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Objectives Describe CHCS Describe the major central repositories that include MTF data Briefly describe the M2 Identify common data quality problems Describe.

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Presentation on theme: "Objectives Describe CHCS Describe the major central repositories that include MTF data Briefly describe the M2 Identify common data quality problems Describe."— Presentation transcript:

1 Objectives Describe CHCS Describe the major central repositories that include MTF data Briefly describe the M2 Identify common data quality problems Describe how M2 Standard Reports can be used to manage data quality Use M2 DQ Standard Reports – Only for attendees of hands-on session

2 The Data’s No Good! And the number of discharges we can recapture is…….. Who cares if the data are bad! We just used the old dartboard method! Since the data is not good At least I didn’t use it! Why fix it?

3 Composite Health Care System Much longer briefing later in course on CHCS High level overview in this session! What is CHCS? – Primary operational system used by MTFs – Used for day-to-day activities within the MTF – Appointing, scheduling, registration, ordering of tests, referrals, etc.. – Importance of CHCS cannot be stressed enough!

4 Composite Health Care System CHCS is the starting point for nearly all MTF data Point of original capture Real-time data Much of the data in CHCS is captured simply because someone is doing their job – For example, when provider orders a prescription in CHCS; a record of that is kept in the CHCS pharmacy file

5 Composite Health Care System CHCS has no central repository – Built a very long time ago – 100+ separate systems! – Significantly hampers usefulness of local data – Richness of CHCS data is a definite plus, but must remember that data are only local – Great for production type studies; not enough for person based work

6 Composite Healthcare System (CHCS) Access NCA Tidewater Pendleton San Diego Etc…. Co Springs Landstuhl No connectivity between 100+ separate systems!

7 Example: MTFs on Eisenhower CHCS Host DMISIDName 0047Eisenhower 0237McPherson 1230Camp Shelby 1550TMC-4 Stockade 7197TMC Connelly 7239TMC Southcom Local CHCS queries only retrieve data for care provided at these MTFs!

8 Example: Inpatient Data Available at EAMC from CHCS Most of the days of care for EAMC area enrollees are not visible in CHCS

9 Composite Health Care System Data Availability Several options for using CHCS Data: – MUMPS Queries – “Fileman” Queries – CACHE – ICDB Varies by MTF what can be done – Larger MTFs tend to have more options Data also available in other central systems

10 CHCS Data Products NameDescriptionAcronym Standard Inpatient Data Record Inpatient Hospital RecordsSIDR AppointmentAppointment records for outpatient visits None! ReferralReferrals for specialty care Standard Ambulatory Data Records Outpatient visit, t-con or inpatient rounds records SADR Ancillary Lab and Rad and Rx Procedure recordsNone! Worldwide Workload Report Summary workload dataWWR

11 CHCS & AHLTA AHLTA new capture system – Intended to be an electronic health record – Replaces (sort of) CHCS Ambulatory Data Module – Unlike ADM, AHLTA built to support provider’s activities (i.e. note taking, reviewing test results, etc) – Overly complex architecture; system problems are common AHLTA writes data to CHCS, which is the used to create a SADR (Called writeback) Still not used in all clinics

12 AHLTA CHCS/ADM SADR APPT Writeback CDR M2 ADM & AHLTA are used to capture ambulatory data SADR file contains ADM & AHLTA information MDR FLOW OF SADR CCE

13 Use of AHLTA for Outpatient Care Very little usage in ER and Same Day Surgery Centers – more for office based care 10% of regular visits still not captured in AHLTA

14 Clinical Data Mart – Enables viewing of some of the more important data from the Clinical Data Repository (AHLTA) – Structured database accessible through Web version of Business Objects – Primary source of data is CDR (and CHCS indirectly) – Also receives nightly file from DEERS – Role-based access; no worldwide access available currently – Not complete enough for many purposes – (Not focus of DQMC for that reason)

