antibiotic administration in surgical cases. Anesth Analg 111, (2010). AIMS Quality/Compliance University of Washington program to improve compliance with pre-surgical antibiotic administration measures 1 Most effective intervention was SAM No Training Required Cheap ($30,000 development cost) Unable to uninstall it to do more head tohead comparisons because it was so effective 1. Nair, B. G., Newman, S. F., Peterson, G. N., Wu, W. Y. & Schwid, H. A. Feedback mechanisms including real time electronic alerts to achieve near 100% timely prophylactic antibiotic administration in surgical cases. Anesth Analg 111, (2010). AIMS Quality/Compliance SAM also used to improve documentation of perioperative beta-blocker administration 1 Baseline compliance was ~ 65.8% Decreased to ~60.5% with AIMS based documentation of beta blocker admin Once SAM-based alerts implemented, compliance rose to ~94.6% 1. Nair, B. G. et al. Improving documentation of a beta blocker quality measure through an anesthesia information management system and real time notification of documentation errors. Jt Comm J Qual Patient Saf 38, (2012). 19

20 AIMS Quality/Compliance For technical description of SAM and discussion of other SAM-based interventions as of 2013, see [1] (and the SAM based intervention in Clinical Decision Support, above) 1. Nair, B. G., Newman, S. F., Peterson, G. N. & Schwid, H. A. Smart Anesthesia Manager (SAM) a real time decision support system for anesthesia care during surgery. IEEE Trans Biomed Eng 60, (2013). AIMS Quality/Compliance Grant et al post hoc review of ~4,000 cases over 17 months Identification of cases with adverse physiologic events, by algorithmic review of AARK data; confirmed by physician review 3.3% of cases found to have adverse events Specifically for colonoscopy, rate was 6.3% and, with respect to laryngospasm during desflurane anesthetic, rate was 1.3%. Analysis of these cases led to development and communication of new care guidelines for these types of cases post-intervention analysis showed adverse events by same definition dropped, for colonoscopy, to 2.8% (p<0.005) and for laryngospasm with desflurane, to 0.13% (p<0.001) 1. Grant, C., Ludbrook, G., Hampson, E. A., Semenov, R. & Willis, R. Adverse physiological events under anaesthesia and sedation: a pilot audit of electronic patient records. Anaesth Intensive Care 36, (2008). AIMS and Risk/Liability Background Belief that AIMS could increase liability by Capturing transient physiologic changes of minimal clinical significance that could be misinterpreted by consumers of the electronic record Incorporating artifactual data Or AIMS could reduce liability by Providing a more contemporaneous, complete, and legible rendition of actual events than the handwritten record. 1. Feldman, J. M. Do anesthesia information systems increase malpractice exposure? Results of a survey. Anesth Analg 99, 840 3, table of contents (2004). 20

21 AIMS and Risk/Liability 2004 Survey: 22/55 Departments of Anesthesia 1 Completely Responded 41 malpractice cases 30 dropped In 5 of these, AIS helped document absence of negligence 11 settlement or litigation In 5 of these, AIS facilitated decision to settle For litigation case: 2 instances of AIS assisting defense; no cases in which AIS hindered the defense 18 respondents: AIS valuable for risk management 2 respondents: AIS essential for risk management Zero respondents believed AIS to be harmful for risk management. 19/22 recommended use of AIS as part of risk management strategy 1. Feldman, J. M. Do anesthesia information systems increase malpractice exposure? Results of a survey. Anesth Analg 99, 840 3, table of contents (2004). AIMS and Risk/Liability Selected survey comments 1 I know of 3 cases where the [automated] anesthesia record directly contributed to the anesthesiologist being dismissed (from the case). ) We have few suits in part because we have an electronic anesthetic record. Concern about artifacts is misplaced they re easy to spot. 1. Feldman, J. M. Do anesthesia information systems increase malpractice exposure? Results of a survey. Anesth Analg 99, 840 3, table of contents (2004). AIMS and Risk/Liability Disclaimer I am not a lawyer! I recommend that you consult your lawyer to discuss the medicolegal implications of your anesthetic record and workflow 21

