Business Needs Section 10

State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015

Solution Alignment With BMS’ Business Needs Section 10.0 Page 112

Solution Alignment with BMS’

Business Needs

Section 10.0 RFP reference: 4.1.10, Solution Alignment with BMS’ Business Needs, Page 51

Vendor should describe in detail how the solution proposed provides the functionality identified in this RFP as necessary to meet BMS’ current business needs and the work of the work units listed. Vendor should also describe how the proposed solution provides the foundation that enables BMS to move toward its vision for its future MITA-oriented Medicaid Enterprise. Vendor should demonstrate in its proposal how the solution provides BMS the ability to perform more sophisticated analyses to make better decisions, improve health outcomes, and make best use of state and federal financial resources through financial analysis; defined and ad hoc reporting capabilities; clinical utilization and care management case analysis; and analytics such as trending and what-if scenarios. The vendor can include additional materials, in a separately labeled section at the back of the proposal, which describes company offerings that should be of value to BMS, but this section would not be reviewed as a formal section of the RFP. The vendor should complete the checklist columns of Appendix 2 – Detailed Business and Technical Requirements, Section A.

Deloitte’s goal is to exceed the Bureau for Medical Services’ (BMS’)

expectations for the implementation of a West Virginia (WV) Medicaid

Data Warehouse/Decision Support System (DW/DSS). Our Design,

Development, and Implementation (DDI) and Operate and Enhance

Phase approach will emphasize the processes involved in classic

project management methodologies. By focusing on the processes

involved, we will confirm that a systematic approach is defined and

followed. This approach includes adhering to a very detailed project

plan, establishing the organizational structure, and implementing our

documented processes that are tried and true from past experience.

Deloitte is committed to the delivery of exceptional service for the BMS’

DW/DSS design, development, implementation, operation and

enhancements.

The DW/DSS project represents a significant initiative for BMS as it

supports the strategic vision for the West Virginia Medicaid program

and it also attains the goals identified during the Medicaid Information

Technology Architecture (MITA) State Self-Assessment (SS-A). Our

approach will assist BMS with its migration towards a MITA-oriented enterprise.

Deloitte is committed to MITA because it believes that the age of legacy IT systems and unitary data

warehousing has passed. SOA architecture supports the seamless integration of vendor packages to support

many aspects of performance monitoring and outcomes management, and other powerful data analytical

tools. Deloitte will make use of these tools and believes that the flexibility afforded by MITA is an important

step in the right direction. Our solution approach will address how we plan to support BMS’ Business Areas.

Team Deloitte

Differentiators

Prior relevant, successful

experience with public sector

data warehousing projects

40-year track record

successfully delivering Health

Sciences and Government

consulting projects

Ranked the “#1 Health Care

and Life Sciences

consultancy” by Kennedy

Information Services

Gartner identified Deloitte to

be a leader provider of

business intelligence and

performance management



Figure 10-1.

While the DW/DSS is intended to support the information and analytical needs of several BMS work units,

i.e., MITA business areas, our solution is flexible enough to support additional business areas as well as

other agencies and business partners outside of BMS and DHHR moving forward. Our understanding of

BMS’ business requirements, coupled with our approach to solution design and delivery, supports both the

immediate and long-term requirements identified by BMS.

Deloitte’s solution approach will align with the MITA Business Process Areas, enabling BMS to support the

MITA Business Model. BMS has initially identified goals in four MITA business areas that can be either fully

or partially met through the DW/DSS implementation.

Operations Management

− Reduce the potential for redundancy in services and payments.

− Improve access to information.

− Enhance and automate reporting capabilities to measure compliance with operational performance

measures.

− Improve operational efficiency and reduce costs in the healthcare system.

Program Management

− Enhance decision and policy-making capabilities through data analysis.

− Enhance the ability to analyze the effectiveness of potential and existing benefits and policies through

the integration of claims data with clinical data.

WV_DW_DSS-063

BMS Work Units MITA Business Areas

Finance Program/Operations Management

Program Management


Program Integrity Management

Program Management


Program Management

Pharmacy

MMIS Operations

Office of Quality and Program Integrity (OQPI)

Program Policy

Medicaid Fraud Control Unit (MFCU)

Technology and Reporting



Care Management

− Improve healthcare outcomes for members.

− Establish access to data from other programs, agencies or entities.


− Use decision support capability to support SUR activities.

− Improve data access, data accuracy, and the accuracy of process results, while reducing the effort

required to achieve these results.

DW/DSS Solution

Deloitte has extensive experience working with state Medicaid agencies that have taken the initiative to

implement a data warehouse and decision support systems. Based on our experience, we can easily predict

the issues and recognize the frustrations that an agency will likely encounter as it attempts to implement the

current tools and systems. We understand both the operational efforts that business areas go through to

operationalize the new system tools and the difficulties end users have in using these tools to both gain

insight and identify actionable information.

Over time these current vendors have either pieced together siloed tools, or developed custom

subcomponents tacked on to standard COTS tools, in order to deploy a system that addresses an agency’s

base Medicaid decision support system requirement. These approaches make the system deployment much

more complicated, which in turn creates both greater risk and more potential areas for failure.

