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Illinois Health Care Spring It Technology Conference

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Illinois Health Care Spring It Technology Conference

  1. 1. Use of IT to Strengthen Care and Agenda Reimbursement in Challenging Times • Challenges & Why Important (Why First) Stepping up to the Quality Imperative – Polish your Stars with Six Sigma and "Shine" › We spend too much on health care Therefore we will have change to spend less? Our health care system is not right – therefore we will change it to Illinois Health Care Assoc. Spring IT Conference be more right? Spring 2009 • Quality and Revenue Improvement utions P Presenter: John Sheridan – P id t – eHDS t J h Sh id President HDS › Immediate & Continuous Culture Change | 4/6/2009 | © eHealth Data Solu Jsheridan@ehds.biz › Risk Management – Shine your Stars! • Lessons / Compliance • Summary - Discussion 2 Disclosures The challenges we face! ons  ealth Data  Solutio • John Sheridan is president of a company that assists managers using data to improve care and We have met the organization performance. enemy and he is us. • This presentation does not mention any specific product or software application which is /2009  |   © eHe represented by Mr. Sheridan. utions utions | 4/6/2009 | © eHealth Data Solu | 4/6/2009 | © eHealth Data Solu |  4/1/ Page 11 3 4
  2. 2. Step by step – 1. Target Declines – 2. Call in Do the right thing! Therapy – 3. Manage the Schedule for MDS Facility: Iowa Place Time: Q4‐08 QI/QM: 9.1 ADL decline • Reimbursement 9 residents decline in ADL 9 residents decline in ADL Resident Case Mix Opportunity on Residents who have declined in their ADL Score on the assessment (and addressed with  › Evidence for more care directs care and service BLACK, JESSICA BRAXTON, JORDAN Assessme Therapy Potential Payment Payment to residents and improves Case Mix CRIMENI, JANINE DAVIDSON, JANINE nt ADL RUG Case RUG Case Payment Days Days Case  DINNOCENZO, JANE Referenc Scor Opportun Case Mix  Opportun Using RUGs to improve Quality Measures DUNN, JANE Resident Name SSN e e Code Mix Code Mix Source ity Points ity Mix  Points – Evaluate residents who decline in ADL and Mobility Scores ENGLE, JANINE EVANKO, JANE Date Inde x Index Q4‐2008 Added Q1‐2008 – ADL decline, mobility worsening – are the 5-Star key measures FLEISSNER, JESSICA BLACK, JESSICA 349‐11‐ 10/23/20 9999 08 15 RAC 1.28 Medicare utions utions – Evaluate potential for restorative care p 349‐45‐ 11/14/20 | 4/6/2009 | © eHealth Data Solu | 4/6/2009 | © eHealth Data Solu BRAXTON, JORDAN BRAXTON JORDAN 9999 08 4 CA1 0 92 RAA 1 040 M di id 0.92 1.040 Medicaid 47 5.64 5 64 45 5.40 5 40 • Quality and Integrity CRIMENI, JANINE 349‐70‐ 11/11/20 9999 08 17 CC2 1.49 RAD 1.62 Medicaid 50 6.5 42 5.46 › Get Ready for Medicare and Medicaid Audit 349‐90‐ 12/14/20 DAVIDSON, JANINE 9999 08 10 IB2 0.85 RAB 1.2 Medicaid 17 5.95 75 26.25 350‐18‐ 10/8/200 DINNOCENZO, JANE 9999 8 12 PD1 0.84 RAB 1.2 Medicaid 84 30.24 8 2.88 All data are real examples that have been de-identified. 5 6 Case Study on IT Evaluation for Optimum Why Use IT? ons  Therapy and Restorative Care Opportunity F-287 Automated Data Processing Requirement ealth Data  Solutio CMI 34 Interval Comparison ‐ Minnesota Place  RUG (1) All Payer Q3‐ (2) All P (1) All P Q3 (2) All Payer Q4‐ Q4 Q3 ‐ Q3 Q4 ‐ Q4 • Intent F 287 F-287 Group 08 08 CMI Days CMI Points Days CMI Points Potential  1.022 Potential  1.046 › …to enable a facility to better monitor a All 6,920 6,873.48 5,954 6,050.50 7,071.06  6,230.11  resident’s decline and progress over time. 0.99 1.02 0.0286  0.0302  Computer-aided data analysis facilitates a more Restorative Care 148.90  