22 AIMS and Risk/Liability One rationale for AIMS is to provide an objective record of what happened How objective and helpful this record may be depends on design and practice considerations. AIMS and Risk/Liability University of Miami case 58 y/o patient underwent craniotomy and suffers post-operative quadriplegia. Automated record keeping (PICIS (v 6.3)) During the case, CRNA provided d break, returned the AIMS to vitals signs screen, and noticed that device data were not being recorded. Although IT/engineering fixed problem, missing data were not entered into the chart (and anesthesia attending was not notified). 1. Vigoda, M. M. & Lubarsky, D. A. Failure to recognize loss of incoming data in an anesthesia recordkeeping system may have increased medical liability. Anesth Analg 102, (2006). AIMS and Risk/Liability University of Miami case 1 Claim filed Investigation: 93 minutes of missing data, likely due to disconnected cable Problems with anesthesia documentation contributed to decision to settle case 1. Vigoda, M. M. & Lubarsky, D. A. Failure to recognize loss of incoming data in an anesthesia record keeping system may have increased medical liability. Anesth Analg 102, (2006). 22

23 AIMS and Risk/Liability University of Miami case 1 Issue 1: Lack of awareness of incomplete record Missing i data that t possibly could refute claim (or support it) Challenged legitimacy of other items in record Required to have every five minute charting 1. Vigoda, M. M. & Lubarsky, D. A. Failure to recognize loss of incoming data in an anesthesia record keeping system may have increased medical liability. Anesth Analg 102, (2006). AIMS and Risk/Liability University of Miami case 1 Issue 1: Lack of awareness of incomplete record Medication window could cover the vital signs display Added an alert for missing data stream(s) Post-case review of chart data integrity Natural anesthesiologist s scan did not include AIMS screen Re-mounted AIMS display on left side of anesthesia machine, near vitals signs monitor 1. Vigoda, M. M. & Lubarsky, D. A. Failure to recognize loss of incoming data in an anesthesia record keeping system may have increased medical liability. Anesth Analg 102, (2006). AIMS and Risk/Liability University of Miami case 1 Issue 1: Lack of awareness of incomplete record Medication window could cover the vital signs display D E S I G N Added an alert for missing data stream(s) M A T T E R S Post-case review of chart data integrity Natural anesthesiologist s scan did not include AIMS screen Re-mounted AIMS display on left side of anesthesia machine, near vitals signs monitor 1. Vigoda, M. M. & Lubarsky, D. A. Failure to recognize loss of incoming data in an anesthesia record keeping system may have increased medical liability. Anesth Analg 102, (2006). 23

24 AIMS and Risk/Liability University of Miami case 1 Issue 2: Timing of Chart Entries Lack of concordance of blood pressure changes and notation of re-zeroing of art line at ear Pre-attestation of presence at extubation by attending Audit trail subpoenaed 1. Vigoda, M. M. & Lubarsky, D. A. Failure to recognize loss of incoming data in an anesthesia record keeping system may have increased medical liability. Anesth Analg 102, (2006). AIMS and Risk/Liability University of Miami case 1 Issue 2: Timing of Chart Entries Challenged practice: charting immediately with option to edit Now documentation at end with option to change time, but never future charting 1. Vigoda, M. M. & Lubarsky, D. A. Failure to recognize loss of incoming data in an anesthesia record keeping system may have increased medical liability. Anesth Analg 102, (2006). University of Miami case Follow-up effort 1 Present at AIMS and Risk/Liability Present at emergence attestation Automated feedback to discourage pre-attestation 1. Vigoda, M. M. & Lubarsky, D. A. The medicolegal importance of enhancing timeliness of documentation when using an anesthesia information system and the response to automated feedback in an academic practice. Anesth Analg 103, 131 6, table of contents (2006). 24