Deloitte’s approach is to have a single complete and deployable solution, not merely a system but rather a

true solution, that will leverage our years of experience in enabling operations with technology systems and

tools. Our solution will build upon our experience gained over the years when we assisted Medicaid

agencies’ deployment of data warehouses and decision support systems. We know the issues, limitations,

and complications of other tools in the market as we have developed business processes and trained

resources to incorporate these as best as possible into day-to-day operations. In developing our proposed

solution, we have leveraged the full spectrum of Deloitte’s knowledge and experience.

Through our experience, Deloitte has designed an innovative, leading edge Data Warehouse and Decision

Support Solution, one that not only focuses on business intelligence but rather one that combines business

and clinical intelligence (BCI). We believe that Medicaid agencies need to change how they analyze and use

the wealth of historical data to enable their operations. They need to transform from the current standard

retrospective cost-based management approach to one that is prospective and addresses the cause of the

high costs, specifically the health status and the quality of care delivery, focusing on population

management.

Our approach will feed the analytical requirements across a Medicaid agency’s business areas and will open

the lines of communication, as every operational and analytic report, trend analyses, case identification, etc.

will draw upon a common foundation of data, a central repository of historical and current actionable

information. This data warehouse will aggregate all data into a single warehouse to include, but not be

limited to, member eligibility, medical claims, encounters, pharmacy, lab values, provider details, risk

assessments, and care management programs. The data warehouse will be the only central repository and



will be developed using open architectural standards and not a proprietary closed system. These open

standards will support the increases in data volume, the expansion of data elements, and these will also

allow easy access using standard commercial-off-the-shelf tools, all supporting the MITA architecture.

Figure 10-2. Proposed Solution Diagram.

Deloitte’s proposed solution diagram above illustrates how our solution addresses each core business area.

Below we will show areas in the Solution Diagram that directly address the business requirements identified

in the RFP and how they are broken out by business area. The Solution Map thus enables us to provide a

more detailed discussion of plans for addressing each business requirement and to document which parts of

the solution will effectuate those plans. The Solution Map contains five core areas in the application:

1. Data Extraction. Data Extraction is the area of the DW/DSS solution which contains the extract

requirements and ETL mappings to load the data from either flat files or through direct interface into the

Lab Vendor

MCOs

MMIS

WV_DW_DSS-001_4

BMS DSS Web-Portal

RAPIDS

Medical Claims

Pharmacy

Eligibility

Encounters

Provider

Lab Results

Medical

ClaimsEncounters

Member

Eligibility

Lab

ResultsPharmacy

Processed

Analytics

Provider

Care

Management

Data

Processing

Business

Analytics

Clinical Grouper

(CRGs)

Clinical

Analytics

Data

Cleansing

&

Validation

Quality

Assurance

and Data

Integrity

Report

Consolidated Cleansed Data

Raw Data from Extracts

Data

Warehouse


Utilization Financial

Care Management

Materialized

Views and

Data Cubes

Population Utilization

Member Quality

Outcomes

Program Management

Program Integrity

Management

Utilization (Services

Rendered)

Financial (Retrospective)

Outcomes Financial (Prospective)

Pharmacy Rate Setting

Provider Vendor

Fraud &

Abuse

Surveillance

Utilization

Personal Health Record

(PHR)

Ad-hoc Reports

Eligibility

Reference

External Data

Dashboard



source data systems. This area will focus on the loading of the raw data elements from a landing area into

load tables. Prior to populating the load tables, initial validation is performed to confirm that the data received

meets the basic conditional requirements, i.e., field size, element constraints, character types, element

delimitation, etc.

2. Data Cleansing. Data Cleansing is the area that will take the data post extraction, from the load tables,

and will process it through a series of data cleansing and detailed validation mappings. This process will

assess the data elements for validity, completeness, and reasonableness. Claim adjustments are carefully

netted against the original claims to produce accurate ―net payment‖ reports. This process is complete once

a validation process verifies the output files are consistent with inputs. Errors or issues with the data from this

process will be reported.

3. Data Storage. Data Storage is the area of the DW/DSS which physically contains the database, data

tables, reference tables, views, dimensions, and other raw data that make up the DW. This data will be

acquired from the different sources. This area will directly support the data processing and analytical areas of

the DW/DSS solution. The data storage will contain finalized MMIS claims data that will be reconciled to

payment and clinical data as well as eligibility data, provider data, MCO encounter data, reference data, and

lab results data.

4. Data Processing. Data Processing is the area of our solution that begins applying the business and

clinical intelligence. The cleansed data stored in the DW will be drawn upon and run through a series of

processes that will break down the data into actionable segments where we can act upon the details to be

processed through our clinical grouper, run through our business and clinical analytics, and formulated to

details and then loaded into the appropriate DW tables. This process creates the data elements that form the

basis of our information delivery.

