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Using Statistical Analysis for Staffing Justification

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Using Statistical Analysis for Staffing Justification KENT WALTERS MEDICINE DEPARTMENT ADMINISTRATOR DEPARTMENT OF STEM CELL TRANSPLANTATION AND CELLULAR THERAPY THE UNIVERSITY OF TEXAS M. D. ANDERSON CANCER CENTER HOUSTON, TEXAS FEBRUARY 27, 2014 INTRODUCTION 1. The problem we all face 2. Staffing Justifications 3. Telling the Story 4. The Inverted Triangle 5. Language tips 6. Transplant Program Metrics 7. Show Me the Data 8. Questions and Answers COMMON PROBLEM Words the administrator hears . We need more physicians . We need more midlevels . We need more data managers . We need more….and……more . One thing I never hear is the need for more administrators! Replies from the administrators . We need more physicians . Okay, that’s great. But our transplant volume is increasing at 4% per year. We will need to generate a margin to cover the incremental salary/benefits. We need more midlevels . Typically occurs when census spikes. For every midlevel, we need to generate enough net revenue to maintain at least a net zero effect on the margin. We need more data managers . Very few hard funded. Link to staffing model for forms submission Now is the time to develop consensus on staffing benchmarks STAFFING JUSTIFICATIONS Writing with a purpose . If data is to be meaningful then it is essential to find meaning in the data (numbers) . Always find a story to tell The story text frames the critical data in the context of short and longer term trends. Story text should explore relationships, causes and effects to the extent that they can be supported by statistical data. TELLING THE STORY Utilize the journalistic writing style of the inverted pyramid . This focuses the reader on the end result first . Find the elements that are the most important or compelling to the story . Avoid starting the story text with methodology i.e. the current staffing ratio of patients to midlevel practitioner is 10-13 to one (10-13 patients per midlevel provider) . The lead is the most important element of the text. It should tell the story about the data. It summarizes the story line concisely, clearly, and simply sets the story in context. It should concentrate on one message or theme and contain a minimum of data. The Inverted Triangle for Story Telling Primary Lead (25-40 words: keep it short and to the point) 1. Summary of who, what, why, when, where, and how (or as many available for use without cluttering the lead). 2. Single outstanding fact. Seconday Lead 1. Continue outline of story began in primary lead: a summary of important facts. Review (if continuing story) 1. Condensed statement on outline of story written so far. Details 1. Identify or further identify all people, titles, program aspect. Be sure Details information is (a) accurate, (b) concise, (c) objective, (d) timely, (e) pertinent, and (f) interesting. 2. Attribute both direct and indirect quotes if applicable. 3. Begin chronological story if telling Background of staffing history. 1. This is pertinent information not directly related to the immediate story, but useful information in understanding the story. Lesser Details LANGUAGE: KEEP IT CLEAR, CONCISE AND SIMPLE • Clear and simple writing tips for clear messages: • Use short sentences; • Aim for one idea per sentence; • Break up long sentences; • Start each paragraph with the most important message; • Keep paragraphs short; • Keep your writing crisp LANGUAGE: KEEP IT CLEAR, CONCISE AND SIMPLE • Avoid the passive voice; use the active voice. Passive verbs can be confusing and make writing long- winded and less direct. BAD EXAMPLE: “The midlevel staffing per inpatient service is inadequate and caused by the high census.” GOOD EXAMPLE: “The high census exceeds the capacity of each inpatient team. Two midlevels per inpatient team with 20 patients is the maximum staffing for clinical safety and effectiveness. A GOOD EXAMPLE OF A LEAD PARAGRAPH This request is to create an incremental APN position to staff a half inpatient attending team is prompted by a continued upward trend in the inpatient average daily census (ADC) for the Stem Cell Transplantation and Cellular Therapy (SCT/CT) program. For FY13, the program had a sustained ADC that averaged from 80 to 106. The program currently has four inpatient attending teams whereby inpatients are assigned as equally as possible. An inpatient team is comprised of an attending physician, PharmD, and one APN for each 10 patients. We can safely manage 20 patients per inpatient service team. It is necessary to establish an additional inpatient service to keep