15 Expense Accounting System (EAS) Repository EAS is the tri-Service financial system used at MTFs EAS is used to create MEPRS data – Full-Time Equivalent Staff (generally via DMHRS) – Workload (via CHCS) – Expense Information (via Service $$ system) MEPRS codes – Used in all MTF systems Data Availability: – EAS Repository – MDR/M2

16 Pharmacy Data Transaction Service Repository Online Drug Utilization Review System Used by MTFs, Mail Order Contractor and Retail Contractor Excellent source of information about prescription drug usage Data Availability: – Through PDTS Business Objects System – MDR/M2 Reported automatically, when MTF does DUR check

17 MHS Data Repository “Home-grown” business data warehouse – Developed outside normal IT process MDR receives and processes data from a wide variety of sources – Data feed management – File Batching – Data Processing – File Storage & Archiving – Preparation of Extracts for Data Marts

18 Basic Data Flow MEPRS MDR Feed Node Data sent to MDR 24/7 CHCS DEERS Claims MDR Processing, File Storage & Limited Access M2 Batches Others users access in M2 Weekly Monthly

19 Preparation of MDR Files MDR is the “workhorse” – where most of the processing of data occurs. Generally includes: – Archiving and Storage – Person Identification enhancement – Application of DEERS attributes – Addition of market concepts (i.e. catchment) – Addition of DMISID attributes (i.e. enrollment MTF Service, etc) – Grouping (DRG, APC, etc) – Addition of costs and weights (RVUs, RWPs) – And much, much more……… Other systems tend to “catch, store and show” Cleanest, most comprehensive source of data

20 The MHS Mart The “M2”: – Very popular data mart – Contains a subset of MDR data – Many data files from MTFs + other data, too! – Significant functional involvement in development and maintenance – users at all levels in the MHS – Ad-hoc querying or “Standard Reports”

21 Systems to use for Data Quality No one system will answer all your questions! Local systems: – Best for real time or near real time management – “How are we doing?” Corporate systems: – MDR/M2 used for most major initiatives and by local MTFs – Important that data be right there! – M2 Standard Reports are designed to assist with monitoring MTF DQ – “How did we do?”

22 Systems to Use for DQ Mgmt M2 Reports: – Many reports available – Most resemble or are exactly the required DQMC reports – Some on emerging DQ issues – Easy to use – Need only basic M2 knowledge – Must know your MTF DMISID to use MTF Level Reports – Will demonstrate throughout! – Report documentation is in your notebooks

23 Data Quality Monitoring and Improvement MTF Data to Review in the context of data quality attributes: – Standard Inpatient Data Records – Standard Ambulatory Data Records – Pharmacy Data Transaction Service – Expense Assignment System (MEPRS) – MTF Lab and Rad

24 Attributes of Data Quality Completeness – Do I get all of the data that I need? Timeliness – Is the data I need there when I need it? Accuracy – Is the data correct, or at least “correct enough”?

25 Completeness

26 Common Data Quality Items Why do you need complete data?

27 Common Data Quality Items Why do you need complete data? FY w/errorFY w/o error 7,387 7, discharge records lost!

28 Why does it matter? Missing component of health history for beneficiaries Less budget at Service level – Less funds for MTFs Appearance of quality issues Underestimation of productivity and efficiency Improper business planning; poor business case analysis

29 Common Data Quality Items Why can data be incomplete & what can you do about it? – Simple lack of data capture – Incomplete or erroneous transmission of data – Improper processing & handling

30 Lack of Data Capture Some data are captured during the business process Often sent off automatically – Example: Appointment file Real-Time Patient Call Real Time Using CHCS to book appt Daily End of Day Processing Periodic standardized data feeds

31 Lack of Data Capture Data captured during the business process – CHCS tables: Updated in real time while MTF staff does their jobs Not generally used beyond local level Lack of central warehouse makes it difficult – CHCS automated extracts: Appointment File Outpatient Lab, Rad and Rx Files Referral File