5. Information Delivery. Information Delivery is the BMS DSS Information Portal (i-Portal). This information

portal is the mechanism with which users interact to both present and disseminate information. The i-Portal is

a single application, COGNOS, along with the SPSS analytical and predictive modeling module, which gives

business users a one stop analytical tool to analyze the Medicaid data. This portal will have the access

security that will assign authorization entitlements to users, only allowing them to see the areas they are

authorized to see, and it will also track user navigation. i-Portal will be broken out by business area and will

provide users access to defined reports and ad hoc analyses.

Deloitte’s Solution will provide both significant operational improvements and demonstrable savings. By

strengthening the architecture and data access components of the DW/DSS, BMS will realize improvements

in five (5) critical operations areas:

1. Scalability. The DW/DSS can grow in size to accommodate future program growth and potentially

voluminous new files from external data sources as envisioned in the RFP.

2. Extensibility. New subject areas can be added as Medicaid Reform and other program initiatives require

new data sets and analyses.

3. Flexibility. The DW/DSS can be quickly modified to meet changing business needs and can support a

variety of needs from simple queries to complex statistical analyses and major multi-user program planning

exercises.



4. Interoperability. The DW/DSS can freely receive and exchange data with other systems and assure that

BMS can support expansion and will be a key player in the evolution of a National Health Information System

(NHIS).

5. Usability. All users will be able to access the DW/DSS, query its’ data, and receive information back

quickly without reliance on specialized technical assistance.

Solution Approach

Deloitte’s transformed BCI-DSS solution is an innovative, integrated secure Web-based decision support

system solution that directly draws upon the data stored in a data warehouse. It aligns both business and

clinical intelligence, and integrates data informatics, operational support, and outcomes management into

day-to-day operations. With these combined capabilities, BCI-DSS enables the operational spectrum by

drawing upon a common foundation of data to be proactive and to share decision support information

between Medicaid business areas, as well as their MCOs and providers.

Deloitte’s BCI-DSS solution will improve BMS’ ability to manage its population by providing a detailed

understanding of its health status and the progression of diseases, the quality of care and delivery of

services, resource consumption and service utilization. This understanding will identify impact areas to

develop case and operational interventions while managing them to outcomes.

BCI-DSS processes the raw cleansed data into a clinically-based stratification model that provides a solid

foundation to identify both outliers and impact areas based on the clinical component of an individual’s health

status. BCI-DSS’s analyses will easily identify outliers due to the focus on the quality aspects of care and

care delivery at the member and provider service levels, introducing the cost component prospectively. BCI-

DSS utilizes the quality indicators and disease burden as the primary drivers of outlier identification,

providing the methodology not only to identify outliers but also the means to track interventional outcomes

and cost savings.

Our solution enables program planning, policy analysis, evaluation, and performance monitoring. This is

accomplished with the use of cohort studies that monitor the impact of various programs and policies on the

changes in the disease burden and other parameters. Quality of care and outcomes assessment are easily

measured through the various quality parameters captured, including ambulatory sensitive admissions,

disease complications, gaps in care (including immunizations, various disease centered services like foot

care, eye care for diabetics) and other parameters developed from AHRQ and HEDIS recommendations.

Most HEDIS measures are part of the quality component of BCI-DSS and these can be modified by request

to accommodate any program specific performance measures.

BCI-DSS combines industry experience and advanced analytics to create an integrated delivery solution that

can support a wide range of business area operations to include:

Operations Management Population Analysis

Utilization Analysis

Financial Analysis



Program Management Utilization Analysis

Financial Analysis

Pharmacy Analysis

Provider Analysis

Vendor Management

MCO Management

Outcomes Tracking

Rate Setting

Care Management Population Analysis

Member Analysis


Quality Analysis

Outcomes Tracking

Program Integrity Management Surveillance and Utilization

Quality Outliers (Fraud & Abuse)

Deloitte understands the phased implementation that BMS has defined in the RFP. We believe that the

approach BMS has outlined is necessary with such a large initiative. Throughout our experience we have

seen many aggressive implementation timelines fail. The phased implementation and enhancement of a

DW/DSS will assist BMS to achieve its goals and to move toward its MITA oriented Medicaid enterprise.

Our solution is designed to support growth and future enhancements of the DW/DSS to easily include the

ability to add data from additional State agencies and potentially enable data access for additional State and

external entities. Our solution is streamlined, will be implemented on all open standards, will have no

proprietary components, and will be developed using a single user application interface, COGNOS along

with their SPSS Module.

Deloitte’s solution will improve information delivery and data access through the use of the proposed BCI-

DSS i-Portal approach. i-Portal and the underlying analytics will enhance BMS’ reporting capabilities and will

provide intelligent information to the end-users to make day-to-day decisions. Our solution is designed for all

levels of resources and is not designed for only the technical resources to run queries. Our goal in designing

our solution is to deliver information to all of the users.

Through the detailed analyses described below, BMS will have the ability to link financials to outcomes and

easily establish pay-for-performance (P4P) initiatives as well as monitor performances of various programs

against contracted service level agreements (SLAs). Performance monitoring will be enhanced through the

integration of detailed clinical data, i.e., lab results and pharmacy data.