107.77  efficient, comprehensive and sophisticated /2009  |   © eHe Therapy 48.68  71.84  review of health data. utions utions Total › Th primary purpose of maintaining th The i f i t i i the | 4/6/2009 | © eHealth Data Solu | 4/6/2009 | © eHealth Data Solu Opportunity 197.58  179.61  assessment data is so a facility can monitor Evaluate Residents and apply restorative care to maintain status and to assist improvement. resident progress over time. The information Where declines have occurred, evaluate and apply therapy (Parts A or B) where appropriate. should be readily available at all times. |  4/1/ Page 22 7 8
  3. 3. CMS has 8 initiatives for Healthcare Recovery Audit Contract Expansion financial integrity - • CMS p g programs related to health care financial integrity: g y › The Medicare Accountability Contractor (MAC) conversion for payers › The Zone Program Integrity Contractor (ZPIC) › The NCCI (National Correct Coding Initiative) › The Medicaid Integrity Contractor (MIC) - Medicaid Payment Integrity › PROBES - After data analysis, Contractors must verify if billing problems exist through probe reviews. utions utions › PERM – Payment Error Rate Measurement – states participate every | 4/6/2009 | © eHealth Data Solu | 4/6/2009 | © eHealth Data Solu three years › CERTs – Comprehensive Error Rate Testing Inpatient Hospital – 85 percent ($823.3 million) RAC - Fees › The RAC – Medicare Payments from 10/1/2007 forward – Automatic Inpatient Rehab – 6 percent ($59.7 million) Region A –12.45 Percent and Complex reviews for appropriate care and correct coding Outpatient Hospital – 4 percent ($44 million) Region B – 12.50 Percent Physicians – 2 percent ($19.9 million) Region C – 9 Percent SNFs – 2 percent ($16.3 million) Region D – 9.49 Percent DME – 1 percent ($6.3 million) 9 10 Ambulance/Lab/Other – < 1 percent ($5.4 million) Prevent/Reduce Recoupment ons  of Medicare and Medicaid Dollars UB-04 / MDS Exceptions [RAC Recovery Audit Contract] ealth Data  Solutio Coun • CMS estimates 10.4 billion in Medicare errors and UB‐04 ‐ MDS Exception Report t 51 Principal Diagnosis does not have a matching ICD9 code or explicit diagnosis field not set on corresponding MDSs a federal 18.0 billion in Medicaid errors. 43 33 Excluded service No admission MDS corresponding to admission date 21 RUG in claim does not match RUG in corresponding MDS 19 Therapy level for patient exceeds therapy cap • Check your electronic records of bills (UB-04) with 16 16 Rehab therapy RUG but no therapy charges Non‐rehab RUGs but with therapy charges those of clinical assessments (MDS). 14 13 Claim type and dates inconsistent with corresponding MDSs Unjustified automatic exception modifier /2009  |   © eHe 13 No qualifying hospital stay before admission 12 Speech therapy but no dysphagia or other allowed diagnosis utions utions • Checks whether Principal and Admitting 11 Therapies on same day may indicate a need for a procedure code modifier | 4/6/2009 | © eHealth Data Solu | 4/6/2009 | © eHealth Data Solu 10 No corresponding MDSs for claim Diagnoses are supported by the care documented 5 4 Admission date is greater than 30 days from qualifying hospital stay Missing occurrence entries for speech therapy and present in the record. 3 3 Therapy cap modifier missing after therapy cap reached Missing occurrence entries for physical therapy › Prevent/reduce RAC recoupment of Medicare 2 2 Missing value code for physical therapy Missing value code for speech therapy and Medicaid $. 1 1 Missing occurrence entries for occupational therapy Outpatient claim dates overlap with part A claim dates without required condition code |  4/1/ 1 Invalid ICD9 code(s)  Page 33 11 12