32 Lack of Data Capture Some data are captured because a policy or guidance requires it – Unified Biostatistical Utility (UBU) distributes health care coding policy – Example: SIDR - Inpatient Stays – Example: SADR - Completed outpatient visits and inpatient rounds

33 Lack of Data Capture Some data are captured because a policy or guidance requires it – More comprehensive set of health care reporting in private sector; not reported = not paid! – MHS decides whether “juice worth squeeze” since budget not entirely claim based – Examples of data not required: Inpatient Surgical CPT Records Ambulance Records

34 Lack of Data Capture Some data are captured because a policy or guidance requires it – Policy gaps cause some problems analytically – “Lack of Capture”: When policies are not followed – makes analysis harder! – Incentives + Supporting Policy = Best availability of data – Recent improvements

35 Capture Requirements Worldwide Workload Report – Earliest CHCS product with information about MTF care delivery – Monthly summary workload: Visits, Days, Dispositions Year, Month, MTF, MEPRS Code, Patient Category – Historical significance: Major determinant of payments to contractors in early TRICARE contracts (not today!)

36 Example WWR Data MTFCY/CMMEPRS Code BencatCount Visits AdmDispBed Days BAADA BAADR AAARET AAAACT BDADA BDADR B MEPRS Code (Outpatient): Visits A MEPRS Code (Inpatient): Adm, Disp and Days

37 Capture Requirements Worldwide Workload Report – WWR is required by all Services for all of their active MTFs – Reports include one month of data – When WWR file is received, it is usually complete – Changes occur at times; but not common – Often called “gold standard”

38 Capture Requirements Worldwide Workload Report – Used to measure completeness of other MTF workload data sources – Reporting of WWR part of DQMC program – Sent to Service Agencies and then onto MDR MDR NMIC AFMSSA PASBA

39 Capture Requirements Standard Inpatient Data Record – One coded record per inpatient stay – Roughly 250,000 per year – Contains rich detailed data on each stay – Can identify patient and providers; includes diagnosis, treatment and other administrative data Significance: – Primary source for most inpatient data needs.

40 Some Sample Data from SIDR MTFReg NumPat IDAdm DateDisch DateDx 1DRG Pat #111/01/200811/03/2008V Pat #210/16/200810/17/ Pat #210/21/200810/24/ Many more data elements available on SIDR – hundreds of them MTF DMISID + Register Number (PRN) is the way to identify a unique record

41 Capture Requirements Standard Inpatient Data Record – MTF Requirement since late 1980s – All inpatient stays must be coded – Stable data feed – Sent to MHS Data Repository / M2 and derivative systems – No inpatient data sent to Clinical Data Repository or CDM

42 Capture Requirements Standard Inpatient Data Record – Completion of a SIDR requires more effort than completion of WWR – Much more detailed report – Completeness is not usually a problem, though – Well established reporting process

43 Picture of SIDR flow CHCS CHCS, etc MDR SIDRs sent monthly from local CHCS hosts Assembled into one file and processed in MDR Sent to M2 M2

44 MDR Processing of SIDR MDR processing includes: – Applying updates and adding new records – Running through DRG Grouper – Adding RWPs – Adding standardized patient information – Adding costs, PPS data – Many, many more things MDR enhancements are significant – Makes the MDR/M2 SIDR files a very useful choice

45 Completeness of SIDR Data Required reporting element for DQMC Measurement: – Number of SIDRs / # dispositions reported in WWR Expressed as % Complete Can easily be reviewed using M2 Corporate Document – tma.rm.dq.dcip.rept.comp.rep

46 Step-by-Step Retrieving a Standard Report

47 Select the report you want and click retrieve! Use report guide in handout

48 Report is already run! Contains monthly comparisons of inpatient workload data All you have to do is look at it! Service Summary and MTF Detail

49 No obvious holes!

50 Capture Requirements Standard Ambulatory Data Record – Record of (some) provider work – One coded record per outpatient visit, telephone consult, and inpatient round – No requirement for inpatient surgery SADRs – Roughly 30 million per year – Can identify patient and providers; includes diagnosis, treatment and other administrative data Significance: – Primary source for most ambulatory data needs.

