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Data Quality in the MHS Tips and Tricks. Objectives. Describe MTF data collection systems. Identify data feeds from MTFs into corporate information systems. Describe the MHS Data Repository and M2. List corporate reports in M2 that can be used to assist in managing MTF data quality.

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Data Quality in the MHSTips and Tricks


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Objectives

  • Describe MTF data collection systems.

  • Identify data feeds from MTFs into corporate information systems.

  • Describe the MHS Data Repository and M2.

  • List corporate reports in M2 that can be used to assist in managing MTF data quality.

  • Characterize the state of MHS data with respect to data quality.

  • Describe how M2 can be used for ad-hoc data quality analysis.


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Major Data Collection Systems

Like most major organizations, the MHS uses “operational systems” to assist with day to day activities

Real-time systems that automate activities necessary to operate a business.

Data is often collected as a by-product of “getting business done”.


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Example of an Automated Business Process

Order is sent to pharmacy automatically

MD sees patient

MD orders Rx in operational system

Drug Utilization Review Query sent automatically

Child’s all better and back to school!

Rx is filled


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In this example, data would be captured and retained when the doctor orders the prescription in the computer

The computer automatically knows where to send the data next (“trigger architecture”)

The data entry person here is a physician – no “cube farms” with people entering data all day

The by-product of the business process is that the health system knows who got what drugs, when, etc…

Data collected this way is called “operational data”

Example of an Automated Business Process


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Major Operational Systems in the MHS

CHCS

Primary system used at MTFs.

Automates many functions for the MTF.

Administrative functions such as registration, appointing, billing, etc..

Clinical functions such as order entry, results retrieval, etc…

Is not an Electronic Health Record, but is the only system at MTFs that keeps track of all direct health care delivery provided by the MTF.

There are more than 100 separate CHCS databases.


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Composite Healthcare System (CHCS)

NCA

San Diego

Co Springs

Tidewater

No connectivity between

100+ separate systems!

Etc….

Landstuhl

Pendleton


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AHLTA

  • AHLTA is another operational system.

  • Electronic medical record system used to document outpatient care.

    • Providers use AHLTA for clinical note-taking for about 80% of outpatient encounters.

    • AHLTA is not generally used in ERs or APVs.

    • When AHLTA is used, clinical data are captured electronically, such as vital signs, height and weight, etc..

  • AHLTA receives some information from CHCS also

    • Laboratory, Radiology, Pharmacy, etc.

  • AHLTA data are stored centrally in the “Clinical Data Repository”.


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Completeness of Data in AHLTA


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Other Important Operational Systems

  • Defense Medical Human Resources System (DMHRS)

    • New tri-Service personnel system used by MTFs.

    • Used to record labor hours.

    • Feeds into tracking of “productivity’.

    • Feeds into cost allocation process in MEPRS.

    • Will be discussed tomorrow in the DQ Course.


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Other Important Operational Systems

  • Pharmacy Data Transaction Service

    • Real-time drug utilization review system.

    • When MHS beneficiaries receive outpatient prescriptions, an online check is done.

    • Pharmacy can only fill prescription if PDTS responds back with “advice” indicating it is safe.

    • Applies to all points of care (direct + retail) worldwide, except overseas purchased care.

      • That is, Landstuhl Army Hospital does use PDTS, but the off-base civilian apothekarie would not!


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Other Important Operational Systems

  • DEERS

    • Primary system with information about MHS eligible beneficiaries and enrollment in TRICARE Programs.

    • Not operated by the MHS; rather, is a personnel system.

    • Beneficiaries correspond directly with DEERS offices throughout the world to update information about their status.

    • DEERS also communicates with many other federal organizations, such as the Military Services, Medicare and Social Security


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Other Important Operational Systems

  • DEERS

    • Another important source of information to DEERS is the DEERS Online Enrollment System

    • Enrollment Management System used in TRICARE Service Centers

    • Enrollment, Disenrollment, Updates of Contact Information, PCM Assignment, “Other Health Insurance” Information Updates

  • DEERS directly updates CHCS whenever a CHCS user requests an eligibility inquiry

    • And not otherwise, generally.

    • Results in inaccurate CHCS person data for some members.


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Benefits of Operational Systems

  • Operational Systems have some key benefits:

    • Real time, since these systems are the point of original entry.

    • Only type of system where real time data are available.

