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HCM 540 – Healthcare Operations Management

HCM 540 – Healthcare Operations Management. Process Flow Basics (Chapter 3 in MBPF). General 4-stage framework for managing healthcare resources (staff and physical capacity). Demand/workload characterization and forecasting Translation from demand to capacity Scheduling

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HCM 540 – Healthcare Operations Management

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  1. HCM 540 – Healthcare Operations Management Process Flow Basics (Chapter 3 in MBPF)

  2. General 4-stage framework for managing healthcare resources (staff and physical capacity) • Demand/workload characterization and forecasting • Translation from demand to capacity • Scheduling • Short-term allocation The details of these 4 stages all vary depending on the specific healthcare context.

  3. 1. Demand/workload characterization • Basic process flow physics • How the work flows • Occupancy/census/inventory/work in process analysis • TOD/DOW nature of workload • Healthcare operational data • Getting data about workload • Patient/work classification systems • Different types of work require different levels of resources • Forecasting • Predicting future workload from past and other causal factors • Work measurement and productivity monitoring • Understanding the inputs and outputs relationship • Important component of staffing analysis

  4. 2. Demand  Capacity • Labor and physical capacity costs dominate in healthcare • Queueing and simulation models might be useful for helping to set capacity levels • when tradeoffs between capacity cost and patient delay and/or access is important • hospital bed allocation, ancillary staffing • surgical block allocation, clinic capacity • Staffing analysis • standards, nurse-patient ratios, variable vs. constant tasks, benefit allowances, benchmarking

  5. Good Resources for healthcare operations info and ideas • Institute for Healthcare Improvement - http://www.ihi.org/ • Family practice web site - http://www.aafp.org/ • Journal has nice toolbox - http://www.aafp.org/x7502.xml • Healthcare management engineering mailing list – HME group in Yahoo groups • Very active practitioner forum about process improvement, operations management, industrial engineering, etc. in the healthcare industry • Knoxville ED Study • See course website for PPT, report and xls file for this nice study which was done by a professor at Univ. of Tennessee and a management engineering group

  6. I. Business Process Perspective on Healthcare Delivery Process Management Network of Activities Inputs Outputs • patients, test results • bill, resolved complaint • patients • specimens • phone calls, charts • complaints • $$$ • Uses resources (capital & labor) • Visit multiple locations • nursing care, test processing, chart coding • Value add and non-value (delays) Information

  7. A1 A2 A3 A3 Flow Units &Attributes • Flow units – things that flow through business processes • Ex: patient, information, cash, people, supplies, test results, exams, paper • Attributes – characteristics of flow units • Ex: patient type, acuity, length of stay, admission origin, discharge status Each attribute like index card in a pocket HW1 examples of Processes, Flow Units, Attributes?

  8. As Entities Flow… • Generated (enter system) • ED, walk-in, call for appointment, specimen arrives at lab, charts to medical records and billing, patient admitted • Attributes checked and/or set • time of arrival, preliminary diagnosis, urgency status noted, surgical case type, IP or OP, DRG • Resources gotten and released • registration clerks, nurse, physician, bed, imaging equipment, transporters, biller, customer service rep • Locations visited • inpatient units, ED cubicle, waiting room, radiology, lab, waiting areas • Get processed and/or transformed • care delivered, procedure done, bill generated, chart filed, diagnosis made • May be delayed, combined, split, rejoined, and eventually exit the system

  9. An Urgent Care Clinic Start/Enter Start/ntr Wait Register Complete HHQ Wait Vitals/ Assessment Wait Provider Contact Exam Wait Diagnostic/ Intervention Wait Provider Contact/ Results Wait Discharge Collections MCHC Pharmacy Wait Outside Pharmacy Wait Leave Finish Patients visit a series of queueing systems in series