  4. 4. Challenges and Obstacles The Steps for Buy-In • Too much Data – What can we do? • Recognition of a need • It’s just the data! • Too little time • Discussion about the need • We need to do what is in front of us now • I only know what I know • Find a solution to the need • Excellence demands growth utions utions | 4/6/2009 | © eHealth Data Solu | 4/6/2009 | © eHealth Data Solu › use of new technology and new methods is challenging › growing pains may cause us to seek relief • Support implementation of the solutions no pain, no gain to resolve the need • Can-do spirit! 13 14 Information, Technology and Management National Quality Forum – A National Consensus ons  www.qualityforum.org ealth Data  Solutio • Serious Reportable Events - Transparency & accountability are critical in • Source of Data reducing medical errors (data in the IT systems) • Management of a Discipline › “Tens of thousands of lives are forever changed each year as a result of healthcare errors. There is a critical need to enhance health system capacity, › Concentrated area of knowledge about so that all patients will receive care that is safe and effective.” - NQF President and CEO Janet Corrigan tools and tool sets › Backup and redundancy • There are 28 Events – CMS plans to recoup $ for "Never Events" /2009  |   © eHe utions utions › › Access The SRE list includes: | 4/6/2009 | © eHealth Data Solu | 4/6/2009 | © eHealth Data Solu Injuries caused by care management rather than the underlying disease › Timeliness – “Fall & Fracture” or “High Risk Pressure Ulcer” Errors that occur from failure to follow standard care or institutional practices and • Evaluation and Analysis policies › A discipline of all management › › The events are largely preventable, but also very serious. The errors are of concern to the public and healthcare providers and warrant careful investigation that should be targeted for mandatory public reporting. |  4/1/ Page 44 15 16
  5. 5. 2008/9 Hospitals are Not Paid for Some SREs / NQF is the Basis for the QMs and QI/QMs Never Events (Pay for Performance) – When will Iowa emerge? • 2003: Minnesota is the first state to require reporting entire NQF list. › Now required b CA CT IL IN MA NJ OR VT WA and WY N i d by CA, CT, IL, IN, MA, NJ, OR, VT, WA, d WY. › Other states are considering implementation of the SREs. • October 1, 2008: CMS reduced payment to treat a list of complications CMS deems preventable with good care. › Many of these complications are included on NQF’s SREs list. › The Medicare payment provision encourages the adoption of evidence-based patient safety practices aimed at preventing hospital utions utions acquired conditions. conditions | 4/6/2009 | © eHealth Data Solu | 4/6/2009 | © eHealth Data Solu • The CMS plan › Respond to unintended negative consequences with appropriate corrections and interventions. › Extend the SRE Medicare and Medicaid payment reduction to SNFs. › OIG’s work plan for 2009 is laying the foundation. This process produced the QMs 17 18 Summary of Why SNF Health Care – One Framework ons  ealth Data  Solutio Admin./ED’s Should Work to Improve Quality & Assure Reimbursement • Use of Information Technology 1. The Quality Committee recommends approval of written policy on quality and quality improvement that is approved by the organization’s team and then effectively communicated to all staff and all associated providers. › A logical management system – feedback loop 2. The Quality Committee sets the agenda for the facility’s discussion on quality. of analytics 3. Each Admin/ED has the responsibility to assure that quality receives appropriate time for discussion not only at QA Meetings, but in all activities regarding care. 4. The Admin/ED oversight