We are focused on delivering a true solution rather than just another product or system. We will provide the

right resources to train and provide the experienced guidance on how to integrate i-Portal and the BCI-DSS

analytics into the business and operational areas. We will provide the clinical and analytic experienced

resources to help interpret the information and train your resources to set interventions and to manage them

to outcomes.



Underlying Clinical Data Stratification

Deloitte’s BCI-DSS solution provides a layer of analytical capabilities

that other vendors cannot provide. We not only provide a world class

data architecture to streamline the analytical process, but we also

provide a detailed clinical stratification that will truly provide the means

to combine the business and clinical aspects of the captured data. Our

design centers on the members’ data from which everything else is

derived, i.e., provider performance is dependent on how care is

delivered to their patients, financial impacts are dependent on the

resource consumption of services to members, etc.

Our solution leverages over 26 years of clinical, financial and

administrative expertise of 3M™ Health Information Systems (HIS)

and their world class classification and grouping solutions, Clinical

Risk Grouping software (CRGs). The CRGs grouping software

provides the underlying clinical categorization of the individual

members by disease or combination of diseases (co-morbidities) as

well as the state of their progression (i.e., the severity of the illness).

3M’s Clinical Risk Grouping Software

We chose to use 3M’s CRGs over other market grouping software

because of 3M’s advanced design that follows the clinical based

categorical model with progression determination (i.e., severity

adjustment), a feature that is not found in any other grouping software.

Other grouping software in the market supports the cost based

statistical model that has been used in the industry for twenty plus

years.

Using the CRGs as the clinical stratification model, all individuals with

the same disease(s) are not categorized in the same bucket, as is the

case with other groupers. With CRGs, for example, you can easily

identify all individuals that have a single disease of Diabetes (CRGs

status 5), but the group of individuals will be adjusted based on the

progression of their individual disease and would be distributed across

4 different severity levels. This status and severity adjustment is the

individual’s health status. This will allow BMS to track and trend the

progression of an individual’s health status and over time will track

how the severity level increases as individuals move into a higher

status.

Unique features of 3MTM

Clinical Risk Grouping

Software:

• Statistical performance

superior to any other

available risk adjustment

system

• Complete specifications

of clinical logic provided

with software- no black

box

• Explicit severity of

illness levels for all

chronic illnesses

• Ability to obtain detailed

breakdowns of the types

and amounts of services

provided for clinically

comparable individuals

• Based on standard claims

data

• Can be used with varying

degrees of data

completeness

• Can be used both

prospectively and

retrospectively

• Give you the ability to

analyze all or part of your

resource expenditures

• Give you the ability to

compare results to

external populations

Unique andDistinguishingFactors



Figure 10-3. Individual Health Status Progression.

The above graph demonstrates the ability to trend the health status progression of an individual, which can

also be accomplished for various populations, which is discussed below under Population Management. The

graph shows an individual with Diabetes assigned to a Status 5 - Severity 1 during the Analysis Period (AP)

1/2007. In the next 12 month period, AP 1/2008, the individual progressed two severity levels to a Status 5 –

Severity 3. During the AP-6/2008, the same individual would be grouped to another group, Status 6 –

Severity 2 as he/she had an additional chronic diagnosis of Congestive Heart Failure (CHF). As time goes

on, the graph shows that this individual again progressed to another group, Status 7 – Severity 4, as this

member now had another chronic diagnosis of Chronic Obstructive Pulmonary Disease (COPD).

CRGs Foundation for BCI-DSS Solution

BCI-DSS will enable BMS to follow a systematic process to understand the health status of its members and

population through the retrospective analysis of their experience. BCI-DSS leverages the CRGs, a

categorical clinical model that assigns each individual to a single, mutually exclusive severity adjusted

category (one of 1,073), the Clinical Risk Group (CRG). Each CRG represents a disease burden category

and varies with both the severity of the chronic condition as well as the co-morbidities found.

Each unique CRG maps to an aggregate status, into one of nine health statuses, where each status may

contain multiple CRGs. These health statuses range from catastrophic conditions, such as a history of a

heart transplant, to healthy individuals without either chronic health problems or indications of risk.



Figure 10-4, Clinical Risk Group Strata, and Figure 10-5, Population’s Health Status and Severity

Distribution, summarize the available CRG groupings.

Figure 10-4. Clinical Risk Group Strata.

The stratification shows which diseases are the most prevalent, which diseases have the highest burden, as

well as which diseases are projected to have the highest burden. Each BCI-DSS analytical report can be

filtered to provide the detailed information to both guide decision making and set the strategic directions.

Figure 10-5. Population’s Health Status and Severity Distribution.

Through the understanding of each individual’s health status, various clinical and business analyses and

studies can be conducted to provide the picture as to the state of the individual members and population with

respect to:

The disease burden

Disease progression

Resource consumption (cost and utilization) outliers

Profile utilization patterns



Track quality of care

Analyze the clinical efficacy of specific treatment patterns

Analyze the costs associated with specific medical services

Assess the appropriateness of payment levels

Set and review payment rates

Profile providers

CRG Assignment

The assignment process is a hierarchical open logic process, there is no black box, wherein each individual

is assessed based on his/her most significant diagnosis or diagnoses. The process of identifying those

diseases is conditional, relying on rules governing the presence and use of diagnoses, procedures,

pharmaceutical, and age/sex demographic factors (excluding costs).