25 AIMS and Risk/Liability Automated information management system produced paper record with 15 minute resolution During case, there was drop in EtCO2 associated with significant ifi blood loss, but this was only apparent at resolution of 1 minute, not on printed summary. Which is official medical record? 1. Green, J. A., Arancibia, C. U. & Colquhoun, A. D. Failure to display a significant change in etco2 on printed automated anesthesia record: case report and medicolegal implications. Society for Technology in Anesthesia (2007). AIMS and Risk/Liability Review of evidence as related to anesthetic records 1 E-discovery Computer forensics Audit Trails 1. Szalados, J. E. The legal implications of anesthesia record shortcomings. Anesthesiology News 33, (2007). AIMS and Risk/Liability Review of evidence as related to anesthetic records 1 Justifiable Reliance Can rely on the AIMS to do what it s supposed to do, i.e. faithfully record data Manufacturer may provide warranty or try to disclaim liability Non-delegable Duty Duty of the anesthesia provider to verify the record Obligation to verify data influenced by Data burden (quantity, complexity) Ease of data review 1. Szalados, J. E. The legal implications of anesthesia record shortcomings. Anesthesiology News 33, (2007). 25

26 AIMS and Risk/Liability Review of evidence as related to anesthetic records 1 Evidence is governed by multiple layers of legislation l HIPPA Audit trails, passwords, e-signatures 1. Szalados, J. E. The legal implications of anesthesia record shortcomings. Anesthesiology News 33, (2007). AIMS and Risk/Liability Discussion of U. of Miami Case 1 If anesthesiologist signed off on case, then there may have been a reasonable duty to review the record (since reviewing a record in PICIS is not burdensome) Spoliation intentional destruction, alteration, or hiding of evidence May sometimes be presumed that party that lost the evidence did so intentionally to prevent harm to their side. 1. Szalados, J. E. The legal implications of anesthesia record shortcomings. Anesthesiology News 33, (2007). AIMS and Risk/Liability Discussion of U. of Miami Case 1 Recommendations medical records are expected to be accurate, legible and complete; the signing physician is expected to authenticate the record and vouch for its truthfulness; and the use of [an AIMS] does not necessarily absolve the signing physician of liability. 1. Szalados, J. E. The legal implications of anesthesia record shortcomings. Anesthesiology News 33, (2007). 26

27 AIMS and Risk/Liability Discussion of STA abstract case 1,2 Official anesthetic record Original data where originally stored Copies and Printouts secondary data If addendum, may cast doubt on record if multiple versions of records exist Court may require original data 1. Green, J. A., Arancibia, C. U. & Colquhoun, A. D. Failure to display a significant change in etco2 on printed automated anesthesia record: case report and medicolegal implications. Society for Technology in Anesthesia (2007). {cited in #2} 2. Szalados, J. E. The legal implications of anesthesia record shortcomings. Anesthesiology News 33, (2007). AIMS and Risk/Liability Reviewer s advice re: STA Case 1,2 [emphasis added] 1. the official record is the data collected by the computer; the printed record is a legally acceptable summary that remains suspect pending analysis of the full electronic record 2. The AIMS database contains the original data it may have components that are not on the summary record, but they are still discoverable. Know what s there 3. Corrections, deletions, addenda, etc. leave a digital trail that may undermine the credibility of your record 1. Green, J. A., Arancibia, C. U. & Colquhoun, A. D. Failure to display a significant change in etco2 on printed automated anesthesia record: case report and medicolegal implications. Society for Technology in Anesthesia (2007). {cited in #2} 2. Szalados, J. E. The legal implications of anesthesia record shortcomings. Anesthesiology News 33, (2007). AIMS and Training Automation of mandated reporting ACGME Case logs 1,2 High concordance with ACGME reports Higher capture of cases Reporting of case-mix to assist program directors to schedule diversity 3 ACGME data has been integrated directly with AIMS and incorporated resident case requests 4 1. Brown, D. L. Using an anesthesia information management system to improve case log data entry and resident workflow. Anesth Analg 112, (2011). 2. Simpao, A. et al. The design and implementation of an automated system for logging clinical experiences using an anesthesia information management system. Anesth Analg 112, (2011). 3. Guffy, P. Personal Communication (2013). 4. Wanderer, J. P., Charnin, J., Driscoll, W. D., Bailin, M. T. & Baker, K. Decision support using anesthesia information management system records and accreditation council for graduate medical education case logs for resident operating room assignments. Anesth Analg 117, (2013). 27