51 Some Sample Data Fields from SADR MTFAppt ID NoPat IDAppt DateDiag 1E&M code MEPRS Code Pat #110/31/ BIA Pat #210/09/ BAA Many more data elements available on SADR – hundreds of them MTF DMISID + Appt ID Number (IEN) is the way to identify a unique record

52 Capture Requirements Standard Ambulatory Data Record – MTF Requirement since mid 1990s – Significant issues with completeness – Reporting compliance is part of the issue (more later on system issues) – Sent to MHS Data Repository / M2 and derivative systems – SADR is not sent to Clinical Data Repository but some similar data is; more later

53 Capture Requirements Standard Ambulatory Data Record – Completion of a SADR is entirely separate from WWR – Much more detailed report – Much more complex process – Two different data collection systems (CHCS and AHLTA)

54 MDR Processing of SADR Fundamental part of MDR processing: – Combination of Kept Appointment File and SADR – Appointment file is automatically captured; where SADR requires additional effort at the MTF – Should be a SADR for each kept appointment – If there is an appointment record but no SADR, called an “inferred SADR”

55 Matching SADRs to Appointment Records When ‘processing’ in MDR: Compare appt and SADR; record by record. Missing a SADR for Appt # 4. #4 will be in the MDR database as an ‘inferred SADR’. SADR #APPT #

56 Final MDR Data Set # Compliance StatusProvPatientClinicE&M 1RealJONRMARYBAA RealJONRJOEBAA RealJONRJANEBAA InferredJONRNANBAAN/A 5RealJONRALBAA RealJONRROBBAA RealJONRSARABAA99499 Appt # 4 has no E&M because no SADR has been collected. This is an appointment-based record

57 MDR Processing of SADR In addition to combining with appt data, MDR processing includes: – Applying updates and adding new records – Combining with appointment file to include records w – Running through APG/APC Grouper – Adding RVUs – Adding standardized patient information – Adding costs, PPS data – Many, many more things MDR enhancements are significant – Makes the MDR/M2 SADR files a very useful choice

58 Completeness of SADR Data Two common ways to measure – Official way is to compare WWR to SADRs – Method developed when appointment data was unavailable – Not a precise match – WWR includes only those encounters deemed “count”; SADR includes all appoinments

59 Concept of a Count Visit Hash mark counting – Early days of MHS – No systems to use to report detailed data – Count visit used to discern between ‘real medical care’ and ‘not’ Inconsistent use – Not recommended for analytic purposes across MTFs – Used by many systems Non-count visits DO earn RVUs – SADRs are expected for both count and non-count visits!

60 All Encounters: N= 32 Million “Count Only N= 29 Million 3.5 Million Non-Count Visits worth almost 1 Million RVUs!

61 Count Visits Care delivered where primary provider is a general duty nurse – FY08 MTF SvcCountNon-CountTotal% Count Army 197, , ,02557% AF 92, , ,42627% Navy 172, , ,76952% Total 461, ,622 1,012,22046%

62 Completeness of SADR Data with WWR Benchmark Required reporting element for DQMC Measurement: – Number of SADRs in B Clinics (and FBN) / # count visits reported in WWR Expressed as % Complete Should be 100% Can easily be reviewed using M2 Corporate Document – tma.rm.dq.dcop.rep.comp.wwr.rep Currently, each report has only one year. Multi-year report under construction

63 Completeness of SADR Data with Appointment Benchmark Final MDR Data Set # Compliance StatusProvPatientClinicE&M 1RealJONRMARYBAA RealJONRJOEBAA RealJONRJANEBAA InferredJONRNANBAAN/A 5RealJONRALBAA99213 Combination of kept appointments and SADR makes precise measurement of missing SADRs possible. Perfect compliance would be 100% No “Inferred” Records

