RM11 Classification adjustments/DRG exercises3 - LINDY AND RIC AND PEDJA RIC AND LINDY.

RM11 Classification adjustments/DRG exercises3 - LINDY AND RIC AND PEDJA
RIC AND LINDY

DRG classifications around the world

Refinement of classes for resource homogeneity and clinical meaningfulness To compensate for “classification failure” The classification may not be able to measure the selection by one hospital of particular types of cases Alternatively use additional flag for a price adjustment – eg use of ventilation, Dx – OR peer hospital flag.

Adjusting for classification limitations

Extra categories in Funding model
outliers and exceptional cases. Many systems use outliers or exceptional case adjustments Well documented example is Victoria. Critics call it “tinkering” or interfering with the signals from the payment mechanism. Others say that it adds precisions and fairness to the payment system. WHO IS RIGHT?

Outpatient and sub-acute caseload and DRGs
Substitution and different models of care When does the care type change? What is the optimum? What is the norm?

Admission and discharge policies
IMPORTANT FOR CONSISTENT DATA ‘APPLES WITH APPLES’

Outpatient activity management tools
Particularly important where substitution with inpatient services can occur eg Work up for a surgical admission The rehabilitation phase of a joint replacement Or even complete episodes Payment neutral incentives Guidelines and clear definitions of payment rules

Simulation of adjustments for funding precision
MODELLING, MODELLING, MODELLING Impact analysis Simulations Feedback and consultation – plan and goal alignment

Outlier policies or classification changes
The need to keep classes to manageable numbers Approaches to specifying outliers.

Clear Description of Costs
Different health systems fund different activities eg private providers usually include capital costs through depreciation, while public providers often have a separate funding mechanism some systems exclude (or “unbundle”) some highly variable/high cost components of care, like intensive care or prostheses The contracted prices should match the appropriate costs This might not happen if you adopt other countries’ costs (eg no blood costs in Australian data).

Specifying Contract Prices
Usually: Relative Value Score (Cost Weight) × Unit Price Cost weights are derived empirically from hospital Data The unit price is negotiated - ideally all hospitals would have same unit price. - the unit price can be modified to reflect:- - differences in cost between groups of hospitals - efficiencies of scale (eg Victoria) - transition arrangements ie “blend” the desired unit price with the hospital’s average cost (as in the Irish Model and the private sector in Australia).

Other Hospital Products
DRGs are only designed to describe acute admitted hospital episodes Different classifications are needed for other hospital services: Outpatients Long stay care Health promotion activities etc

How good are DRGs Typically DRGs explain about 25%-40% of the variation in the costs of treating patients. Hospitals don’t get a random sample of patients. Referral patterns and role delineation means that some hospitals treat sicker patients. Most systems have rules that provide extra payments so hospitals aren’t disadvantaged (ie share financial risk). With appropriate risk mitigation, funding models can explain over 80% of the variation in cost.

Rules to moderate financial risk
Financial risk moderation for individual patients through outlier policy Same day policy Severity co-payments for specific subgroups within DRGs Grants High cost patient “adjustment funding pools”

Outlier Policy Paid below the standard rate Paid the standard rate
Adjustments are made to the “average” rate for patients that stay in hospital for fewer days than or more days than the pre-defined times for each DRG Paid below the standard rate Frequency Paid the standard rate Paid above the standard rate High Outliers Low Outliers Low Boundary High Boundary Average LOS Length of stay (days)

General Casemix Model

Same Day Policy Differences between the costs of DRGs in different hospitals can be due to differences in the proportions of same day cases. This can be due to:- Different types of cases in the DRG (COMPLEXITY) Differences in admission/discharge policy (eg admitting rather than treating on an outpatient basis. Hospital clinical practice Efficiency Setting separate same day payment rate within a DRG can be used to prevent hospitals with mostly overnight patients being inappropriately disadvantaged. Same day payment rates are not always a good idea because they can discourage hospitals from moving to same day care where appropriate – CLINICAL JUDGEMENT IS REQUIRED.