at her/his facility Quality Committee includes measures for /2009  |   © eHe › Source of data for the analytics quality and resident/patient safety as part of the performance evaluation of the ED, RVP utions utions and Division VP. | 4/6/2009 | © eHealth Data Solu | 4/6/2009 | © eHealth Data Solu 5. The Admin/ED must act with the Quality Committee to improve the quality literacy of the facility staff and volunteers (IT is the source of data) › Tools and tool sets to express the analysis 6. The Quality Committee aligns the mandate for quality improvement in the organization and recommends how this mandate is measured and enforced. Board Engagement in Quality: Findings of a Survey of Hospital and System Leaders - H. Joanna Jiang, PhD,; Carlin Lockee,; Karma Bass, FAHE; and Irene Fraser,PhD; Journal of Healthcare Management 53:2 March/April l 2008 |  4/1/ Page 55 19 20
  6. 6. Suggested QA Process Overview - (IT Dashboard) 1. Each Department Presents Their Own QA Information Outcomes Measurement 2. HSKP – Upkeep of Cleaning Schedules, etc. (automated) (K-Tags) a. Laundry – temperature and maintenance logs for laundry equipment b. Maintenance – review of performance for water temperatures, fire & safety systems, call lights, Outcomes need to be reviewed in the context of a preventative maintenance on specialized equipment t ti i t i li d i t continuous monitoring and improvement process. ti it i di t c. Dietary – audits of food temperatures, dish-machine temperatures, & sanitation levels d. Activities – review of participation (F-249) 3. Customer Service Review This approach requires an organization to follow a. Review results of satisfaction surveys – online presentation – look at history through IT five essential steps: b. Discussion of plan for focused resolution of any issues 1) identify important aspects of care, 4. Grievance/Concern Form Review 2) implement indicators of performance, a. Review summary of concern forms received in last month – trend analysis, etc. utions utions b. Discussion of plan… 3) collect and analyze data, | 4/6/2009 | © eHealth Data Solu | 4/6/2009 | © eHealth Data Solu 5. Loss Control Review – examine recent losses – plan review and development 4) isolate common and special causes, and –(IT reports to insurer) 5) develop and implement solutions that improve either a process 6. Infection Control – review of data from last month(s). Analyze distribution map. (IT) or an outcome. 7. Incident Report – online review of IT data Fall analysis – use projector 8. QI/QM Review – IT data – use projector and review control charts – Coordinate Care Plans Donabedian A. Explorations in quality assessment and monitoring. Volume I: the definition of quality and 9. Survey/Complaint – review issues occurring in last month. approaches to its assessment. Ann Arbor, Mich.: Health Administration Press. 10. Medical Records – review select audits completed in last month (IT based) 21 22 Identify QIs-73.5% of Measures Improved Identify - QI/QMs ons  Long Term Care Aggregate Quality of Care Performance QI/QM Residents Population 2007 2006 Accidents 1.1 Fractures 44 1,109 4.00% 4.90% that are… ealth Data  Solutio 1.2 Falls 327 1,155 28.30% 26.30% Behavior/Emotional Patterns 2.1 Depression 516 1,170 44.10% 47.10% 2.2 Affect on others 330 1,150 28.70% 31.60% 2.2‐Hi Affect on others high risk 308 968 31.80% 35.70% 2.2‐Lo Affect on others low risk 25 215 11.60% 9.20% 2.3 Depression w/o therapy 23D i / h 129 1,155 11.20% 1 155 11 20% 13.80% 13 80% Directly Related to the Survey F-Tags Clinical Management 3.1 Medications 896 1,155 77.60% 79.30% Cognitive Patterns 4.1 Cognitive impairment 66 270 24.40% 27.70% Elimination/Incontinence F-Tag Standard QI/QM 5.1 