This process utilizes readily available data that will be captured in the data warehouse, which is routinely

gathered as part of the processing of medical claims (e.g., ICD-9/10, CPT, HCPCS, etc.), and it also follows

uniform data standards, NCVHS, and National Drug Codes (NDCs), e.g., clinical drug nomenclature. CRGs

assign each individual member to a single CRG category.

CRGs have four key features.

Categorical Model. CRGs assign individuals to one and only one category. If multiple chronic diagnoses

are present, they are addressed either through severity adjustment of the most significant diagnosis or

through assignment to a CRG which includes multiple diagnoses (e.g., co-morbidities).

Severity Adjusted. All chronic diagnosis CRGs are severity adjusted and reflect the extent and

progression of the member’s diagnosis or diagnoses.

Hierarchical. All CRG assignments rely on hierarchical decisions. This assures that criteria are

consistently applied.

Conditional. CRGs make extensive use of conditional relationships, including recency and frequency

between and among diagnoses and procedures. This permits the recognition of precise clinical

relationships.

Benchmark Data and Predictive Modeling Weight Sets

3M’s CRGs is a predictive modeling software that builds upon the clinical categorization and stratification of

the specific population, as described above. Because the clinical logic focuses on the population’s disease

burden, the longitudinal progression of a member’s disease burden is easily supported. This disease

progression not only provides an understanding of the population’s burden, but it can be used to develop

expected values based on historical trending.

These expected values, known as relative weights, are associated with each CRG and by extension to each

individual assigned to that CRG. This in turn allows a case mix and severity adjusted index to be calculated

for any group of members which is a composite of the relative risk posed by all of the members of that group

and communicates the health status of the group as a whole. Relative weights and case mix indices can be



calculated for different combinations of benefits, resources, or other measures. Because CRG assignments

are based solely on clinical rather than statistical criteria, risk group assignments are independent of the

resources or other measures being projected.

Relative weights are important as they convey normative information for each risk category and can be used

to make predictions. Other factors, however, may also influence the predictive accuracy of relative weights,

including:

Age and sex, especially for the healthier segments of a population;

The extent of limited or partial exposure data for newer enrollees;

The age of the data or the difference between the period from which it is collected and the period for which

it is to be applied; and

Other factors such as differences in benefit design.

Since CRGs are a clinical model, these factors were intentionally excluded from the model.

We leverage the wealth of information BMS has in its members’ historical data. There is nothing more

valuable than creating normative data and benchmarks from the population you are actually analyzing, as it

will be automatically adjusted to the demographics and case mix of the West Virginia demographics.

To develop both the retrospective normative data sets, for benchmarking, and the prospective weight set, for

predictive modeling, we will use 2 years (years 2 and 3) of the initial 3 years of historical data, to include all

claim and encounter diagnosis and procedure codes, pharmacy NDCs, and member eligibility along with age

sex factors. The 2 year grouping period will stratify every member into his/her respective CRG category.

From this we will then map the aggregate resource consumption and financials in the second year of the

grouping period to develop the retrospective benchmark data sets. The prospective weight set will use the

same grouping mentioned above but will use the fourth year of data, i.e., most current year acquired, to

capture the aggregate resource consumption and financials, as shown in Figure 10-6. Prior to developing the

actual data sets, we will determine and trim the high and low outliers to provide a more accurate normative

data set for benchmarking and predictive modeling.

Figure 10-6. Weight Development Periods.

Deloitte will also work with BMS to identify any other types of normative data it would like to use to

benchmark its analyses against. During the initial phases of data processing, we will develop a core set of

prospective weights, i.e., relative risk scores, which will be used to benchmark. If BMS requires additional

normative data sets or have existing sets, we can load them into the data warehouse and use them to as

benchmarks.

WV_DW_DSS-065

Grouping Period

Resource Period

Yr-2 Yr-3

Grouping Period

Yr-3 Yr-4Yr-2

Resource Period



In addition to the relative weights developed using the CRGs, most defined analyses will have the ability to

compare performance to the respective aggregates, shown in Figure 10-7. Throughout the analyses, as the

users drill down into lower levels of detail, they will have the ability to compare against higher, aggregate

level detail.

Figure 10-7. Comparison of Subpopulations to the Total Population.

Data Refreshes and Updates

The BCI-DSS i-Portal will provide access to the complete set of data

stored in the data warehouse, a maximum of 10 years scrolling, where

end users will have the ability to run defined reports, to conduct ad hoc

queries, and to establish trending analysis against as much data as is

stored. After the initial load of 4 years of data, i.e., 3 historic and 1

current, every update will include the transactions processed since the

last load/update. Deloitte is currently planning to both load and

reconcile new refreshes of data on a monthly basis, but we can adjust

this accordingly based on the business requirements.

