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

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

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?

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!


• 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


• 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

Composite Healthcare System (CHCS) Access

NCA

Tidewater

Pendleton

San Diego

Etc….

Co Springs

Landstuhl

No connectivity between

100+ separate systems!

Example: MTFs on Eisenhower CHCS Host

DMISID Name

0047 Eisenhower

0237 McPherson

1230 Camp Shelby

1550 TMC-4 Stockade

7197 TMC Connelly

7239 TMC Southcom

Local CHCS queries only

retrieve data for care provided at these MTFs!

Example: Inpatient Data Available at EAMC from CHCS

Proportion of Bed Days for Eisenhower Host Enrollees

39%

8%

53%

Local MTFs

Other MTFs

Purchased Care

Most of the days of care for

EAMC area enrollees are not visible in

CHCS


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

CHCS Data Products

Name Description Acronym

Standard Inpatient Data Record

Inpatient Hospital Records

SIDR

Appointment Appointment records for outpatient visits

None!

Referral Referrals for specialty care

Standard Ambulatory Data Records

Outpatient visit, t-con or inpatient rounds records

SADR

Ancillary Lab and Rad and Rx

Procedure records None!

Worldwide Workload Report

Summary workload data

WWR

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

AHLTA

CHCS/ADM

SADR

APPT

Writeback

CDR

M2

ADM & AHLTA are used to capture ambulatory data

SADR file contains ADM & AHLTA information

MDRFLOW OF SADR

CCE

Use of AHLTA for Outpatient Care

Very little usage in ER and Same Day Surgery Centers – more for office based care

% of SADRs Captured in AHLTA

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

FYFM

200502

200504

200506

200508

200510

200512

200602

200604

200606

200608

200610

200612

200702

200704

200706

200708

200710

200712

200802

200804

200806

200808

200810

200812

200902

200904

200906

200908

200910

200912

201002

201004

201006

201008

201010

201012

ER Other OP SDS IP

10% of regular visits still not captured in AHLTA

Clinical Data Mart

• 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)

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

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

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

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 1500+ users access in M2

Weekly Monthly

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

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– 1500+ users at all levels in the MHS– Ad-hoc querying or “Standard Reports”

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?”

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

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

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”?

Completeness

Oct-07 Nov-07 Dec-07 Jan-08 Feb-08 Mar-08 Apr-08 May-08 Jun-08 Jul-08 Aug-080

100

200

300

400

500

600

700

800

Monthly Discharges

Common Data Quality Items

• Why do you need complete data?

Oct-07 Nov-07 Dec-07 Jan-08 Feb-08 Mar-08 Apr-08 May-08 Jun-08 Jul-08 Aug-080

100

200

300

400

500

600

700

800

Monthly Discharges


• Why do you need complete data? FY w/error FY w/o error

7,387 7,727

340 discharge records lost!

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


• 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

Lack of Data Capture

• Some data are captured during the business process

• Often sent off automatically– Example: Appointment file

Real-TimePatient Call

Real Time Using CHCS to book appt

DailyEnd of Day Processing

Periodic standardized data feeds


• 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


• 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


• 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


• 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

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!)

Example WWR DataMTF CY/CM MEPRS

CodeBencat Count

VisitsAdm Disp Bed Days

0001 200801 BAA DA 66 0 0 0

0001 200801 BAA DR 222 0 0 0

0029 200801 AAA RET 0 90 97 339

0029 200801 AAA ACT 0 56 252 47

0029 200801 BDA DA 5286 0 0 0

0029 200801 BDA DR 542 0 0 0

B MEPRS Code (Outpatient): VisitsA MEPRS Code (Inpatient): Adm, Disp and Days


• 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”


• 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


• 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.

Some Sample Data from SIDRMTF Reg Num Pat ID Adm Date Disch Date Dx 1 DRG

0125 6470071 Pat #1 11/01/2008 11/03/2008 V3000 391

0117 6221377 Pat #2 10/16/2008 10/17/2008 49121 088

0117 6221596 Pat #2 10/21/2008 10/24/2008 2273 300

• Many more data elements available on SIDR – hundreds of them

• MTF DMISID + Register Number (PRN) is the way to identify a unique record


• 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


• 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

Picture of SIDR flowCHCS

CHCS

CHCS

CHCS

CHCS, etc

MDR

• SIDRs sent monthly from local CHCS hosts

• Assembled into one file and processed in MDR

• Sent to M2

M2

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

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

Step-by-Step

Retrieving a Standard Report

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

• 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

SIDR % Complete by Service

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

Oct-04

Dec-04

Feb-0

5

Apr-0

5

Jun-

05

Aug-0

5

Oct-05

Dec-05

Feb-0

6

Apr-0

6

Jun-

06

Aug-0

6

Oct-06

Dec-06

Feb-0

7

Apr-0

7

Jun-

07

Aug-0

7

Oct-07

Dec-07

Feb-0

8

Apr-0

8

Jun-

08

Aug-0

8

Oct-08

A

F

N

No obvious holes!