64 Completeness of SADR Data with Appointment Benchmark Not a required reporting element for DQMC Based on the ‘by record’ match Gives a better answer than official metric And is actionable since you can identify missing records Measurement: – Number of reported SADRs in B Clinics (and FBN) / # total kept appointments in same clinics Expressed as % Complete Can easily be reviewed using M2 Corporate Document – Report Name: tma.rm.dq.dcop.rep.comp.apptbench.rep

65 Completed Outpatient Appointments with No SADRs Writeback Meltdown! Major Improvements in Compliance

66 SADR Completeness Action Report Provides record level report of missing SADRs Includes MTF and Appointment Identifier so that MTF may retrieve information about missing record and fix the problem! Also includes estimate of lost PPS $$ due to lack of SADR Prompted filter report: – Data not already run; user is prompted to enter MTF DMISID; then report runs Can easily be reviewed using M2 Corporate Document – Report Name

67

68

69 After entering your DMISID: Kept Appointments with No SADR

70 Use Slice and Dice to determine which clinics are losing the most PPS $$$ due to lack of completeness of SADR

71 Surgical Clinics, Primary Care, ER

72 Back to slice and dice to look at lost earnings by provider

73 “By Provider” list of missed earnings. Identifiers covered up EACH ROW IS A PROVIDER!……. The first provider listed needs to submit 300K worth of SADRs!

74 Back to slice and dice to look at which SADRs are missing.

75 “Record IDs” are the appointment IENs of the missing SADRs Use to find the missing records in ADM or AHLTA

76 MEPRS Expense Assignment System – Financial Accounting – Tri-Service System – Expenses – Workload – Full Time Equivalent Staff Info Summary Data Only – Too aggregated for most business questions – Extremely valuable as a basis for more sophisticated costing methodologies – Only tri-Service source for FTE data

77 MEPRS Data Flow Workload (CHCS) Financial Data ( STANFINS, STARS-FL, GAFS-R) Personnel Data (DMHRSi) EAS-Internet MDR (Large MEPRS dataset) M2 (Smaller MEPRS dataset) (Monthly Processing) (Nightly/Monthly Processing) EAS IV Repository (Full MEPRS dataset) Monthly MEPRS data due 45 days after month end

78 MEPRS Completeness MEPRS Policy requires submission of “MEPRS Package” from all fixed MTFs Preparation of MEPRS extract requires significant effort – MEPRS Manager at each MTF MEPRS reporting is/has been problematic recently – EAS-I – DMHRSi

79 Example of Some MEPRS Data MTF & MEPRS code identifies the reporting unit Staff info from DMHRS (usually) Workload from CHCS (usually) Expenses from Service System + MEPRS Algorithms – Entire section on MEPRS later! MTFMEPRS Code FY/FMAvail Clin FTES Bed DaysTotal Expense Lab Expense 0024AAAA ,1904, BAAA ,779

80 Timeliness

81 Common Data Quality Items Why do you need timely data? Steady trend until recent timeframes Includes FY08 and part of FY09

82 Common Data Quality Items Why do you need timely data? Missing data causes an artificial year to year trend FYDisp 20064, , ,862 Annual Recap

83 Why does it matter? Completeness & Timeliness have the same impacts – Missing component of health history for beneficiaries – Less budget at Service level Less funds for MTFs – Appearance of quality issues – Underestimation of productivity and efficiency – Improper business planning; poor business care analysis

84 Timeliness Standards Data Type Standard/Note SIDR w/in 30 days of discharge SADR 3 days for routine; 15 for APV WWR by 10 th of month MEPRS 45 days after month ends Lab/Rad Auto send PDTS Auto send Appointment Auto Send

85 Timeliness Timeliness Standards are best monitored locally – CHCS, ADM and AHLTA speakers to present Batch processing in MDR/M2 makes it an insufficient tool for monitoring timeliness Very useful for completeness, though