28 AIMS and Training Vanderbilt system for clinical performance Feedback for anesthesia trainees ACGME Next Accreditation System (7/2014) 25 Milestones for six core competencies (Patient care, Medical Knowledge, Systems-based practice, Professionalism, Interpersonal and communication skills, Practice base learning and improvement) to be reported every 6 months To reduce reporting burden and decrease reporting latency, they developed automated system to measure, assess, and report clinical performance to both trainees and program directors 1. Ehrenfeld, J. M., McEvoy, M. D., Furman, W. R., Snyder, D. & Sandberg, W. S. Automated near real time clinical performance feedback for anesthesiology residents: one piece of the milestones puzzle. Anesthesiology 120, (2014). AIMS and Training Vanderbilt system for anesthesia resident clinical performance feedback 1 Created in response to ACGME Next Accreditation System Every 6 month reporting of 25 Milestones for six core competencies (Patient care, Medical Knowledge, Systemsbased practice, Professionalism, Interpersonal and communication skills, Practice base learning and improvement) To reduce reporting burden and decrease reporting latency, system measures, assesses, and report clinical performance to both trainees and program directors 1. Ehrenfeld, J. M., McEvoy, M. D., Furman, W. R., Snyder, D. & Sandberg, W. S. Automated near real time clinical performance feedback for anesthesiology residents: one piece of the milestones puzzle. Anesthesiology 120, (2014). AIMS and Training Vanderbilt system for anesthesia resident clinical performance feedback 1 Goals Real Time Objective Measures Collected as part of routine documentation Minimal clerical efforts for residents and administration 1. Ehrenfeld, J. M., McEvoy, M. D., Furman, W. R., Snyder, D. & Sandberg, W. S. Automated near real time clinical performance feedback for anesthesiology residents: one piece of the milestones puzzle. Anesthesiology 120, (2014). 28

29 AIMS and Training Vanderbilt system for anesthesia resident clinical performance feedback 1 Process grabbed data as they were brought into data warehouse (each night) As starting point, assessed 3 process and 2 outcome measures based on national and local guidelines 1. Ehrenfeld, J. M., McEvoy, M. D., Furman, W. R., Snyder, D. & Sandberg, W. S. Automated near real time clinical performance feedback for anesthesiology residents: one piece of the milestones puzzle. Anesthesiology 120, (2014). AIMS and Training Vanderbilt system for anesthesia resident clinical performance feedback 1 Antibiotic administration prior to procedure Glucose monitoring Central line insertion (appropriate documentation ti elements, e.g. hand hygiene) Pain management (first documented PACU pain score) Temperature management (documentation of temperature, and emergence temp > 36.0 C) 1. Ehrenfeld, J. M., McEvoy, M. D., Furman, W. R., Snyder, D. & Sandberg, W. S. Automated near real time clinical performance feedback for anesthesiology residents: one piece of the milestones puzzle. Anesthesiology 120, (2014). Vanderbilt system for near-real- time anesthesia resident clinical performance feedback 1 AIMS and Training 1. Ehrenfeld, J. M., McEvoy, M. D., Furman, W. R., Snyder, D. & Sandberg, W. S. Automated near real time clinical performance feedback for anesthesiology residents: one piece of the milestones puzzle. Anesthesiology 120, (2014). 29