Co-payments for specific subgroups within DRGs
Sometimes it is possible to identify subgroups within a DRG that cost more than the average. In these cases additional payments can be made. Co-payments are best used where groups of patients have higher than average cost in many DRGs. eg in Victoria Mechanical ventilated patients Native Australians Using too many copayments reverts back to input based funding.

Grants or ‘Block funding’
Higher costs for some hospitals can be: difficult to quantify (eg teaching hospital costs) or not directly related to activity (eg running an emergency department; the department must be kept open regardless of the level of activity) In such cases cash grants are often paid to hospitals in addition to activity based funding. Such grants are often easier to use in a public system than in a private system.

Adjustment Funding Pools
A set budget is put aside for allocating additional funding for specific patients based upon applications from hospitals Example 1: New Technology in Victoria AU$3million is set aside and hospitals apply for funding specific technologies in small numbers of patients Example 2: High Cost Patient Pool in Western Australia Approximately 20% of total hospital costs are required to treat the most expensive 5% of patients. These patients are difficult to fund under the casemix “averaging” approach. In WA hospitals are able to apply for additional funding for individual patients, but patients’ records are independently clinically reviewed and payment approved by an industry committee.

Risk Moderation While in Transition
When new activity based funding models are introduced not all hospitals are equally affected- some win and others lose. It is important to protect hospitals from extreme budgetary changes until they have time to adjust to the new funding model (ie find efficiencies). This is usually done by introducing “transition” grants or differential unit prices in the first few years.

Types of Casemix Models
Casemix was initially developed as a prospective payment mechanism (ie this year’s activity determines this year’s funding). This approach is still widely used (eg USA Medicare, Victoria public hospitals and within the Private sector). Prospective payment increases the incentives to achieve technical efficiency but reduces budgetary certainty for hospitals. Casemix can also be used as a retrospective payment mechanism (ie last year’s activity determines this year’s funding). This form of model is used in Ireland and New South Wales. Typically, in retrospective casemix models this year’s budget is set based upon last year’s budget plus growth.

Limiting the total amount of activity
Experience suggests that health expenditure is extremely elastic and the potential to spend money on health care almost certainly exceeds any system’s capacity to pay for that care. Most systems attempt to limit the amount of activity funded by: setting activity caps (ie only funding activity to a certain level) or excluding funding for some health care intervention (eg cosmetic surgery) or introducing patient contributions

Variations around activity Targets
Hospitals cannot exactly identify how many people will be admitted Funding models can be designed to accommodate this uncertainty by funding activity above target activity at a marginal rate and reducing funding at a marginal rate for hospitals failing to achieve target.

Other Components of a successful casemix policy
In the previous slides we have described the technical and process building blocks in developing a successful casemix policy. Non-technical issues are equally important:- Openness and transparency Fairness Stakeholders involved Formal channels of review Willingness to listen and change

TOPICS USE CASES FOR DRGS AND DESIRED CRITERIA THE GROUPING PROCESS
DESIGN OF THE DRG ALGORITHM OVERVIEW OF NATIONAL DRG SYSTEMS PATTERNS OF ADOPTION OF DRGs WHERE/HOW WOULD AN I-DRG FIT IN?

MEASURING ACTIVITY LEVELS AND PAYING FOR THEM
PbR, ABF, PfP, Prospective payment, Casemix funding, Episode payment

GRANULARITY OF CATEGORIES
Terms – concepts +/- 600,000 Snomed CT terms +/- 300,000 Snomed RT concepts Classification categories +/- 15,000 Diagnoses +/- 5,000 Procedures 500<->1000 DRGs 300<->400 ADRGs [+/- 200 SRGs - +/- 100 Clinical service types] 23 MDCs

Other - Care types Service Related Groups – SRGs
Rehabilitation, aged care, specialised nursing Chronic care, Mental health. Service Related Groups – SRGs Specialty utilisation measures – DRG aggeregation Risk adjusted capitation groupings DCGs Care-staging-associated unbundled groupings – eg DBCs

DRG Design Goals Clinical and cost homogeneity,
Exhaustive and mutually exclusive ?????? Materiality, Transparency, Data burden – routine clinical/admin data Quality inputs – required precision clinical, policy, and cost