86 Accuracy

87 Completeness and Timeliness: – Analysts always prefer complete data – When not available, common to use historical/available data to estimate missing data Inaccurate data is much more difficult to work with – Can lead to much more damage! – Can’t always apply “workarounds”

88 Accuracy Private sector health care data is reported as part of a payment process – Completeness: Not claimed means not paid! – Timeliness: Delays in submission mean delays in payment – Accuracy: Data elements used to determine payments can get providers in trouble if they are wrong! Code checking / bundling software used

89 Direct Care Direct Care SIDR and SADR: We don’t have the same stick as private sector! – MHS uses policies for completeness and timeliness. – Coding and Compliance Editor (CCE) for code edits – (No bundling software at all) Coding audits required as part of DQMC – Sample size often too small to spot problems – Sometimes, external auditors hired – Since data used for billing (Third Party Collections), bad coding could cause MTF problems, also

90 Coding Creep

91 Direct Care SIDR and SADR M2 is a wonderful tool for analyzing accuracy of data Contains local record identifiers to enable ACTION! Standard Reports for accuracy: – Ungroupable DRGs & APGS – Unlisted Provider Specialty Code – Potential Pharmacy Table Errors – Potential Provider ID Errors Ad-hoc possibilities are limitless

92 Ungroupable DRG Report DRG Grouping software: – Assumes coding rules are followed – Allows for all known or potentially possible combinations of diagnosis and procedure codes Ungroupable DRG: – Rules are not followed in some way; or – Diagnosis and Procedures simply don’t make sense together Ungroupable DRGs receive no PPS funds for the Service – Significant improvement since PPS!

93 M2 Ungroupable DRG Report Currently built with regular DRGs – tma.rm.dq.dcip.ungroupable.drg MS DRG report to be added soon Includes: – MTF Identifier & Information – Date of Care – Patient Register Number (to find in CHCS) – Bed Days – Estimated Cost of Care

94 Choose Corporate Documents

95 Select: tma.rm.dq.dcip.ungroupable.drg Pick report name of interest and hit “Retrieve”

96 Report is already filled with data Updated each month when SIDR Table is updated

97 “Record ID” is the patient registry number from CHCS. Bring to coders to fix!

98 Fixing SIDRs The reasons a DRG is “ungroupable” are not always clear. Some things to look at: – Diagnosis and procedure codes may be unrelated – Information needed by the grouper may be missing or miscoded – Age and dates of service may be inconsistent. – Check the medical record for coding accuracy. – Check the date of birth, admission and discharge dates

99 M2 ad-hoc users can get details associated with problem records Limit to Tx DMISID and Record ID with ungroupable DRGs Include data elements of interest from SIDR

100 Admitted and Discharged prior to BIRTH!

101 Unlisted Provider Specialty on SADR Provider Specialty Code: – Important to understand who delivered care “Catch all” specialty codes vs real codes No specialty code = No PPS Earnings! M2 Report Name: tma.rm.dq.fy**.dcop.unspecified.provspec Code Description 001 Family Practice Physician 923 Family Practice Clinic 603 Pediatric Nurse Practitioner 520 Independent Duty Corpsman Who delivered the care when specialty is 923?

102 Improvement in Use of Specific Provider Specialty Code Power of Budget Incentives!

103 Invalid Provider IDs Provider ID is supposed to represent the person delivering care Some MTFs use “catch-all” IDs Easier to appoint, but makes it impossible to determine who did what! Report Name: tma.rm.dq.fy**.dcop.invalid.provid – Prompted filter report

104 Invalid Provider IDs Report is a list of workload by provider and MTF Sort by descending workload Are the most productive providers reasonable? – Are they real people? – You CANNOT bill for “ER DOC”……… Lost TPOCS billings. Are the daily totals reasonable? Clean out provider table to remove these IDs as options. – Discuss with clinic/appointing staff to ensure access is not harmed, though.