• 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.

Some Sample Data Fields from SADR

MTF Appt ID No Pat ID Appt Date Diag 1 E&M code

MEPRS Code

0117 33858389 Pat #1 10/31/2008 56400 99283 BIA

0075 7106236 Pat #2 10/09/2008 7242 99441 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


• 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


• 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)

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”

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 #

1 1

2 2

3 3

4

5 5

6 6

7 7

Final MDR Data Set

#Compliance Status Prov Patient Clinic E&M

1 Real JONR MARY BAA 99214

2 Real JONR JOE BAA 99213

3 Real JONR JANE BAA 99213

4 Inferred JONR NAN BAA N/A

5 Real JONR AL BAA 99213

6 Real JONR ROB BAA 99214

7 Real JONR SARA BAA 99499

Appt # 4 has no E&M because no SADR has been collected. This is an appointment-based record

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

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

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!

All Encounters:

N= 32 Million

“Count Only

N= 29 Million

3.5 Million Non-Count Visits worth almost 1 Million RVUs!

Count Visits

Care delivered where primary provider is a general duty nurse – FY08

MTF Svc Count Non-Count Total % Count

Army 197,324 150,701 348,025 57%

AF 92,172 243,254 335,426 27%

Navy 172,102 156,667 328,769 52%

Total 461,598 550,622 1,012,220 46%

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

Completeness of SADR Data with Appointment Benchmark

Final MDR Data Set

#Compliance Status Prov Patient Clinic E&M

1 Real JONR MARY BAA 99214

2 Real JONR JOE BAA 99213

3 Real JONR JANE BAA 99213

4 Inferred JONR NAN BAA N/A

5 Real JONR AL BAA 99213

• Combination of kept appointments and SADR makes precise measurement of missing SADRs possible.

• Perfect compliance would be 100%• No “Inferred” Records

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

Completed Outpatient Appointments with No SADRs

Missing SADRS

0

20000

40000

60000

80000

100000

120000

Oct

-98

Oct

-99

Oct

-00

Oct

-01

Oct

-02

Oct

-03

Oct

-04

Oct

-05

Oct

-06

Oct

-07

Oct

-08

A

F

N

Writeback Meltdown!

Major Improvements in Compliance

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

After entering your DMISID:

Kept Appointments with No SADR

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

Surgical Clinics, Primary Care, ER

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

• “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!

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

“Record IDs” are the appointment IENs of the missing SADRs

Use to find the missing records in ADM or AHLTA

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

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

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

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!

MTF MEPRS Code

FY/FM Avail Clin FTES

Bed Days Total Expense

Lab Expense

0024 AAAA 200901 2.89 120 295,190 4,233

0109 BAAA 200901 6.88 0 1085948 133,779

Timeliness

Timeliness


• Why do you need timely data?

Date

200801

200802

200803

200804

200805

200806

200807

200808

200809

200810

200811

200812

200901

200902

2009030

50

100

150

200

250

300

350

400

Reported Dispositions • Steady trend until recent timeframes

• Includes FY08 and part of FY09


• Why do you need timely data?

Missing data causes an artificial year to year trend

Date

200801

200802

200803

200804

200805

200806

200807

200808

200809

200810

200811

200812

200901

200902

2009030

50

100

150

200

250

300

350

400

Reported DispositionsFY Disp

2006 4,3022007 4,2512008 3,862

Annual Recap

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

Timeliness Standards

Data Type Standard/Note SIDR w/in 30 days of discharge SADR 3 days for routine; 15 for APV WWR by 10th of month MEPRS 45 days after month ends

Lab/Rad Auto send PDTS Auto send

Appointment Auto Send

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

Accuracy

Accuracy

• 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”

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

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

Coding Creep

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

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!

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

Choose Corporate Documents

Pick report name of interest and hit “Retrieve”

• Report is already filled with data

• Updated each month when SIDR Table is updated

• “Record ID” is the patient registry number from CHCS.

• Bring to coders to fix!

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

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

Admitted and Discharged prior to BIRTH!

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 Description001 Family Practice Physician923 Family Practice Clinic603 Pediatric Nurse Practitioner520 Independent Duty Corpsman

Who delivered the care when specialty is 923?