Principles of Design Groups of healthcare activities which are:
Iso-resource similar resource consumption Derived using readily available data Clinically meaningful Manageable number of groups Describe actual/typical care patterns An eye to incentives for efficiency/quality ?? Mappable from other systems Benchmarking – time series Whilst the need to develop a local classification was recognised, the design criteria of the original DRGs were largely upheld in the first version of the HRGs. These attributes are set out below. The classification should be “interpretable medically, with subclasses of patients from homogenous diagnostic categories.” “Individual classes should be defined on variables that are commonly available on hospital abstracts and are relevant to output utilisation, pertaining to either the condition of the patient or the treatment process.” “There must be a manageable number of classes, preferably in the hundreds instead of thousands, which are mutually exclusive and exhaustive.” “The classes should contain patients with similar expected measures of output utilisation.” “Class definitions must be comparable across the different coding schemes.” Fetter, RB, Shin, Y, Freeman, J, Averill, RF & Thompson, JD (1980), ‘Casemix Measurement by diagnosis related group’, Medical Care, vol. 18, no. 2(supp), pp. 5.

Data Primary data sources Underlying classifications
ICD/Morbidity, Procedures, Patient function Dependent variable E.g. EPISODE: Cost, length of Stay, price, charges Quality indicators Available design and test data sets

Design process Formal timetable of representations, Design and response Germany, USA/Medicare (annual) Semi formal, biannual/annual processes Australia, UK, Nordic Engagement with stakeholders Hospitals, Clinicians, Policy, Commissioners Education

Statistical/classification tools
Discriminant analysis (DA) Uses least squares methods Regression models (multiple and logistic) relationship between multiple variables Artificial Neural Networks interconnected simple processors Tree-based algorithms (CART) Classification and Regression Trees (CART), CHAID, AUTOGRP (Yale) Rules for new groups Size, homogeneity

Clinical input and design
Clinical Panels, representatives of medical associations Australia, UK Formal representation from hospitals, medical associations USA, Germany Direct clinical design input and evaluation Practicing clinicians, full time design

Design Issues USE CASE ISSUES TECHNICAL APPROACH Purpose
Responsibilities Design principles Review and Revising process Currency, DRG unit of activity for payment Setting Independence Unbundling Iso-Resource Groupings Clinically meaningful Comprehensive coverage Readily available data Quantitative rules Statistical Criteria Improving the Explanation of Variance

Data cleaning and input
Grouping Process Data cleaning and input Data Edits Grouping Modelling Reporting Output Standardise data Fixed file format One or multiple files Single patient (interactive) or Multiple patients (Batch) Face validity Consistency Warnings or failures Apply algorithm (s) – Table driven Ungroupables Predictive models Concurrent models Observed v Expected Aggregate statistics Grouping variables Input file & grouping variables Expected values File format(s)

Nord DRG – Respiratory grouping Logic

The full version of the manual is found at www.nordclass.uu.se

Non Specific/Symptoms
Design Structure Body System Or Specialty Surgical Groups Major Surgery Intermediate Surgery Minor Surgery With/WO CC/Age Medical Neoplasms Specific Diagnoses Non Specific/Symptoms Major CC, Severity Scale* *APDRG, APRDRG

Check list CC levels, multiple levels Multiple procedures
Minor, Intermediate, Major + multiple Multiple procedures Procedure escalators, effect of ITCs Treatment packages E.g. renal dialysis, chemotherapy Chronic care Stable, non-stable, catastrophic events Generic design Primary care Cross over with outpatients

Options Build your own DRG system Adapting another country’s system
(no adaptation) International examples Adopt a grouper (e.g. Ireland) Adapt a grouper (e.g. Germany) Develop new grouper (e.g. UK, Australia)

Options (2) Adoption of a procedure classification
Separate decision to Casemix classification E.g. Germany Joint decision e.g. Ireland, Portugal International Standard Grouper Countries need to make decisions on grouper for domestic use (support national policies) Can make a separate decision for international comparisons Advantage in having the two related.