    • Closest to the point of capture; means that focusing on data quality in source systems can save time and money in reprocessing…

    • And makes data more usable locally.


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Drawbacks of Operational Systems

  • The main drawback of an operational system is that data problems cannot (are not) generally fixed.

  • Numerous errors originate at the source.

  • Some input errors simply can only be fixed locally or with patient assistance.


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Quality in Operational Systems

  • Some input errors (and other issues) can be fixed.

    • Usually not done in operational systems, though, because they are too important to an organization to shut down.

    • (Person identification and AHLTA/CDR/CDM is a good example).

  • Instead, data from operational systems are typically exported to other systems (warehouses) for further processing.

  • This processing can be critically important to using data.


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Data Warehouses

  • There are two types of warehouses:

  • Dedicated Warehouses

    • Usually receive data from one operational system only, though not always the case.

    • Can be thought of as a storage silo.

    • Data are not generally processed, so that all the quality weaknesses in the source system are present in the warehouse.

  • Many of our operational systems have dedicated warehouses:

    • Clinical Data Repository, PDTS Warehouse, Purchased Care Data Warehouse…


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MHS Data Repository

  • Comprehensive Data Warehouses:

    • Take in data from multiple systems

    • Data are usually processed to standardize and enhance data quality.

  • The MHS has one comprehensive data warehouse; the MHS Data Repository (MDR).

  • MDR is the most popular system you never heard of!

    • MDR data are used as a source of data for many commonly used applications, such as:

      • M2

      • Population Health Portal / Care Point

      • MHS Insight, etc…

  • Documented on http://www.tricare.mil/ocfo/bea/functional_specs.cfm

    • Contains an easy to use data dictionary


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MHS Data Repository

  • MDR is immensely flexible

    • Can upload and download data

    • Sophisticated programming tools

    • Can access Clinical Data Mart (CDM) data through MDR front end

  • Very difficult to use, however

    • No point, click, drag, drop

    • The mouse barely even works!

    • Must be a skilled programmer to use

  • Most users touch MDR data in “M2”


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Basic Data Flow

Data sent to MDR 24/7

MDR File Storage & Limited Access

TED

MDR Feed Node

Batches

CHCS/AHLTA

Weekly Monthly

DEERS

PDTS

Data Marts

User Access in Data Marts

Others


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Some selected MDR Processing Enhancements

Person Identification Enhancement:

“DEERS Person Identifier” is an element in the MDR that identifies each beneficiary.

Some records come in with only partial or incorrect person identifying information, though.

Example: Newborns have a sponsor identifier, but no person ID.

MDR has a Master Person Index file that is used to add missing information to every record.

Allows for consistent identification of patients, regardless of source in the MDR.


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Question: How many well visits did person “111111111” have?

As received

After MDR Processing


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MDR Enhancements

Application of DEERS attributes to each data record

After correcting person ID issues, the MDR then standardizes demographic and enrollment information.

Avoids ‘apples and oranges’.

Needed because person demographics are not always accurate on source data or can be missing entirely.

Also, some systems only contain current demographics, while “contemporaneous” data are usually needed.

Beneficiary Category, Enrollment Program, Primary Care Manager, etc.

Allows for retroactive changes, also.


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Example: How many well visits did enrollees of Fort Belvoir have?

After MDR Processing

As received

Newborn was retroactively enrolled in DEERS to Fort Belvoir.


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How many PDHAs?

As received. Likely indicates patient’s current status, when query was run.

After MDR Processing

* Retirement date from DEERS.


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Another very important application of the MDR is to add Weighted Workload Values to direct care encounter records.

Relative Weight Products (RWPS), Relative Value Units (RVUs) and APC Weights are extremely important in the MHS.

Serve as the basis for budgeting for most inpatient and ambulatory healthcare in MTFs.

MTFs do not code RWPs or RVUs.

RWPs and RVUs are added to MDR records based on information that is coded on SIDRs and SADR/CAPERs.

The logic for adding these data elements is published on the MDR website.

The RWPs and RVUs in MDR/M2 are the ones that are used for major HA/TMA initiatives, such as PPS or Business Planning.

Will discuss derivation rules later.

MDR Enhancements


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Another very important application of the MDR is to add cost data to direct care encounter records.

Full and Variable cost estimates are added to each record.

These elements are used routinely by MTFs for financial decision-making.

The algorithms for creating these variables involve the combination of SIDR/SADR/CAPER data with MEPRS cost information.