Incontinence low risk 462 864 53.50% 51.70% 5.2 Catheter 112 1,155 9.70% 13.20% 5.3 Incontinence w/o plan 224 613 36.50% 42.60% 5.4 Impaction 0 1,155 0.00% 0.10% 248 Activities Little or No Activity Infection Control 6.1 Urinary tract infection 198 1,155 17.10% 19.90% Nutrition/Eating 314 7.1 Weight loss 198 1,089 18.20% 19.20% 7.2 Tube feeding 7.3 Dehydration 83 1 1,155 7.20% 1,155 0.10% 7.10% 0.00% Pressure Ulcers High Risk Pressure Ulcers /2009  |   © eHe Pain Management 8.1 Pain 92 1,155 8.00% 11.90% 324 utions utions Prevent Accidents Falls (Last 30 Days) Physical Functioning 9.1 ADL decline 299 1,066 28.00% 31.50% 9.2 Bed or chair 69 1,150 6.00% , 6.00% | 4/6/2009 | © eHealth Data Solu | 4/6/2009 | © eHealth Data Solu 325 9.3 Mobility worse 259 848 30.50% 32.80% 9.4 ROM decline Psychotropic Drug Use 10.1 Psychotropic drug use 163 183 1,146 14.20% 1,050 17.40% 17.50% 18.40% Nutrition Weight Loss (5% - 30 Days) 10.1‐Hi Psychotropic drug use high  329 risk 67 252 26.60% 33.10% 10.1‐Lo Psychotropic drug use low  risk 140 920 15.20% 14.50% Unnecessary Drugs 9+ Medications 10.2 Antianxiety/hypnotic use 275 1,050 26.20% 27.30% 10.3 Frequent hypnotic 83 1,155 7.20% 8.40% Quality of Life 11.1 Physical restraints 15 1,155 1.30% 3.00% 11.2 Little or no activity 110 1,150 9.60% 10.20% Skin Care 12.1 Pressure ulcers high risk 160 847 18.90% 21.10% 12.2 Pressure ulcers low risk 9 429 2.10% 5.30% |  4/1/ Page 66 Post‐Acute Care 13.1 Short‐stay delirium 29 887 3.30% 3.30% 13.2 Short‐stay pain 130 895 14.50% 16.90% 13.3 Short‐stay pressure ulcers 153 808 18.90% 18.70% 23 24
  7. 7. QI/QMs and F-Tag 323 / F-324 QI/QMs and F-Tag 314 Level of Harm Residents Affected Level of Harm Residents Affected Inspectors Determined: (Least>Most) (Few>Some>Many) Inspectors Determined: (Least>Most) (Few>Some>Many) 3=Actual Harm Few Give residents proper 2=Minimal harm Few Make sure each resident is treatment to prevent or potential for being watched and has Facility 1.8% new bed (pressure) actual harm assistance devices when Fx sores or heal existing needed: prevent accidents. USA 1.9% sores. 6% Adequate supervision Prevent and/or heal utions utions 13.60% pressure ulcers | 4/6/2009 | © eHealth Data Solu Assistance devices to | 4/6/2009 | © eHealth Data Solu Facility 11.8% 11 8% Falls prevent accidents NC State Coach Valvano USA 12.9% 12.90% was cognizant of the fact that today's success is 0.0% 3.5% 7.0% 10.5% 14.0% 17.5% tomorrow's failure. 0.00% 2.00% 4.00% 6.00% 8.00% 10.00%12.00%14.00% Q4 – 2007 Fractures & Falls Q4-07 High Risk Pressure Ulcers 25 26 Using and Interpreting SPC Charts* – A First Step Statistical Process Control Charts Key Terms ons  to Cost Effective Continuous Improvement ealth Data  Solutio • Using Statistical Process Control to facilitate Variation: amount, rate, extent, or degree of change* , , , g g quality improvement Standard Deviation: the typical distance from • Correctly identify quality deficiencies the mean (average) Benchmark against state norm, other facilities, In a “normal distribution,” 68% of scores will fall within 1 standard deviation of the mean. and your historical data. /2009  |   © eHe 95% will fall within 2 standard deviations utions utions Create trend and SPC charts to measure change 99 73% will fall within 3 standard deviations 99.73% | 4/6/2009 | © eHealth Data Solu | 4/6/2009 | © eHealth Data Solu over time. 99.9999998% will fall within 6σ (standard deviations) Inclusive of the Upper and Lower “control” limits Use these tools to empower and validate your quality committee. * "variation." Dictionary.com Unabridged (v 1.1). Random House, Inc. 23 Mar. 2007. <Dictionary.com http://dictionary.reference.com/browse/variation>. |  4/1/ Page 77 *Using statistics to identify and target quality improvement 27 28