30 AIMS and Training In era of duty hours limitation, structured handoff advocated 1 At UCSF, development of ARCH Anesthesia Resident Checklist Handoff project 1. DeRienzo, C. M. et al. Handoffs in the era of duty hours reform: a focused review and strategy to address changes in the Accreditation Council for Graduate Medical Education Common Program Requirements. Acad Med 87, (2012). AIMS and Research * Numerous studies using AIMS data locally. * Great interest in utilizing the vast quantities of perioperative data has resulted in several national 1 and international 2 efforts to harvest AIMS data. 1. McCormick, P. J. Breaking out of the silo: sharing perioperative data with national organizations. American society of anesthesiologists newsletter 76, (2012). 2. Cumin, D., Newton Wade, V., Harrison, M. J. & Merry, A. F. Two open access, high quality datasets from anesthetic records. J Am Med Inform Assoc 20, (2013). (this issue of JAMIA concerns sharing patient data; see Sharing data for the public good and protecting individual privacy: informatics solutions to combine different goals) AIMS and Research 1 AQI 2,3 NACOR (2010) National Anesthesia Outcomes Registry > 175 anesthetic groups, > 5 million cases as of 2012 Standardized data dictionary (IOTA, SNOMED) Publically available Approved PQRS registry Accepts pen and paper, billing, as well as AIMS data Institutional cube and inter-institution benchmarks AIRS (2011) Anesthesia Incident Reporting System (? Integration with AIMS) 6 other registries 1. McCormick, P. J. Breaking out of the silo: sharing perioperative data with national organizations. American society of anesthesiologists newsletter 76, (2012) (access 4/2/2014) 3. Dutton, R. P. & Dukatz, A. Quality improvement using automated data sources: the anesthesia quality institute. Anesthesiol Clin 29, (2011). 30

31 AIMS and Research 1 MPOG 2,3 Multicenter Perioperative Outcomes Group University of Michigan Detailed clinical information from AIMS For example see [4] You own your data 1. McCormick, P. J. Breaking out of the silo: sharing perioperative data with national organizations. American society of anesthesiologists newsletter 76, (2012). 2. Kheterpal, S. Perioperative comparative effectiveness research: an opportunity calling. Anesthesiology 111, (2009). 3. Ramachandran, S. K. & Kheterpal, S. Outcomes research using quality improvement databases: evolving opportunities and challenges. Anesthesiol Clin 29, (2011). 4. Bateman, B. T. et al. The risk and outcomes of epidural hematomas after perioperative and obstetric epidural catheterization: a report from the Multicenter Perioperative Outcomes Group Research Consortium. Anesth Analg 116, (2013). AIMS and Research 1 Concerns about using AIMS data for research Data quality artifacts may be easily detected by human review, but may mislead statistical analysis. Validation process is important 1 Clinicians are not research assistants For general advice regarding participation in a registry including project planning, HIPAA, confidentiality, and consent issues see [2], and also discuss with registry representatives 1. Kadry, B., Feaster, W. W., Macario, A. & Ehrenfeld, J. M. Anesthesia information management systems: past, present, and future of anesthesia records. Mt Sinai J Med 79, (2012). 2. McCormick, P. J. Breaking out of the silo: sharing perioperative data with national organizations. American society of anesthesiologists newsletter 76, (2012). AIMS and OR Management Outside of scope of this presentation See [1] for example use of AIMS/peri-op IT data in perioperative management domain. See [2] for excellent perioperative p operations research bibliography by same author (includes an excellent annotated AIMS bibliography) Quick comment to manage, you need data 1. Dexter, F., Marcon, E., Aker, J. & Epstein, R. H. Numbers of simultaneous turnovers calculated from anesthesia or operating room information management system data. Anesth Analg 109, (2009). 2. Dexter, F. et al. Bibliography of operating room management articles. (2014). 31