Improvement in Use of Specific Provider Specialty Code

Encounters with Unspecified Provider Specialty Code

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

Oct-04

Nov-04

Dec-04

Jan-05

Feb-05

Mar-05

Apr-05

May-05

Jun-05

Jul-05

Aug-05

Sep-05

A

F

N

Power of Budget Incentives!

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

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.

• 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

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

MTF Product Name Issue Date Days Supply

Quantity Person ID Ordering Clinic

0089 Oxycodone 10/01/2008 30 10 #1 BIA

0089 Nexium 10/01/2008 30 60 #2 FCC

Sample Pharmacy Data from an MTF

PDTS Data Flow

CHCS Hosts

Retail

Mail Order

PDTS

MDR

M2

PDTS Web Interface

Weekly

Paper Claims

Warehouse

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)

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

Advair at $660 per script!

Asthma medication is not that expensive!

Problems with pre-defined units and NDC.

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.

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

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

Radiology Records from one MTF

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

Oct-04

Dec-04

Feb-0

5

Apr-0

5

Jun-

05

Aug-0

5

Oct-05

Dec-05

Feb-0

6

Apr-0

6

Jun-

06

Aug-0

6

Oct-06

Dec-06

Feb-0

7

Apr-0

7

Jun-

07

Aug-0

7

Oct-07

Dec-07

• Used Radiology Table• Big Holes in the middle of FY07

(completeness)

Ad-Hoc Report with MEPRS data at one MTF (beware monthly data!)

FY FMDisposition

sBed Days Total Exp

Available Clinician

FTEs

Available RN FTEs

2007 1 2 4 $184,494 2 1.06

2007 2 1 1 $161,362 2 0.99

2007 3 0 0 $190,998 2 0.94

2007 4 3 12 $311,324 2 1.41

2007 5 3 3 $148,320 2 1.18

2007 6 5 11 $337,549 2 1.44

2007 7 4 6 $119,829 2 0.98

2007 8 6 9 $194,973 2 1.35

2007 9 5 12 $300,148 2 1.59

2007 10 4 7 $286,248 2 1.26

2007 11 6 13 $344,088 2 0.42

2007 12 2 3 $261,216 1.79 0.16

Costs less to treat patients than to not treat patients!

Ad-Hoc Report with MEPRS data at one MTF (beware monthly data!)

FY FMDispositio

nsBed Days Total Exp

Available Clinician

FTEs

Available RN FTEs

2007 1 10 23 $56,515 0.16 0

2007 2 13 22 $62,197 0.32 0

2007 3 10 14 $157,662 0.06 0

2007 4 9 13 $64,372 0.79 0

2007 5 8 11 $29,814 0.12 0

2007 6 10 14 $39,635 0.1 0

2007 7 13 27 $50,379 0.02 0

2007 8 17 40 $102,042 0.56 0

2007 9 15 36 $137,371 0.4 0

2007 10 8 11 $34,940 0.56 0

2007 11 12 16 $35,185 0.27 0

2007 12 16 30 $89,789 0 0

Ad-Hoc Report with M2 MEPRS

Rx Expense and Total Expense for Ambulatory Clinics in FY07

$0.00

$200,000.00

$400,000.00

$600,000.00

$800,000.00

$1,000,000.00

$1,200,000.00

Oct

-06

Nov

-06

Dec

-06

Jan-

07

Feb

-07

Mar

-07

Apr

-07

May

-07

Jun-

07

Jul-0

7

Aug

-07

Sep

-07

Rx Exp

Total Exp

Note how much larger rx is in Sep 07 compared with prior months

Ad-Hoc Report with Monthly MEPRS from MDR

FY FM Dispositions Bed Days Total Exp

2007 1 45 200 $5,639,371.42

2007 2 40 188 ($3,010,001.83)

2007 3 44 224 $1,362,895.50

2007 4 55 374 $1,137,152.31

2007 5 51 318 $868,267.19

2007 6 66 321 $991,846.96

2007 7 40 145 $602,137.16

2007 8 44 151 $764,113.54

2007 9 31 144 $660,709.34

AD-Hoc Report with M2 Monthly MEPRS (Beware Across Service Lines)

MEPRS Code Army MTFs AF MTFs Navy MTFs All MTFs

BCA - Family Planning

3,180,304

145

12,774

BCB - Gynecology

80,121,683

81,008,784

123,864,534

926,449

BCC - Obstetrics

81,448,763

31,887,059

532,385

BCD - Breast Care

1,182,718

381

7,066,993

25,010

BCX - OB/GYN Cost Pool

-

2,109

-

Grand Total

664,253

358,628

473,737

1,496,618