MTFs who do not record workload and labor in the same location as costs may end up having their cost data in MDR/M2 impacted.

Will show an example of this later.

MDR Enhancements


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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.

    • More than 1000 users.

    • Ad-hoc Querying or “Standard Reports”.

    • M2 is currently transitioning to a new software version (the Desk-I version is recommended).


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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?”


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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 can be obtained from DeskI.


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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


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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”?


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Completeness


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Common Data Quality Items

  • Why do you need complete data?


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Common Data Quality Items

  • Why do you need complete data?

340 discharge records lost!


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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


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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


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Lack of Data Capture

  • Some data are captured during the business process

  • Often sent off automatically

    • Example: Appointment file

Daily

End of Day Processing

Periodic standardized data feeds

Real-Time

Patient Call

Real Time Using CHCS to book appt


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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


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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 (CAPER) - Completed outpatient visits and inpatient rounds and case management acuity assessments.


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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


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Example of Rhinoplasty

  • No procedure coded on SADR

  • Separate pre-op and follow up visit coded

Direct Care Coding


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Example of Rhinoplasty

  • Private Sector Coding

  • Procedure is coded in both inst and non-inst records

  • No pre-op or follow up visit (bundling)


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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


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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!)


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Example WWR Data

B MEPRS Code (Outpatient): Visits

A MEPRS Code (Inpatient): Adm, Disp and Days


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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”


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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

PASBA

AFMSSA

NMIC


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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.


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Some Sample Data from SIDR

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

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


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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


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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


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Picture of SIDR flow

  • SIDRs sent monthly from local CHCS hosts

  • Assembled into one file and processed in MDR

  • Sent to M2

CHCS

MDR

CHCS

CHCS

M2

CHCS, etc


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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


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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


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Step-by-Step

Retrieving a Standard Report


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  • Select the report you want and click retrieve!

  • Use report guide in DeskI


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  • 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


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No obvious holes!


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Capture Requirements

  • Standard Ambulatory Data Record (renamed Comprehensive Ambulatory Provider Encounter 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.


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Some Sample Data Fields from SADR

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

  • MTF DMISID + Appt ID Number (IEN) is the way to identify a unique record


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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


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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)


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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”


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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’.


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


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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


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Completeness of SADR Data

  • Two common ways to measure

    • Official way is to compare WWR to SADRs deemed “countable”.

    • Method developed when appointment data was unavailable

    • Not always a precise match since ‘count’ status can change from the time of appointment to the time of care delivery.

  • 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, however.

  • Non-count visits DO earn RVUs

    • SADRs are expected for both count and non-count visits!


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All Encounters:

N= 32 Million

“Count Only

N= 29 Million

3.5 MillionNon-Count Visits worth almost 1Million RVUs!


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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 greater than 100%

  • Can easily be reviewed using M2 Corporate Document

    • tma.rm.dq.rep.comp.wwr.rep


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Completeness of SADR Data with Appointment Benchmark

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

  • Perfect compliance would be 100%

  • No “Inferred” Records


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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 that ‘count’ (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


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Completed Outpatient Appointments with No SADRs

Writeback Meltdown!

Major Improvements in Compliance


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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 RVUs 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

    • tma.rm.dq.dcop.rep.comp.actionrep


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After entering your DMISID:

Kept Appointments with No SADR


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


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Surgical Clinics, Primary Care, ER


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


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  • “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!


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


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

Use to find the missing records in ADM or AHLTA


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Case Management SADRs

  • The MHS must report to Congress the:

    • Number of case managers, and

    • The patient’s acuity, and

    • The number of patients in case management.

  • Tri-Service agreement was obtained for the methodology for reporting of CM.

    • Describes in the UBU Coding Guidelines

  • Tri-Service agreement was reached that MEPRS B codes would not be used for CM.

    • FAZ2, ELAN, and ELA2 are the only approved codes for CM.

    • tma.rm.dq.casemgmt.child.rep & tma.rm.dq.casemgmt.parent.rep


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Case Management SADRs

  • The chart below shows the impact of case management SADRs being recorded in “B” Codes in PPS.

  • While the MTFs that use B codes for CM will receive excessive funding under PPS, there is an obvious downside:

    • These sites will be given no credit for doing CM in a required report to Congress on CM services and Wounded Warriors.