32 Selecting and Installing an AIMS See [1,- 8] for overview of selecting and installing an AIMS and basic design considerations But, a few comments Anesthesia Module in Enterprise EMR You might not get to choose! Integration and Interoperability Best of Breed anesthesia Module Silo? We are different 9 Integration with enterprise EMR [see 10 for considerations] Or meet in the middle? 1. Ehrenfeld, J. M. & Rehman, M. A. Anesthesia information management systems: a review of functionality and installation considerations. J Clin Monit Comput 25, (2011). 2. Muravchick, S. et al. Anesthesia information management system implementation: a practical guide. Anesth Analg 107, (2008). 3. Reich, D., Levin, M. A., Was, D. B. L. & Kheterpal, S. The basics of anesthesia information management systems. ASA Refresher Courses in Anesthesiology 38, (2010). 4. Douglas, J. R. J. & Ritter, M. J. Implementation of an Anesthesia Information Management System (AIMS). Ochsner J 11, (2011). 5. Pregler, J. AIMS Insights, Part 1. CSA Online First July 5, 2011, 6. Pregler, J. AIMS Insights, Part 2. CSA Online First July 25, Moore, J. AIMS Insights, Part 3. CSA Online First September 6, Guffey, P. Implementing Epic s anesthesia system: from soup to nuts. SPA (Society for Pediatric Anesthesia) News 25(1), Cover (2012). 9. Sandberg, W. S. Anesthesia information management systems: almost there. Anesth Analg 107, (2008). 10. Springman, S. R. Integration of the enterprise electronic health record and anesthesia information management systems. Anesthesiol Clin 29, (2011). Selecting and Installing an AIMS Get the right team together! 7,8 Clinical expertise Representatives from sub-specialties OR Director or equivalent Cultural expertise Quality, compliance, legal, billing folks Executive gravitas Medical center knowledge Training 1. Ehrenfeld, J. M. & Rehman, M. A. Anesthesia information management systems: a review of functionality and installation considerations. J Clin Monit Comput 25, (2011). 2. Muravchick, S. et al. Anesthesia information management system implementation: a practical guide. Anesth Analg 107, (2008). 3. Reich, D., Levin, M. A., Was, D. B. L. & Kheterpal, S. The basics of anesthesia information management systems. ASA Refresher Courses in Anesthesiology 38, (2010). 4. Douglas, J. R. J. & Ritter, M. J. Implementation of an Anesthesia Information Management System (AIMS). Ochsner J 11, (2011). 5. Pregler, J. AIMS Insights, Part 1. CSA Online First July 5, 2011, 6. Pregler, J. AIMS Insights, Part 2. CSA Online First July 25, Moore, J. AIMS Insights, Part 3. CSA Online First September 6, Guffy, P. Implementing Epic s anesthesia system: from soup to nuts. SPA (Society for Pediatric Anesthesia) News 25(1), Cover (2012). 9. Sandberg, W. S. Anesthesia information management systems: almost there. Anesth Analg 107, (2008). 10. Springman, S. R. Integration of the enterprise electronic health record and anesthesia information management systems. Anesthesiol Clin 29, (2011). Selecting and Installing an AIMS Comparing usability one AIMS vs.. another Consider using simulated environment 1 (Simulation can also be useful for comparing customized build options 2 ) My recommendation test drive in a similar environment Survey of current vendors (as of 2011) 3 1. Wanderer, J. P., Rao, A. V., Rothwell, S. H. & Ehrenfeld, J. M. Comparing two anesthesia information management system user interfaces: a usability evaluation. Can J Anaesth 59, (2012). 2. Marian, A. A., Dexter, F., Tucker, P. & Todd, M. M. Comparison of alphabetical versus categorical display format for medication order entry in a simulated touch screen anesthesia information management system: an experiment in clinician computer interaction in anesthesia. BMC Med Inform Decis Mak 12, 46 (2012). 3. Stonemetz, J. Anesthesia information management systems marketplace and current vendors. Anesthesiol Clin 29, (2011). 32