  10. iGrafx Process

  11. Basic Operational Flow MeasuresCh 3 of MBPF Inputs Processing System Outputs R Flow Rate or throughput = average number of flow units (entities) that flow through a certain point in a process per unit time T Flow time = processing time + wait time (total time in the box) Occupancy or Inventory = number of flow units within the boundaries of some process I R units/time I = units of inventory T = avg flow time R units/time

  12. Throughput (Flow Rate) Concepts • Throughput rates are the number of flow units per unit time • admits/day, tests/hour, phone calls/hour, $/month • Flow is conserved – what flows in, must flow out • Inflow and outflow fluctuate over short term • In > Out  Occupancy, queue or inventory grows • Out > In  Occupancy, queue or inventory shrinks • Long term stable process • Flow In = Flow Out • Can combine and split flows Ri2 = clinic walk-in patients per day Ro= total flow of patients out of clinic per day Process (T=flow time in clinic) Ro= Ri1 + Ri2 Ri1 = scheduled clinic patients per day

  13. Flow Time Concepts • Flow time is amount of time spent in some process • May include both waiting and processing • It’s a duration and measured in units of time • length of stay, exam length, processing time for a test, procedure length, time to register, recovery time • Service rate = 1/avg flow time • Example: avg flow time = 0.5 hours  service rate of 2/hr • Flow time varies for individuals and/or different types of flow units • consider average flow time for now What is overall average time in dotted box? 20 pats/hr R1 R1 = type 1 flow in Type 1 Flow Time10 mins R1+R2 Type 1&25 mins 5 pats/hr R2 = type 2 flow in Type 2 Flow Time20 mins R2

  14. Flow Time, Flow Rate, and Inventory Dynamics Ri(t) = instantaneous inflow rate at time t Ro(t) = instantaneous outflow rate at time t DR(t) = instantaneous inventory (occupancy) build up rate at t DR(t) = Ri(t) - Ro(t) If Ri(t) > Ro(t)  get buildup at rate DR(t) > 0 If Ri(t) = Ro(t) get no change in occupancy If Ri(t) < Ro(t)  get depletion at rate DR(t) < 0

  15. Example: Constant DR during (t1,t2) In other words, during the time period (t1,t2), occupancy is being depleted or is building up at a constant rate DR. Occupancy change = Buildup Rate x Length of Time Interval O(t2)-O(t1) = DR(t2-t1) Example: If system empty at t1, and DR=3 people/minute, how many people are in the system after 10 minutes?

  16. Occupancy & Inventory can be averaged over time for stable processes At 10:10 the inventory will start to build again for next flight. Inventory = 0 from 9:43-10:10 (27 mins) So, what’s the average inventory in here (from 9:10-9:43)? Hint: How can we interpret the AREA of this triangle? Avg inventory = (33(30) + 27(0))/60 minutes = 16.5 people

  17. If you know any two, you can calculate the third You choose what to manage and how Relationship between some important averages Can be applied to many different types of business processes Put “Little’s Law” into Google and you’ll see the wide variety of applications of this basic law of systems Little’s Law: I=RTAverage occupancy = Throughput x Avg. Flow Time Stuff in system = Rate stuff enters x How long it stays x I = R T / / T = I R R = I T

  18. Avg # Customers in Line = Customer arrival rate * Avg Time in line Length of billing cycle = $ in Accounts Rcv / Avg Sales per Month Avg Hospital Daily Census = Admission Rate * Avg Length of Stay Avg # customers at web site = Hit Rate * Avg Time Spent at Site Work in process = work input rate * Avg Processing Time Simple Applications of Little’s Law

  19. In class flow analysis (handout) • Patient Flow Model 01 • one patient type, one unit, infinite capacity • average arrival rate and length of stay given • Patient Flow Model 02 • two patient types with different average length of stay • Exercise 3.10 in MBPF • A little Hotel Occupancy problem (we can always learn from other industries)

  20. Little’s Law in action • Typical daily census report • Monthly summary similar – may include comparison to previous month or same month last year • What does this show? • How created? • What doesn’t this show? The numbers reported in the Free Press a few years ago.