Clinician and other stakeholder input
Clinical Panels, representatives of medical associations Australia, UK Formal representation from hospitals, medical associations USA, Germany Direct clinical design input and evaluation Practicing clinicians, full time design

Overview of country specific variants
USA Medicare’s DRGs: evolution to MS-DRGs (Contributor: Julian Pettengill) Australia ARDRG Canada CMG Germany G-DRG England HRG Nordic DRGs AND OTHERS

International Evolution of DRGs
HCFA v6-18 AP-DRGs v8-15 AN-DRGs v1-3 NACRI CHAMPUS/DoD NY-DRGs v5-7 APR-DRGs v8-15 HCFA v5 (4th Revision) 1988 (CC )exclusions Yale RDRGs 1989 HCFA Version 1-4 England Portugal France AR-DRGs v4 HRG v1-2 HRG v3 1997 HRG v3.5 2003 English Casemix Groups AP-DRGs v16-23 CMS DRGs v19-23 Nord DRG G-DRGs v1-2 HRG v4 2006 Inc Non-Acute AR-DRGs v5 2002 IR-DRGs v1-2 Canada CMG Japan DPC Netherlands DBC Adapted from Fetter R (1999) Casemix Classification Systems, Australian Health Review vol 22 no 2

Medicare’s evolution to MS-DRGs
In 2008 Medicare adopted Medicare-severity DRGs From 1989 to 2007 differences in severity of illness were captured by presence or absence of a CC Early in the 2000s, many hospitals were beginning to take strategic advantage of opportunities for selection: Specialization in cardiac care and orthopedic surgery Development of physician-owned specialty hospitals CMS contracted with 3M to develop MS-DRGs, which: Expanded the number of DRGs from 500 to 750 Completely revised the CC and CC-exclusion lists Many base DRGs are split 3 ways, with MCC, CC, no/CC Medicare made a major revision to its DRG definitions by adopting Medicare severity DRGs or MS-DRGs in When the IPPS was first implemented in 1983, Medicare used a very liberal definition of complications and comorbidities (CCs) because hospitals’ coding of secondary diagnoses was woefully inaccurate and incomplete. So the CC list included lots of codes for chronic conditions and any patient over age 69 was presumed to have at least one CC. The presence or absence of a CC is the indicator or split in the DRG definitions that people commonly think of as capturing differences in resource use due to differences in the burden of coexisting illness. What else is going on that complicates the treatment of the patient’s principle condition, thereby increasing the cost of care? The use of secondary diagnoses in the DRG definitions prompted hospitals to make rapid improvements in coding and reporting. In 1988, Medicare dropped the age criterion and patients were grouped to a DRG with CC only if they had a secondary diagnosis on the claim that was included in the CC list. Except for new diagnosis codes that were created over the years, the CC list remained virtually unchanged until 2008. The motivation for changing the DRG definitions was that we began to see strategic specialization by hospitals. Usually, we think of specialization as a good thing, but this time it appeared to be driven by disparities in profitability between and within DRGs. Physician-owned hospitals were forming and analysis showed that they were gaining a significant financial advantage due to favorable patient selection. After all, who would know patients’ severity of illness better than a physician who referred patients to his own hospital? Following recommendations by the Medicare Payment Advisory Commission or MedPAC, CMS contracted with 3M to develop severity refinements to the DRGs. What I want to show you is the process that 3M and CMS used to make a complete re-analysis and revision of the CC list and to designate diagnoses on that list as either a major CC (MCC), a CC, or not a CC.