The i-Portal Dashboard (shown in Figure 10-8 below), which can be

configured based on user preferences, will view the detailed

information in the last 12 month period, referenced as the Analysis

Period (AP). This AP will be a scrolling 12 months and will be updated

on a monthly basis as new data extracts are received. Once new data

extracts are received and processed through the data cleansing and

validation process, it will then be processed through the business and

clinical processing logic. Once the data processing is complete, we will

then populate the respective data fields, by month, into the data

warehouse and refresh any required views in the BCI-DSS system.

ICD-10 Compliance

The implementation of ICD-10 in 2013 is likely the most significant event since the adoption of DRGs for

Medicare payments in 1983. ICD-10 codes bring more clinical specificity and precision. Healthcare

organizations are faced with finding and translating all ICD-9 codes in their documentation‚ contracts‚

homegrown applications‚ databases‚ financial and quality reports‚ super-bills‚ and medical necessity policies.

The question is: Can this process be fully automated?

WV_DW_DSS-064

Population Mem Avg MM Avg DB DB ImpactAvg Cost

(PMPM)

Proj Cost

(PMPM)

Total 331,318 7 1.124 112.380 $531 $846

FFS 218,301 8 1.562 102.920 $762 $1,250

MCO 1 113,017 7 0.277 9.46 $565 $1,169

A Dictionary is Handy!

The GEMs are like a two-way

foreign language dictionary. You

can look up an ICD-9 code and

see all the ways it might be said

in ICD-10. You use the 9-to-10

GEM if you only know the ICD-9

code and are forced to use the

(usually worse) ICD-10 code that

expresses it. You use the 10-to-9

GEM (in reverse lookup, a

capability not found in non-

computerized dictionaries) to see

all the ways an ICD-10 coder

might code the condition or

procedure in ICD-10 when he/she

has access to additional

information in the medical record.



Deloitte has been researching and working with industry leaders to identify sustainable approaches and to

develop methodologies to ease the transition. Our experts have been working with many commercial payers,

providers and integrated delivery systems, as well as state Medicaid agencies, to develop the ICD-10

transition roadmaps and implementation plans to train resources and remediate existing system and

business processes.

Deloitte has teamed with some of the leading clinical and coding experts in the industry to include 3M HIS.

No entity has more experience with ICD-10s than 3M HIS, as it is under contract with the Center for

Medicare & Medicaid Services (CMS) as they designed and developed the ICD-10 Procedure Coding

System (PCS) and the General Equivalence Mappings (GEMs).

3M HIS has a plan in place to update all their groupers that use ICD diagnosis and procedure codes, which

includes the CRGs, by October 2012. 3M’s approach is to replicate the ICD-9 based groupers in ICD-10,

which means the same record will generate the same CRG whether it is ICD-9 or ICD-10 based. In time, as

post implementation data is generated and can be used to calibrate 3M’s logic, the groupers will begin to

take advantage of the new level of specificity.

This plan, which is a sound and conservative way of dealing with the transition, will have the added benefit of

allowing organizations to continue to track their data trends without running into a sudden cliff caused by the

shift in member assignments that will be created by the integration of new data.

Using the CRGs to clinically stratify the members will minimize the effects, caused by the ICD-10 transition,

on the BCI-DSS analyses, historical trends and studies, as the CRGs will level off any sudden shifts in

trends.

BCI-DSS Analytics

Deloitte’s proposed BCI-DSS solution is designed to exceed BMS’ DW/DSS requirements as outlined in the

RFP. Within our solution and the underlying data model, with the business and clinical stratification, lies the

foundation for BMS to design, conduct, and develop an unlimited number of defined reports, trending

analyses and ad hoc queries. Users will be trained to understand the data foundation after which they will be

able to design and develop their own reports, analyses and run various ―what-if‖ queries through the use of i-

Portal, a complete COGNOS system solution. Using COGNOS as the underlying application for the i-Portal

will enhance BMS’ capability to support any type of informational analysis.

In addition to the power and flexibility of the i-Portal, Deloitte brings a wealth of cross operational expertise

and subject matter experts to BMS to advise on the development of data analytics for this engagement. We

also bring a wealth of experienced advisors with relevant qualifications in all aspects of business and clinical

analytics, to include, but not limited to:

Financial analysis and management

Program performance analysis

Risk-adjustment

Medicaid rate setting

Provider performance monitoring



Provider performance analysis (P4P)

Quality management and assessments

Population analysis

Resource consumption outlier analysis

Profile utilization patterns analysis

Quality of care analysis

Analyze the clinical efficacy of specific treatment patterns

Analyze the costs associated with specific medical services

Assess the appropriateness of payment levels

Set and review payment rates

Profile providers

The combination of our professionals’ reporting expertise and the awareness of the relevant analytics to

support BMS’ goals will provide valuable insight into the development of the BCI-DSS analytical solution.

Deloitte’s solution is comprehensive and will power BMS and their work units and business areas with

actionable, intelligent information delivered through the use of an innovative leading edge BCI-DSS system

that is flexible and which will surpass the current set of demands, thereby leading BMS into the MITA

Oriented future.