  21. Beyond Averages • Little’s Law is about averages • Average may be meaningless • Example: bimodal distribution from pooling long and short procedure times, extreme DOW volume swings • Upper percentiles • 90% of calls answered in less than 1 minute • 95% of the time we have <= 200 patients in house • Time of day and/or day of week (TOD/DOW) effects may be significant • Seasonal effects may be significant • Range • be careful with minimums and maximums • Example from ED consulting report • Hands on – let’s create some histograms of real healthcare data • We’ll do this with some real length of stay data momentarily

  22. Hospital Census Data • Hard to tell if DOW effect present • Impossible to see TOD effect since data is daily • Seasonality? • At time exceed capacity? • data quality? • is capacity correct? • census reflects patient type

  23. Enhanced Census Reporting Examples • Bed Allocation Committee Monthly Report • Used @ monthly meeting of stakeholders to assess occupancy issues • Daily, weekly census, Overall & M-Thu summaries, 30-60-90 day trends, unit group summaries, validity checks • Obstetrical Occupancy Reports • Used as part of planning for OB expansion Note: Data values and sources have been modified to preserve confidentiality.

  24. Raw Data Summary Data

  25. Discharge timing by hour of week TOD/DOW Avg. and 95%ile DOW Occupancy frequency distribution Discharge timing by hour of day summary

  26. Analysis of Time of Day Dependant Data • Many processes in healthcare have important TOD/DOW effects • high variability and uncertainty in timing of arrivals and length of stay (or duration of process) • overall averages simply not that useful • timing of arrivals, occupancy and discharges drives staffing and capacity planning • Examples: recovery & holding areas, emergency, IP OB, walk-in clinics, call centers, short-stay units • Applies to any units of flow such as tests, phone calls, patients, nursing requirements

  27. If Arrivals and LOS are Random Variables

  28. Then, occupancy is certainly a random variable that depends on TOD and DOW Question: See p34 in IHI Guide. What exactly is Figure 3.1 showing?

  29. Hillmaker – A Tool for Empirical Occupancy Analysis • Data has in/out date-timestamp • admit/discharge, start/stop, enter/exit, etc. • Example: entry and exit times from a surgical holding areas was available in surgical scheduling system • Interested in arrival, discharge, occupancy statistics by time of day and day of week • mean, min, max, and percentiles • Time bins: ½ hr, hr, 2hr, 4hr, 6hr, 8hr • Example: mean and 95%ile of occupancy with ½ hr time bins • Want statistics by some category or classification of interest as well as overall • Example: category created was combination of location (which holding area) and phase of care (preop, phase I, phase II) • Freely available from http://hillmaker.sourceforge.net/

  30. Why Hillmaker needed? • Many processes in healthcare have important TOD/DOW effects • high variability and uncertainty in timing of arrivals and length of stay • overall averages simply not that useful • timing of arrivals, occupancy and discharges drives capacity planning • Examples: recovery & holding areas, emergency, IP OB, walk-in clinics, call centers, short-stay units • Applies to any units of flow such as tests, phone calls, patients, nursing requirements, dollars, specimens, staff, etc. • Provides important first step in applying stochastic patient flow models such as simulation or queueing • Estimation of arrival rate parameters • Standard hospital information systems usually are very weak in area of TOD/DOW metric reporting • Consider the traditional inpatient census report • “Can you explain ‘percentile’ again to me?” said the manager. • Obsession with averages and uncomfortable with distributions • Yes, I’m amazed that such tools aren’t standard fare in a healthcare manager’s arsenal

  31. What Hillmaker Does Scenario data (in/out/ category) Hillmaker (Access) Graphing Templates Arrivals, discharges, occupancy by DateTime-category Arrivals, discharges, occupancy summaries by TOD-DOW-category

  32. In/Out Data

  33. Hillmaker Interface Data source inputs Date/time related inputs Algorithmic options Output products

  34. A portion of Excel graphing engine

  35. Day of week graphs

  36. Getting Hillmaker • http://hillmaker.sourceforge.net/ • Isken, M. W., Hillmaker: An open source occupancy analysis tool. Clinical and Investigative Medicine, 28, 6 (2005) 342-43. • Ceglowski, R. (2006) Could a DSS do this? Analysis of coping with overcrowding in a hospital emergency department, Nosokinetics News (http://www2.wmin.ac.uk/coiec/Nosokinetics32.pdf), 3(2) 3-4.