How CMS revised the CC list
Is a given diagnosis (Dx), when present as a secondary Dx, a Major CC (MCC), a CC, or not a CC? Clinicians re-evaluated 13,549 Dxs to make initial MCC, CC, no CC assignments, and exclusions CMS measured resource impact for 3 patient groups: The target Dx is present as a 2nd DX, and the patient has: No other 2nd Dx, or all other 2nd Dxs are not CCs At least one other 2nd DX that is a CC, but no 2nd Dx is a MCC At least one other 2nd DX that is a MCC CMS calculated ratios of average charges for each group to average charges for all patients where no 2nd Dx is a CC. So the question CMS and 3M tried to answer is the one posed here. For each possible secondary diagnosis, should it be a MCC, a CC, or not a CC? As in the past, CMS used an iterative process. First, panels of physicians used their clinical judgment to make a preliminary assignment for each diagnosis based on what they thought the resource impact would be when the target diagnosis was present as a secondary diagnosis. Then CMS estimated the actual resource impact for each target diagnosis as a secondary diagnosis. To make the empirical estimates, CMS identified 3 groups of patients as shown on this slide. Then they used the charges from Medicare claims to calculate the ratio of average charges for the claims in each group to the average charges for all claims where no secondary diagnosis qualified as a CC or a MCC. So, for the first group, they calculated average charges for all claims that had the target secondary diagnosis but no other secondary diagnosis qualified as a CC or MCC. Then they calculated the average charge for all claims where none of the secondary diagnoses qualified as a CC or MCC. The average charge for this broader group, which is the denominator of the charge ratio, represents the expected average charge for claims where no secondary diagnosis is a CC. If the charge ratio is about 1.0 or lower, this would suggest that the target diagnosis when it is a secondary diagnosis has about the same impact as a non-CC diagnosis; that is, it doesn’t increase inpatient resource use. For the second group, CMS compared the average charge for claims with the target secondary diagnosis and at least one other secondary diagnosis is a CC to average charges for all claims where no secondary diagnosis qualifies as a CC. Similarly, for the third group, they compared the average charge for claims with the target secondary diagnosis and at least one other secondary diagnosis is a MCC to average charges for claims where no secondary diagnosis qualifies as a CC.

Results Target 2nd Dx Group 1 Group 2 Group 3 CC Class N1 Charge ratio
Benign hypertension 12,308 0.96 40,113 1.72 5,297 2.38 Non-CC Obstructive bronchitis 7,003 1.42 32,276 2.19 13,355 3.04 Respiratory failure 5,332 2.10 118,937 2.94 223,054 3.34 MCC This table shows results for 3 potential secondary diagnoses, including benign essential hypertension, obstructive chronic bronchitis, and acute respiratory failure. The N values show the number of claims that had the target diagnosis as a secondary diagnosis in each of the three analysis groups. If we look at the charge ratios for the three groups for each diagnosis, we can see that the ratio for the first group of hypertension patients was only 0.96, this suggests that hypertension when present as a secondary diagnosis has no greater impact on resource use than other secondary diagnoses that are non-CCs. The charge ratios are higher when, as in group 2, at least one other secondary diagnosis qualifies as a CC, and higher still in group 3 when at least one other secondary diagnosis qualifies and a MCC. When the charge ratio for group 1 is greater than 1, as it is for obstructive bronchitis and respiratory failure, it is clear that the target diagnosis does have a greater impact on resource use than non-CC diagnoses and may be acting more like a CC or an MCC. This method does not provide a specific threshold charge ratio to distinguish between target secondary diagnoses that should be considered a CC versus those that should be considered a MCC. However, after CMS and 3M had examined the results for thousands of diagnoses, they concluded that, subject to agreement from clinicians, when a diagnosis had a charge ratio for group 1 that was greater than 2.0 and near 3.0 for group 2, it had a large enough impact on resource use to qualify as a MCC. Diagnoses with ratios above 1.0 but less than 2.0 for group 1 and near 2.0 for group 2 generally qualify as CCs. After clinicians reviewed the empirical results, they made decisions about whether to change the preliminary designation for any diagnosis. Then the empirical estimates were revised accordingly and reviewed again. Although the method is intriguing, judgment is still essential.

Australian Refined DRG (ARDRG)
AN-DRG v1.0 1992 updated annually 1998 bianually AR-DRG v6, 2008 Commonwealth of Australia, Department of Health and Ageing Clinical Casemix Committee National Casemix and Classification Centre (NCCC), University of Wollongong 23 MDCs, 665 DRGs Surgical heirarchy, principal diagnosis ICD-10-AM, ACHI Increase in groups with CC splits ARDRG v5 The use of these CC levels in the design of AR-DRG produces 222 groups with CC splits, which use different combinations of CC levels and splits where the splits are CCs or other high cost patients, defined by specific age splits, procedures, malignancies or mental health legal status.