Analytics Delivery

Deloitte’s focus during phase one is to compile a comprehensive list of reports to develop in support of BMS’

work units/business areas. We will work with BMS to prioritize the list of reports in order to develop a delivery

plan. These reports will then be further defined as we work with the various work units to detail the design

specifications. Each report design will include (i) a layout of the report with drilldowns, if applicable; (ii)

specifications of the data elements required to produce the report; and (iii) the business and clinical

mathematics required to generate the report. Each report design will be reviewed and signed off before

moving into the development life cycle.

Deloitte’s approach will develop the analytics for tiered users, to include executive, causal business, and

power level users. When users log into the i-Portal they will have a customizable dashboard which they can

configure to meet their specific requirements, as shown in Figure 10-8. There will be a set of defined

modules that a user can select to view. A user will be granted access or entitlement authorizations that will

define both the level of detail and the business areas he/she is authorized to view.



Figure 10-8. i-Portal Dashboard.

From the Dashboard, shown in Figure 10-8, users will have access to the respective areas they are entitled

to access. An extensive portfolio of defined reports will be available through the i-Portal. The reports serve as

templates that can be either run as is or modified by the user. The reports are organized in topical folders

that cover a wide range of information by business area, for example, Care Management will have topical

folders for Population, Member, Utilization, Quality, Outcomes, Trending/Studies along with an ad hoc report

generation function. Within each topical folder there will be a series of defined reports and studies/trending

analyses. Each report will have various filters to focus the information viewed as well as providing drilldown

capabilities into the next levels.

BCI-DSS will have significant filtering capabilities:

Analysis/Date Period

Product (e.g., FFS, MCO1, MCO2, MCO3)

Eligibility

Eligibility Category

Region/County

Demographics (e.g., age, gender)

Disease

Services (e.g., utilization types, service type, sites of service)

Quality Indicators (i.e., gaps in care, outliers,)

Provider Types

View

Trend Analyses

View Detailed

Analytical ReportsList of Frequently

Used / Accessed

Analytical Reports

Establish Alerts and Triggers

Geo Map Data

WV_DW_DSS-058



Claim Types

Pharmacy Types (e.g., brand, generic)

As the user drills down in the reports, he/she will be able to benchmark the detail to the aggregate(s). The

below figures demonstrates the drilldown and benchmarking capabilities.

The Geo-Mapping capability, inherent in the COGNOS tool, allows the user to map the information analyses

to a geographic map in order to provide a graphic view of the information distribution. This provides the basis

to identify key areas by geographical component (i.e., region, county, zip code, etc.).

Personal Health Record

BCI-DSS will enable BMS to follow a systematic process to understand the health status of its members and

population through the retrospective analysis of their experience. The data warehouse design centers on the

members’ data from which everything else is derived; therefore the lowest level of information is the member

level detail which can be viewed in their Personal Health Record (PHR).

A crucial part of a business and clinical analytical solution is to create a clear picture of a member’s health

status. BCI-DSS does just that, through the creation of a member PHR, as shown in Figures K1 through K6,

by aggregating healthcare claims, pharmacy, lab results and other various sources of data. The underlying

goal of the PHR is to capture each individual’s health status and to share this information across the

healthcare delivery spectrum.

The PHR contains a series of tabs whereby users with the proper entitlements can view the latest 12 month

analysis period of claims data (i.e., medical, encounters and pharmacy), clinical and financial details, and

quality indicators. The PHR is the lowest level of drilldown from a member list, as shown in the analytic

examples below. During the business requirements gathering effort, all elements in the PHR tabs will be

reviewed for potential revision and validation before development.

The PHR contains the following tabs:

Administrative. This tab contains the individual’s administrative information to include contact information,

age, sex, eligibility months, and Primary Care Physician (PCP) information. (Shown in Appendix A, Sample

Reports).

Claims. Included in this tab is a chronological list of processed medical claims/encounters to include all

inpatient, outpatient, office, ambulatory surgical center, emergency room, and independent laboratory as well

as pharmacy. Claims will also be able to be filtered by claim type. (Shown in Figure 10-9).

Providers. This tab displays a chronological list of providers that have provided services to the member. It

identifies the provider specialty, date of service, place of service, diagnoses, and procedures. (Shown in

Figure 10-10).

Pharmacy. A listing of all Rx prescriptions filled by the individual. This list contains the NDC, description, fill

date, brand/generic, number of fills, prescribing provider, pharmacy and cost. (Shown in Figure 10-11).

Diagnoses and Procedures. This tab contains the list of diagnoses and procedures categorized by disease

category. (Shown in Appendix A, Sample Reports).



Admits. This tab contains a list of inpatient admissions that the member has had. The information includes

admit and discharge dates, attending physician, diagnoses, procedures, Length of Stay (LOS), total paid and

complications, DRG (if assigned). As an option, but not scoped for inclusion in this proposal, BMS could use

the 3M’s All Patient Refined DRGs (APR™ DRGs) to view the APR-DRG, APR-SOI (Severity of Illness), and

APR-Risk of Mortality (ROM), cost variance, and LOS variance. (Shown in Appendix A, Sample Reports)

Diagnostic Testing. This tab includes three lists; laboratory results, radiology results and diagnostic

procedure results. The information displayed in this tab is populated by data extraction from ancillary service

data (e.g., national providers of diagnostic testing services). The information captured includes test type,

description, dates of service, results, and abnormal flags (lab only). (Shown in Figure 10-12).