  37. Sources of Internal Workload DataMeasuring Flow Time & Rate • Departmental information systems • lab, radiology, surgical scheduling, nursing, ED patient tracking, patient transport • Hospital information systems • Reg ADT, billing, appointment scheduling, finance • Data warehouses and data marts • Management engineering, finance, planning, marketing • Clinical data repositories • Log books, tally sheets, hard copy reports (yuck!) • Will devote a session to “business intelligence” technology • data warehousing, OLAP, data mining • Getting data out of information systems • Tips for data collection • See p38 in IHI Guide • I’ll show you some techniques for Excel based data collection tools

  38. Patient Classification • What are our products and services? • What types of workload drives demand? • classifying workload into a manageable number of different classes facilitates forecasting and capacity planning models that are robust to changes in workload mix • A myriad of classification schemes exist for both patient types, procedures, tests • We’ll look in detail at productivity monitoring schemes and nursing classification schemes when we discuss staffing in a few weeks

  39. Guiding Principles for Classification Schemes • Similar bundle of goods and services in diagnosis and treatment of patients • similar resource use intensity • Based on “readily available” data • administrative data, clinical data • Manageable number of classes • Similar clinical characteristics within a class • medically meaningful

  40. Sampling of Patient Classification Systems • MDC, DRG – the basic for PPS • CCS – Clinical Classification Software • AHRQ developed for health service research • CSI, Disease Staging, MedisGroups, RDRG, APR-DRG, SRDRG – severity based systems • APG, APC – outpatient version of DRGs • Service – a simple proxy often used internally (e.g. based on attending physician, surgeon, etc.) • Nursing Unit / Unit Type - another simple proxy • ignores effect of overflows

  41. Why is classification hard? • Not all diseases well understood • Treatments for same disease differ • Coding illnesses is difficult • some classes too narrow, some too broad • Tradeoff between manageable number of classes and within class homogeneity • Severity matters • Administrative easily available but other data in chart more expensive to obtain • Different classification schemes needed for different purposes • resource allocation, financial reimbursement, outcomes analysis

  42. DRGs • Originally intended as production definition for hospitals (dev’d @ Yale by Fetter et al 70’s & early 80’s) • To serve as basis for budgeting, cost control and quality control • Adopted by Medicare in 1983 for PPS • Based on MDC (medical and surgical), ICD9-CM codes, age, some comorbidities & complications • Statistical clustering along with expert medical opinion • See Fetter article in Interfaces for very nice description of DRG development Diagnosis Related Groups: Understanding Hospital PerformanceFetter, Robert B..Interfaces. Linthicum: Jan/Feb 1991. Vol. 21, Iss. 1; p. 6 (21 pages)

  43. Refinements to DRG’s • DRG’s questioned on ability to describe resource use • Limited account of severity • Numerous severity based refinements to DRG’s proposed • Computerized Severity Index • Fetter et al developed Refined DRGs which better reflect severity and resource use • will be phased in by HCFA (now CMS) • Bottom line – no one perfect classification system for resource management • become familiar with many and use each as needed • important to use SOMETHING as gross aggregate measures are not extremely useful for detailed resource management

  44. IHI: Reducing Delays and Waiting Times • IHI’s process improvement framework • General guidance on delay reduction • 27 Change concepts for delay reduction • Redesign the system • Shaping the demand • Matching capacity to demand • Four key examples • Surgery • Emergency Department • Within clinics and physician’s offices • Access to care

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