AR-DRG CC Splits Complication and comorbidity level (CCL)
patient clinical complexity level (PCCL) assignment CC Level Description Not a complication or comorbidity 1 Minor 2 Moderate 3 Severe 4 Catastrophic The grouper allocates a severity weight, or complication and comorbidity level (CCL) to each diagnosis code in the record and then considers all the values within the record to allocate a patient clinical complexity level (PCCL). The CCLs were determined using both clinical judgment and statistical expertise. It should be noted that the CCL values are not fixed to the codes but change according to the adjacent DRG where the code occurs. The PCCL system gives the AR-DRG system a degree of sophistication lacking in systems where a code either counts as a comorbidity or complication (CC) as being present or not, and where the cumulative effect of multiple CCs is not taken into account. Australian Refined Diagnosis Related Groups, Version 5.1, Definitions Manual, Volume 1. Canberra: Commonwealth of Australia (Department of Health and Ageing) 2004 p. 8.

Criteria for Partitioning groupings ARDRG v5.1
Improved RID – partitioning should achieve a minimum 5% increase in Reduction in Deviance (RID) for LoS and total cost. Where a discrepancy occurs between the LoS and Cost analyses, total cost will take precedence as the dependent variable if the LoS distribution from the cost data can be shown to approximate the LoS distribution for the morbidity data using a chi-squared goodness-of-fit test. Minimum national group size – Size of DRGs created by partitioning an existing group should be the minimum of 10% of the original group and 500 estimated weighted separations (EWS). Where EWS = Estimated annual separations x NHCDC public sector cost weight Difference in resource use – New DRGs should differ in mean LoS by at least 2 days. If the longer stay group has a mean LOS of less than 4 days, then the shorter-stay group mean should not exceed 50% of the longer-stay group mean. The difference in the mean Total Cost of DRGs formed by partitioning an existing DRG should be at least 20% of the mean Total Cost of the higher cost group. New group homogeneity – The CV (LOS and Total Cost) for DRGs formed by partitioning and existing DRG should not exceed 1.3 x CV of the original group. Statistical Methodology for AR-DRG Version 5.0

AR-DRG - Public Submission
Statistical benefit Clinical currency Alignment with health system priorities Implications for funding mechanisms Maintenance of classification system stability

Canada CMG Introduced 1983 Redevelopment in late 1980s, 1990 improved
1997, age & complexity overlays Age,CC not splits in CMGs 5 comorbidity levels 2001, ICD-10-CA introduced 2004 Redevelopment, 2007 CMG+ 2007 CMG+, 2010 (CA 10th revision) 21 MCCs, 560 CMG 5 Factor adjustments Age Category, Comorbidity Level, Flagged Intervention, Intervention Event, Out-of-Hospital Intervention CACS, Ambulatory care DPGs for day procedures, CACS for ambulatory Care, RPGs for rehabilitation, SCIPP for Inpatient Psychiatry and RUGs for care and assessment in the community. groups of interventions is present in a patient’s record: - feeding tubes - pleurocentesis - vascular access device - dialysis - tracheostomy, - radiotherapy, - chemotherapy, - mechanical ventilation greater than 96 hours, - mechanical ventilation less than 96 hours, - paracentesis, - heart resuscitation, - cell saver, - cardioversion, and - parenteral nutrition.

CMG+ Comorbidity Levels
Comorbidities assign patient to one of 5 Comorbidity Levels, impact on resource consumption: Level 0 (0% to 24%) Level 1 (25% to 49%) Level 2 (50% to 74%) Level 3 (75% to 124%) Level 4 (125% or higher)