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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


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MEPRS DataFlow

EAS IV Repository

(Full MEPRS dataset)

Workload

(CHCS)

MDR(Large MEPRS dataset)

Financial Data(STANFINS,STARS/FL,BASF)

EAS-Internet

(Monthly Processing)

Personnel Data(DMHRS,UCAPERS,SPMS, EAS)

(Nightly/Monthly Processing)

Monthly MEPRS data due 45 days after month end

M2(Smaller MEPRS dataset)


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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!

  • There are frequent timeliness problems with MEPRS data.


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Timeliness

Timeliness


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Common Data Quality Items

  • Why do you need timely data?

  • Steady trend until recent timeframes

  • Includes FY08 and part of FY09


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Common Data Quality Items

  • Why do you need timely data?

Annual Recap

Missing data causes an artificial year to year trend


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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


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Timeliness Standards


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Timeliness

  • Timeliness Standards are best monitored locally

    • CHCS, ADM and AHLTA speakers to present in this course.

    • Ask them about local tools for managing timeliness!

  • Batch processing in MDR/M2 makes it an insufficient tool for monitoring timeliness

  • Very useful for completeness, though


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Accuracy


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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”


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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


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Direct Care Accuracy

  • Direct Care SIDR and SADR:

    • MHS uses policies for completeness and timeliness.

    • Coding and Compliance Editor (CCE) for code edits but is rarely used.


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Direct Care Accuracy

  • SIDRs are typically coded by registered coders and are of higher quality that SADRs.

  • SADRs have some significant issues with quality.

    • Compliance

    • Choice of CPT codes

    • Use of Units of Service

  • Coding audits are required at MTFs but sample sizes are too small.


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Coding Creep in the MHS

Average E&M Code Intensity

MHS Worldwide Average (non ERs), October 2005 through January 2011

100


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Coding Creep in the MHS

Average E&M Code Intensity in Emergency Rooms

MHS Worldwide Average (ERs), FY2006 through FY2010

101


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Service-Wide Impact of Incorporating Unit of Service Limits in RVU Calculations


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CPT Impacts of Unit of Service Limits

  • Some selected extreme examples from SADRs

  • Each SADR represents care provided to one patient on one day.

  • The first three SADRs indicate that there were 80 patients were given more than 900 vaccinations at one visit!

  • The last SADR shows 159 encounters where the patients had up to 52 days of psych counseling in one day!


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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 MS-DRGs

    • Unlisted Provider Specialty Code

    • Potential Pharmacy Table Errors

    • Potential Provider ID Errors

    • Improperly Coded Case Mgmt records

  • Ad-hoc possibilities are limitless


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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 MS-DRG:

    • Rules are not followed in some way; or

    • Diagnosis and Procedures simply don’t make sense together

  • Ungroupable MS-DRGs receive no PPS funds for the Service

    • Significant improvement since PPS!


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M2 Ungroupable DRG Report

  • Report name:

    • tma.rm.dq.dcip.ungroupable.drg

  • Includes:

    • MTF Identifier & Information

    • Date of Care

    • MS-DRG, Description, MDC

    • Patient Register Number (to find in CHCS)

    • Bed Days

    • Estimated Cost of Care


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Choose Corporate Documents


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Select:

tma.rm.dq.dcip.ungroupable.drg

Pick report name of interest and hit “Retrieve”


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  • Report is already filled with data

  • Updated each month when SIDR Table is updated


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

  • Bring to coders to fix!


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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


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  • M2 ad-hoc users can get details associated with problem records

  • Limit to Tx DMISID and Record ID with ungroupable DRGs

  • Must include MTF and Record ID to get unique list of records

Include data elements of interest from SIDR


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Admitted and Discharged prior to BIRTH!


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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.dcop.unspecified.provspec

Who delivered the care when specialty is 923?


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Improvement in Use of Specific Provider Specialty Code

Power of Budget Incentives!


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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.dcop.invalid.provid

    • Prompted filter report


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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.


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  • 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


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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

Sample Pharmacy Data from an MTF


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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)


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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.pdtsrx.directcare.rxcost.rep

    • Prompted filter report


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Advair at $660 per script!

Asthma medication is not that expensive!

Problems with pre-defined units and NDC.


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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.


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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


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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


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Full Time Equivalents – StaffingFTE Reporting Problem Example


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Full Time Equivalents – StaffingFTE Reporting Problem Example


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Rx Percent of Ambulatory Expenses


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