mhs data sources – techniques for analysis
DESCRIPTION
MHS Data Sources – Techniques for Analysis. 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 - PowerPoint PPT PresentationTRANSCRIPT
MHS Data Sources –Techniques for Analysis
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!
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
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
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
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
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% of SADRs Captured Using AHLTA
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
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
Oct
-07
Nov
-07
Dec
-07
Jan-
08
Feb
-08
Mar
-08
Apr
-08
May
-08
Jun-
08
Jul-0
8
Aug
-08
Sep
-08
Oct
-08
Nov
-08
Dec
-08
APV
ER
Other OP
Very little usage in ER and Same Day Surgery Centers – more for office based care
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
Common Data Quality Items
• Why do you need complete data?
Common Data Quality Items
• 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
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
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
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
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
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
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
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
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”
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
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.
Some Sample Data from SIDR
MTF 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
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
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
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!
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.
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
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
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)
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
Common Data Quality Items
• Why do you need timely data?
•Steady trend until recent timeframes•Includes FY08 and part of FY09
Common Data Quality Items
• Why do you need timely data?
Missing data causes an artificial year to year trend
FY Disp2006 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