105 Daily Encounters by one provider at one MTF. Hundreds of daily encounters each day! Mostly physicals for AD ~7 times the RVUs of other providers at this MTF

106 PDTS Data MTF Pharmacy Data is heavily used! –Pharmacy is the #2 product line in the MHS –Data comes from Pharmacy Data Transaction Service –Weekly extract to the MDR MTFProduct NameIssue DateDays Supply QuantityPerson IDOrdering Clinic 0089Oxycodone10/01/ #1BIA 0089Nexium10/01/ #2FCC Sample Pharmacy Data from an MTF

107 PDTS Data Flow CHCS Hosts Retail Mail Order PDTS MDR M2 PDTS Web Interface Weekly Paper Claims Warehouse

108 PDTS Data Quality Issues Direct Care Pharmacy Data has some problems – Not fixable by MTF CHCS National Drug Code may not be right Will hold the proper drug, but may indicate incorrect vendor, etc CHCS Pharmacy Table: – Improper definitions of default units of measure (e.g. birth control pills; 28 pills or 1 pack?) – Pricing is wrong (rounding problems, drug code problem and unit dose problem!) – (MDR does not CHCS prices – too poor of quality)

109 Most Expensive Drug Report When improper units of measure are in CHCS pharmacy tables, data is wrong Easy to identify by looking at most and least expensive drugs and doing a reasonability test Report Name: tma.rm.dq.fy**.pdtsrx.directcare.rxcost.rep – Prompted filter report

110 Advair at $660 per script! Asthma medication is not that expensive! Problems with pre-defined units and NDC.

111 Ad-Hoc Use of M2 Robust capabilities of M2 Ad-Hoc (Full Client) Business Object Tool: – Allows ad-hoc queries – you decide the question! – Allows combination of data files – Can write one query to use as a “filter” in another – Can create new variables – Can link variables – Can bring in external data files and use with M2 data (i.e. link, filter, combine, etc) Very powerful and easy to use What follows is the use of M2 for ad-hoc analysis and identification of data issues.

112 Accuracy Problem Used SIDR Table Very bad data – 367 day stay for a routine c- section! Probably mistyped either the admission or the disposition date. Record ID is the PRN

113 Standard Inpatient Data Record LOS errors affect RWP assignment, usually. RWP is the DRG Relative Weight – Unless patient stays “too long” or “too short” – Outliers defined as length of stay outside two standard deviations from the mean. For outlier cases, RWP is adjusted based on how different actual LOS is from mean. In this case: – RWP should likely have been: 0.55 – RWP was: 98.38

114 Used Radiology Table Big Holes in the middle of FY07 (completeness)

115 Ad-Hoc Report with MEPRS data at one MTF (beware monthly data!) FYFMDispositionsBed DaysTotal Exp Available Clinician FTEs Available RN FTEs $184, $161, $190, $311, $148, $337, $119, $194, $300, $286, $344, $261, Costs less to treat patients than to not treat patients!

116 Ad-Hoc Report with MEPRS data at one MTF (beware monthly data!) FYFMDispositionsBed DaysTotal Exp Available Clinician FTEs Available RN FTEs $56, $62, $157, $64, $29, $39, $50, $102, $137, $34, $35, $89,78900

117 Ad-Hoc Report with M2 MEPRS Note how much larger rx is in Sep 07 compared with prior months

118 Ad-Hoc Report with Monthly MEPRS from MDR FYFMDispositionsBed DaysTotal Exp $5,639, ($3,010,001.83) $1,362, $1,137, $868, $991, $602, $764, $660,709.34

119 AD-Hoc Report with M2 Monthly MEPRS (Beware Across Service Lines) MEPRS Code Army MTFs AF MTFs Navy MTFs All MTFs BCA - Family Planning 3,180, ,774 BCB - Gynecology 80,121,683 81,008, ,864, ,449 BCC - Obstetrics 81,448,763 31,887, ,385 BCD - Breast Care 1,182, ,066,993 25,010 BCX - OB/GYN Cost Pool - 2,109 - Grand Total 664, , ,737 1,496,618


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