up with the volume of appropriate transplant candidates. The creation of the 5th inpatient team is required to maintain optimal patient care for our high acuity transplant patients. The requested incremental APN position will provide the requisite midlevel staffing to this newly created inpatient team. ESSENTIAL METRICS TO TELL THE STORY Metric Qualifier Period Recommended Chart Type Payment per case Overall and payer specific Current Year vs. Prior Year Bar Case Mix (for DRG based programs) Current Year vs. Prior Year Bar and maybe a pie chart (but try and limit use of pie charts since those are static) Case Mix Index (for DRG based programs) Current Year vs. Prior Year Bar Net Revenue Program overall and by payor Current Year vs. Prior Year Bar Inpatient - admissions, discharges, ALOS Current Year vs. Prior Year Line or Bar Actual vs. Expected ALOS 13 month rolling Bar - run chart for upper and lower control limits Actual vs. Expected Complication Rate 13 month rolling Bar - run chart for upper and lower control limits Actual vs. Expected Survival Rate Annually (CIBMTR data) Current Year vs. Prior Year Bar - run chart for upper and lower control limits Bar - run chart for upper and lower control limits Ratio Analysis Multiple 13 month rolling Line Transplants per cFTE Physician At the physician level Current Year vs. Prior Year Paretto chart for the year Transplants by Diagnosis Referring physician level 13 month rolling or annual Bar for annual line for rolling Transplants by Donor Source 14 month rolling or annual Bar for annual line for rolling Activity by professional activity 6 category New patients, OP Consults, Monthly and by most Bar and/or line roll-up Inpatient, Est. OP, recent 12 month reporting Procedures, Other period Activity and gross revenue by billing area Monthly and by most Bar and line (GE Centricity/IDX) Billing area with provider recent 12 month reporting level detail period wRVUs Provider level 13 month rolling or annual Line or table ESSENTIAL METRICS TO TELL THE STORY BMT Provider WRVUs (rolling 12-month summary) WRVUs normalized Provider name Provider type cFTE Feb-13 Mar-13 Apr-13 May-13 Jun-13 Jul-13 Aug-13 Sep-13 Oct-13 Nov-14 Dec-14 Jan-14 Rolling 12 by cFTE Doc1 MD 0.90 635.9 652.4 535.7 462.2 531.1 660.3 851.9 473.7 335.1 553.9 1,108.4 311.8 7,112.4 8,392.6 Doc2 MD 0.25 128.6 322.7 153.5 218.5 31.1 147.5 252.9 323.5 418.1 163.7 120.7 352 2,632.8 11,183.80 Physician sub-total 1.15 764.5 975.1 689.2 680.7 562.2 807.8 1104.8 797.2 753.2 717.6 1229.1 663.8 9,745.2 19,576.4 NP1 NP 1.0 112.3 153 174.1 149.5 153.7 95.3 151.9 177.6 17.5 24.5 81.3 54.4 1,345.1 N/A NP2 NP 1.0 59.1 19.6 26.1 34.4 12.7 19.5 5.3 6.3 2.6 4.7 13.8 6.6 210.7 N/A Nurse Practitioner sub-total 2.0 171.4 172.6 200.2 183.9 166.4 114.8 157.2 183.9 20.1 29.2 95.1 61 1,555.8 N/A Grand Total 935.9 1147.7 889.4 864.6 728.6 922.6 1262 981.1 773.3 746.8 1324.2 724.8 11,301.0 Productivity Benchmark: MGMA 90th percentile is 8,230 (for 12 month time frame) Analysis: both BMT physicians exceed the MGMA 90th percentile for the 12-month reporting period ESSENTIAL METRICS TO TELL THE STORY Growth Rate from Prior Year Transplant Year Total Transplants Auto Allo Allo Related Allo Unrelated Auto Allo Allo Related Allo Unrelated Growth Rate FY91 293 210 83 73 10 FY92 279 190 89 54 35 -4.8% -9.5% 7.2% -26.0% 250.0% FY93 335 207 128 96 32 20.1% 8.9% 43.8% 77.8% -8.6% FY94 422 302 120 91 29 26.0% 45.9% -6.3% -5.2% -9.4% FY95 480 303 177 137 40 13.7% 0.3% 47.5% 50.5% 37.9% FY96 528 318 210 158 52 10.0% 5.0% 18.6% 15.3% 30.0% FY97 538 322 216 157 59 1.9% 1.3% 2.9% -0.6% 13.5% FY98 541 290 251 179 72 0.6% -9.9% 16.2% 14.0% 22.0% FY99 634 320 314 238 76 17.2% 10.3% 25.1% 33.0% 5.6% FY00 532 263 269 191 78 -16.1% -17.8% -14.3% -19.7% 2.6% FY01 569 254 315 196 119 7.0% -3.4% 17.1% 2.6% 52.6% FY02 578 250 328 230 98 1.6% -1.6% 4.1% 17.3% -17.6% FY03 589 258 331 215 116 1.9% 3.2% 0.9% -6.5% 18.4% FY04 638 290 348 192 156 8.3% 12.4% 5.1% -10.7% 34.5% FY05 613 274 304 152 152 -3.9% -5.5% -12.6% -20.8% -2.6% FY06 681 336 345 160 185 11.1% 22.6% 13.5% 5.3% 21.7% FY07 703 331 372 182 190 3.2% -1.5% 7.8% 13.8% 2.7% FY08 690 300 390 189 201 -1.8% -9.4% 4.8% 3.8% 5.8% FY09 711 290 421 180 241 3.0% -3.3% 7.9% -4.8% 19.9% FY10 837 392 445 205 240 17.7% 35.2% 5.7% 13.9% -0.4% FY11 865 410 455 223 232 3.3% 4.6% 2.2% 8.8% -3.3% FY12 848 422 426 170 256 -2.0% 2.9% -6.4% -23.8% 10.3% FY13 900 447 453 182 271 6.1% 5.9% 6.3% 7.1% 5.9% WHAT’S MISSING? • Transplant center benchmarks for staffing • Midlevels • Search Coordinators • Long-term Follow-up • CTL Staffing • Quality Management • Clinic staffing • Data managers VISUALIZING DATA .
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