CMG+ & RIW Resource Intensity Weight (RIW™)
To measure the expected use of hospital resources, CIHI developed weights known as Resource Intensity Weights (RIW™). Each CMG is assigned a weight that represents the relative value of resources that cases within that CMG are expected to consume when compared to other CMGs, and this value is called the RIW. RIWs were originally based on U.S. charge data (1985 New York data for 1991 grouper; 1991/92 Maryland data for 1993 grouper) and Canadian length of stay data. Typical cases within a given CMG™ are assigned a single RIW™. For atypical cases, individual RIWs™ are estimated for each category. A RIW is assigned to each patient on discharge based on the CMG to which they are assigned and other factors, such as age and co-morbidities (CIHI, 1996, 2005). The RIW allows fair comparisons across hospitals, regions, and provinces. CIHI computes the value of RIWs using micro-cost data from other Canadian provinces where these data are collected (see CIHI documentation for further information, CIHI, 1996, 2005). The RIW reflects the relative amount of resources (i.e., the relative cost of care) for each individual. Patients who receive more complex and costly care would receive a higher RIW than those receiving less complex care (CIHI, 1996, 2005). For example: A patient with CMG assigned a value of 2 is expected to consume twice as many resources as a patient with a CMG assigned a value of 1. A patient undergoing hip replacement would consume more resources than a similar person treated for bronchitis. With each CMG, CIHI calculates a weight based on one of three different age groups (0 to 17 years, 19 to 69 years, and 70 years and over) and one of the following complexity levels: 1 - no complexity 2 - complexity related to chronic condition(s) 3 - complexity related to serious/important condition(s) 4 - complexity related to potentially life-threatening condition(s), OR 9 - complexity not assigned NOTE: Most cases with the same CMG/age group/complexity level will have the same RIW. However, atypical cases will be assigned a unique weight that reflects the expected level of resources consumed by that particular case. Atypical cases include cases that result from a transfer to or from an acute care facility, long-stay outliers, deaths, and those who leave the hospital against medical advice. This approach recognizes that, even with a single CMG, there may be varying costs. For example, it may be more costly to provide care to an older person than a younger person, and multiple co-morbidities may also increase associated costs.

Germany G-DRG Introduced 2003, adapted from AN-DRG v4.1
Annual revision via the structured dialogue 40% of all suggestions resulted in adjustments of the weights or the classification calculation of the relative weights of each DRG 2008, 1,137 DRGs, 26 Chapters Incorporates hours on mechanical ventilation

G-DRG Functions 16 different types of functions, global spilt criteria: OR-procedure not related to the principal diagnosis Weight at admission (for patients with an age < 1 year at admission) Specified procedures Complex procedures Complicating procedures Dialysis Polytrauma Procedure on several locations Intensive care therapy with a score above 552 points Intensive care therapy with a score above 1104 points Complex early rehabilitation therapy in geriatrics Early rehabilitation therapy Sequence of complex OR-procedures Specified OR-procedures, conducted at four different time levels Pre-transplantation hospital stay Complicating procedures in conjunction with an allocation to the pre-MDC.

England Healthcare Resource Groups (HRG)
Payment by Results Timetable Healthcare Resource Groups (HRG) and Service Classification Tools (SCT) Implementation of limited HRG Tariffs 2003/04 15 HRGs 2004/05 48 HRGs (piloting of tariff) Tariff based system 2005/2006 Objective 60% total NHS spend Acute Inpatients, Outpatients, A&E, Critical Care, Mental Health

Reimbursement Issues Activity currency (Total Hospital Stay)
Specialised Services (Hospital Tariff Uplift) Research and Development (Grants) Teaching (SIFT Formula) Critical care (per day payment) High length of stay patients (per day payment) Outpatients (per attendance) Chemotherapy (Drug Costs) High cost devices/drugs (Exception payments)

Scope of HRG4 More Settings Increased Services
Designed beyond admitted care Allocates the same procedures to HRGs irrespective setting Increased Services Chemotherapy Critical Care Diagnostic Imaging Emergency & Urgent Care Interventional Radiology Rehabilitation Radiotherapy Specialist Palliative Care Supported by extended underlying OPCS classification (4.3) 2,000+ new codes introduced [25% increase] Non-surgical interventions

HRG v3.1 to v3.5 to v4 V3.1 published 1997
V3.5 Revision October 2002 to May 2003 19 Clinical working Groups Initial Meetings Analysis Meetings Quality Assurance of Recommendations 572 to 610 HRGs HRGv4 1398 groups (A&E, OP, Chemotherapy etc)

English Casemix Classification
Healthcare Resource Groups Originally based on DRGs Developed within the National Health Service Based on groups of diagnoses and surgery/treatments Originally used for treatments outside of resident area Now used to pay hospitals for their activity via a tariff HRG Version 1 1991 HRG Version 2 1994 HRG Version 3 1997 HRG Version 3.5 2003 HRG Version 4 2007