33 Future of AIMS Automation takes off RFID, infrared tracking of patients,? Providers Automated documentation Greater intelligence? Next generation of clinical decision support? Aware of context? New AIMS <-> human interfaces Wearable technologies? Standardization across vendors and medical centers? Integration with other systems? All resulting in Improved usability? 1. Kadry, B., Feaster, W. W., Macario, A. & Ehrenfeld, J. M. Anesthesia information management systems: past, present, and future of anesthesia records. Mt Sinai J Med 79, (2012). My Recommendations AIMS have arrived accept it! Work with your AIMS champion, IT leadership, and vendor to improve your AIMS design Make sure that front-line needs are not lost in the AIMS elephant Use the projects in this report (and others) as inspiration for your own! Collaborate with other AIMS users Borrow their ideas Participate in users groups Consider participation in a national registry Upcoming Events CSA Fall Anesthesia Seminar October 27-31, 2014 Kohala Coast, HI Fairmont Orchid Hawaii CSA Winter Anesthesia Seminar January 12-16, 2015 Wailea Maui, Hawaii Fairmont Kea Lani Visit for more information. 33

34 2014 Winter Anesthesia Seminar The ACA: Four Years Later Where Are We? How Did We Get Here? Where Are We Going? Marc Leib, M.D., J.D. Chair, Committee on Economics California Society of Anesthesiologists April 27, 2014 Disclosures No financial conflicts or disclosures Learning Objectives At the end of this session, attendees should be able to: Discuss the history of the ACA from 2010 to 2014 Identify two predominant mechanisms by which individuals will obtain insurance under the ACA Develop strategies to negotiate their participation in ACOs and Bundled Payment Arrangements Discuss the benefits of participating in a Perioperative Surgical Home 1

35 2010: Confusion Dominated Uncertain times with no clear direction Politics, lawsuits, and mistrust prevailed Much of that remains, but some things are clearer 2012: Less Uncertainty June 28, 2012 Supreme Court Rules: Individual Mandate IS constitutional Mandatory Medicaid Expansion or loss of all Medicaid funding IS NOT Every state will have a Health Insurance Exchange (HIX) by the state or the feds (approximately split among states) States can decide whether to expand Medicaid (50-50 split, but not necessarily the same states) 2013: Implementation Regulations and standards were not published until after the 2012 elections Less than one year to develop and implement ACA infrastructure No end-to-end testing of federal-facilitated market or connections with state exchanges Although widespread political differences on ACA exist, no credible disagreement exists on the initial implementation 2

36 October 1, 2013: CRASH IT WAS A DISASTER! Has been significantly improved and now have over 5 million individuals covered under the various Exchanges, although some disagree about who these policy holders are newly or previously insured Today States have functioning Exchanges or participate in the Federal Facilitated Market But, little information exchanged between the FFM and State Medicaid programs to enroll applicants across programs Applicants might need to go to the FFM and their individual state Medicaid programs to assure enrollment in proper program Current Status Everyone above 133% of the Federal Poverty Level (FPL) has access to some insurance product with subsidies for those up to 400% of FPL In states without Medicaid Expansion, those below this level may not have access to insurance or to Medicaid, leaving many uncovered 3

37 Medicaid Expansion Some red states have expanded their Medicaid programs because it is fiscally prudent to do so In some states federal funds for expansion cover more than the costs of providing care to those covered by the expansion This reduces the states costs for the remainder of their covered populations, saving state tax dollars ACA and Anesthesiologists ACA should decrease uncompensated care, but, at what rates? May y have significantly more Medicaid claims with conversion factors that vary from ~$10 to over $40 per unit Exchange plans are commercial plans, but some want to pay Medicaid rates, which are often less than Medicare rates Other ACA Provisions Other provisions of the ACA that could affect anesthesiologists include: Accountable Care Organizations Bundled Payments Medicaid RAC audits similar to Medicare Increased OIG investigations of Medicaid Payment reductions to for Provider Preventable Conditions 4