  8. 8. Hypothetical Example of “Your” Data • Incontinence w/o Plan Evaluating Quality Step by Step Using SPC › Strategy: Red – Needs Improvement › 7.9% gap (UPB-LPB) › 14 periods above national norm 1. 1 Review QI/QMs monthly › 0 periods equal to or below › national norm 8 occasions special cause 2. Group and categorize the QI/QMs › Note: Graph starts at 40% 3. Calculate from the previous month • Pressure Ulcers High 4. Review trends vs. month-to-month QI/QMs Risk › Strategy: Yellow 5. Review the SPC chart to determine: utions utions › 3.5% gap g p Is any change normal variation, or an ariation | 4/6/2009 | © eHealth Data Solu | 4/6/2009 | © eHealth Data Solu › 0 periods above national norm › 14 periods equal to or below national norm Is there a process problem? › 2 occasions special cause › Note: Graph starts at 8% Is quality improving? › Extra-Note: pressure ulcer Are the interventions working? SOW goal = 6.0% - therefore with respect to where we “should” be we are RED 29 30 Statistical Process Control SPC Key Terms, continued ons  ealth Data  Solutio SPC can tell you whether the variation in your QI/QM SPC presents a Dynamic Display scores is due to chance or because of a special cause or Variation in a process is measured by asking: Is it “normal” or the result of a “special” cause? meaningful changes over time.* Does the process need to be re-evaluated? Common Cause Variation – random, due to regular, natural, or Originally developed for the manufacturing field, SPC has ordinary causes. Common Cause Variation is expected to occur. Example: acuity of residents. also been effectively applied to healthcare. Special Cause Variation – due to irregular or unnatural causes. /2009  |   © eHe When special causes are present, a process will be “out of control” utions utions or “unstable”. E ample resident falls unstable Example: | 4/6/2009 | © eHealth Data Solu | 4/6/2009 | © eHealth Data Solu Process = “controlled”: outcomes can be predicted within LIMITS = ? Process ≠ “controlled”: the SPC chart will tell you WHEN to assist and explain WHY. The risk of “tampering” with a controlled process must be balanced against the risk of “under controlling”… |  4/1/ Page 88 *JCAHO and ISO require SPC charts as proof of QA/QI 31 32
  9. 9. SPC Key Terms, Continued. • Data Points: plotted over time Example: Incontinence • Mean: average of data points over time • Upper Control Limit (UCL): + 3 standard deviations from the mean In 2006, an average of 60.3% residents triggered 5.1 each month • Lower Control Limit (LCL): – 3 standard deviations from the mean Percent of Residents with 5.1 per month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec % 54 52 45 88 54 50 57 48 71 70 60 68 with LRI utions utions | 4/6/2009 | © eHealth Data Solu | 4/6/2009 | © eHealth Data Solu 4/6/2009 33 34 Example 1 Trends and SPC ons  ealth Data  Solutio A trend is also an indicator of a special cause. Trend: an unusually long series of consecutive increases or decreases in the data. Many people infer trends that are not really present because the consecutive data points are not long enough to be statistically significant. /2009  |   © eHe utions utions The Th consecutive “ ” of data points must be, minimally ti “run” f d t i t tb | 4/6/2009 | © eHealth Data Solu | 4/6/2009 | © eHealth Data Solu i i ll between 5-7, depending on the size of the data base. Points above / below the average • Is this process stable? Points in an increasing or decreasing trend • Is this level of incontinence acceptable? Points above / below state or national norm |  4/1/ Page 99 35 36

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