OTHERS FRANCE AUSTRIA JAPAN THAILAND COMMERCIAL – eg 3M MALAYSIA UNU
??VALUE IN A COMPREHENSIVE TABULATION

International Adoption/Development
Country Diagnoses Procedures Casemix USA ICD-9-CM ICD-9-CM, CPT4 HCFA/CMS-DRG, AP/R/APR-DRG Australia ICD-10-AM ACHI AR-DRG, local variations Japan ICD-10, ICF DPG South Korea ICD-10 KHIC-PH Korean DRG Mexico HCFA DRG, version 16 South Africa AP-DRG Austria Austrian LKF Belgium INAMI, /ICD-9-CM AP-DRG (1995), APR-DRG (2002) Denmark Nomesko Nord DRG/DAGS Finland NCSP Nord DRG/Fin France CDAM GHM/GHJ Germany SGVB G-DRG UK OPCS HRG Ireland AR-DRG Italy HCFA-DRG, version 14 Netherlands DBC Dutch coding lists DBC-Groups Norway Norwegian code Nord DRG Portugal HCFA DRG Spain Sweden Switzerland Hungary ICPM-Hungary Hungarian grouper Romania ICPM AP-DRG, HCFA, IR-DRG Russia Turkey, fyrMacedonia Croatia, Serbia Bosnia h. Singapore ICD-10-AM ICD-10 ACHI AR-DRG-V6 Indonesia Malaysia Philippines Mongolia ICD-10 ? ICD-9-cm v3 UNU

TOWARDS AN INTERNATIONAL DRG?
EURO DRG PROJECT – compatible goals? UNU SIMPLIFIED GROUPER PROJECT – collaboration? APPROACHING UPTAKE OF ICD-11 AND ICHI NEED FOR INTERNATIOAL COMPARISONS NEED FOR ACCESSIBLE - OPEN SOURCE MATERIAL COST OF MAINTAINING MULTIPLE PLATFORMS - THE BUSINESS CASE FOR LOCAL VARIANTS

POTENTIAL GOALS STANDARD INTERNATIONAL CORE
At “adjacent” (general) DRG level. Approximately 400 categories Expandable to with complexity splits General CC tables but local levels values Local complexity splits. Initial scope - acute inpatients ??? but expandable to incorporate other care types and setting independence.

Discussion???? Thank you

Statistical Evaluation .. Making it work.
Performance of overall system Reduction in Variance (RIV), R-Squared, &/or RAR Log adjusted e.g. Reduction in Deviance (RID) Individual groups – between hospitals Coefficient of Variance (CV), RIV Contribution to RIV Trimming Explanation of outlier data

R-squared, or Coefficient of Determination

R² does not tell whether
the independent variables are a true cause of the changes in the dependent variable; omitted-variable bias exists; the correct regression was used; the most appropriate set of independent variables has been chosen; there is collinearity present in the data on the explanatory variables; the model might be improved by using transformed versions of the existing set of independent variables.

RIV with multiple factors
Adjusted R2 (often written as and pronounced "R bar squared") is a modification of R2 that adjusts for the number of explanatory terms in a model. Unlike R2, the adjusted R2 increases only if the new term improves the model more than would be expected by chance. The adjusted R2 can be negative, and will always be less than or equal to R2. The adjusted R2 is defined as where p is the total number of regressors in the linear model (but not counting the constant term), n is the sample size, dft is the degrees of freedom n– 1 of the estimate of the population variance of the dependent variable, and dfe is the degrees of freedom n – p – 1 of the estimate of the underlying population error variance. The principle behind the Adjusted R2 statistic can be seen by rewriting the ordinary R2 as Glantz, SA & Slinker, BK (1990), Primer of Applied Regression and Analysis of Variance, New York, McGraw-Hill Health Professions Division.

Revision and Development
Cycle of revision Minor and major revisions Additional data sources Inform new groups, use of other dependent variables Inform new data collection items Splitting groups Combining groups Multivariate analyses Comorbidities, multiple procedures

RM11 Classification adjustments/DRG exercises3 - LINDY AND RIC AND PEDJA RIC AND LINDY.

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