38 ACOs and Bundled Payments Both payment methods are efforts by the federal government to decrease fee-forservice payments for individual services This puts much greater economic risks on providers hospitals, physicians, and others Anesthesiologists will have to negotiate their portion of the initial bundled payments and of any Shared Savings generated ACOs Managed care imposed on people who are not enrolled in managed care organizations Providers, not payers, impose restrictions Single sided risk allows providers to have limited share of potential savings without downside financial risk of losses Double sided risk includes greater upside potential but also downside financial risks ACOs According to Forbes Magazine, about 50% of the US population live in areas with at least one Accountable Care Organization Currently about 15% of the population is actually enrolled in an ACO and receive some or all of their care from that entity ACO activities will cover greater numbers of patients over next several years 5

39 ACOs In theory, ACOs provide more efficient care and eliminate unnecessary services compared to fee-for-service Medicare ACO participants share in the savings Medicare beneficiaries do not have to get all care from the ACO in which they enroll, which makes it more difficult for the ACO to control costs and have savings to share Bundled Payments CMS has implemented four bundled payment options for up to 48 conditions: Model 1: Hospital only, discounted rate for inpatient t services; episode of care ends at hospital discharge Model 2: Hospital and post-acute care services for the first 30, 60, or 90 days after discharge Bundled Payments Four models, continued: Model 3: Post-acute care only (SNF, LTC hospital, inpatient rehab center, or home health); begins within 30 days of hospital discharge and extends 30, 60, or 90 days Model 4: Single payment to hospital for all services provided during stay, including physicians and other practitioners, and any readmissions within 30 days of discharge 6

40 Payment Arrangements Fee-for-Service payments will continue to dominate for the foreseeable future, but other payment models are emerging and will increase in importance Anesthesiologists must have a game plan for negotiating payments in alternative payment models, such as ACOs and Bundled Payment Arrangements Negotiating Fees Negotiations should involve more than payments for surgical anesthesia services or payments are likely to decrease Need to demonstrate value added and outline areas that others are not trained to provide, including pre- and post-op care Larger groups may want to develop a version of the Perioperative Surgical Home that makes sense in their institutions Negotiation Barriers Reluctance of early enterprise participants to share additional payments with increased numbers of providers Desire to pay for most services on a discounted FFS basis and reserve savings for a few participants Little appreciation of anesthesiologists contributions to overall success or savings Not seeing us as an integral part of the team that can achieve success in SSA 7

41 Anesthesia Responses Outline anesthesiologists contributions to increased quality or decreased costs: Fewer inpatient days Decreased utilization of blood products Post-op pain management Decrease in cancelled or delayed cases Appropriate pre-operative testing only Increased patient satisfaction scores Perioperative Surgical Home PSH can add significant value to enterprise Surgical costs are a large portion of hospital overhead Reducing costs through appropriate patient and/or resource management increases shared savings for system Participation by anesthesiologists is critical for a successful PSH Anesthesiologists and PSH Anesthesiologists are natural leaders of PSH activities Need to show hospital administrators and other physicians that they will benefit by allowing anesthesiologists to direct PSH Anesthesiologists should benefit from portion of payment attributed to surgical savings resulting from PSH activities 8

42 Conclusions Initial confusion surrounded the ACA with little political will to work together to clarify the system implications between Supreme Court decision brought some clarity in mid-2012, but also opened the door to numerous variations between states Less than 1 year to implement complex infrastructure with disastrous results Conclusions ACA affects anesthesiologists far beyond providing insurance to our patients A multitude of alternative payment models will be implemented in the near future Anesthesiologists must develop strategies to negotiate their share of fees that are not defined by the individual services provided Questions? Marc Leib, M.D., J.D. Chair, ASA Committee on Economics [email protected] 9

43 Upcoming Events CSA Fall Anesthesia Seminar October 27-31, 2014 Kohala Coast, HI Fairmont Orchid Hawaii CSA Winter Anesthesia Seminar January 12-16, 2015 Wailea Maui, Hawaii Fairmont Kea Lani Visit for more information. 10