History. This tab provides the details as to the member’s health status/disease history and his/her

progression over time and will include the AP date range, CRG (Health Status) status and severity, disease

description, disease burden, total paid, projected cost, inpatient admits, and number of gaps in care. (Shown

in Figure 10-13).

Summary. This tab details the various utilization, quality, financial, pharmacy, as well as identified gaps in

care. (Shown in Figure 10-14).

Figure 10-9. PHR Claims Detail Tab.

WV_DW_DSS-101_4

IP Cost OP Cost OP Surgery Cost Rx Cost Lab Cost Rad Cost Ther Cost

$25,594 $18,243 $68 $9,469 $554 $474 $40

Date of

ServicePlace of Service Description Units

Primary

Diagnosis

Secondary

Diagnosis

Total

CostNetwork Provider Name

09-Apr-2006 Emergency Room Emergency Dept Visit 1 Migrne Unsp w/o Ntrc Mgrn $23 Y Powell, Ralph


09-Apr-2006 OP Hospital Hydroxyzine Hcl Injection 1 Migrne Unsp w/o Ntrc Mgrn $17 Y Powell, Ralph

09-Apr-2006 OP Hospital Meperidine/Promethazine Inj 1 Migrne Unsp w/o Ntrc Mgrn $6 Y Powell, Ralph

09-Apr-2006 Pharmacy Albuterol 17 $13 Y Johnson, Robert L

09-Apr-2006 Pharmacy Zofran 50 $1,119 Y Johnson, Robert L



03-Apr-2006 OP Hospital Hydroxyzine Hcl Injection 1 Migrne Unsp w/o Ntrc Mgrn $17 Y Powell, Ralph

03-Apr-2006 OP Hospital Meperidine/Promethazine Inj 1 Migrne Unsp w/o Ntrc Mgrn $6 Y Powell, Ralph

01-Apr-2006 Pharmacy Catapres-Tts 3 4 $165 Y Johnson, Robert L

30-Mar-2006 Office Office/OP Visit, Est 1 DM 2 w/o Cmp Nt St Uncntr $23 Y Johnson, Robert L

30-Mar-2006 Pharmacy Hydrocodone-Acetaminophen 25 $9 Y Johnson, Robert L

Admin Claims Providers Pharmacy Diagnoses and Procedures Admits Diagnostic Testing HS History Summary X

Member ID: 1191167 Disease Burden: 4.530

Name: Smith, Jane R Disease(s): Diabetes and Asthma

DOB: 04-Oct-1972 CRG S/S: 6-5

Sex: F Cost: $53,305 Projected Cost: $42,382



Figure 10-10. PHR Provider Service Detail Tab.

Figure 10-11. PHR Pharmacy Detail Tab.

WV_DW_DSS-102_4

Provider ID Provider Name PCP Specialty Network Date of Service Place of Service Primary Diagnosis Secondary DiagnosisPrimary

Procedure

Secondary

ProcedureP

710480302 Powell, Ralph J No Y 01-Feb-2006 IP Hospital DM 1 Neuro Nt St Uncntrld Gastroparesis

207844200 Johnson, Robert R Yes Gen Practice Y 01-Feb-2006 IP Hospital Nausea with vomiting DM 1 w/o Cmp Nt St Uncntrld Initial Hospital Care

800566513 Powell, Ralph No Y 01-Feb-2006 Emergency Room Nausea with vomiting Abdominal Pain Epigastric

800566513 Powell, Ralph No Y 01-Feb-2006 Emergency Room Nausea with vomiting Abdominal Pain Epigastric Als1-Emergency

710480302 Powell, Ralph J No Y 02-Feb-2006 IP Hospital Abdominal Pain Oth Spcf St CT Abdomen w Dye

710480302 Powell, Ralph J No Y 02-Feb-2006 IP Hospital Abdominal Pain Oth Spcf St CT Pelvis w Dye

207844200 Johnson, Robert R Yes Gen Practice Y 02-Feb-2006 IP Hospital Nausea with vomiting DM 1 w/o Cmp Nt St Uncntrld Subsequent Hospital Care

207844200 Johnson, Robert R Yes Gen Practice Y 03-Feb-2006 IP Hospital Nausea with vomiting DM 1 w/o Cmp Nt St Uncntrld Subsequent Hospital Care

207844200 Johnson, Robert R Yes Gen Practice Y 16-Feb-2006 Office DM 1 Neuro Uncntrld Gastroparesis Office/OP Visit, Est

207844200 Johnson, Robert R Yes Gen Practice Y 21-Feb-2006 Office Acute Sinusitis Nos Office/OP Visit, Est

010566503 Powell, Ralph No Y 01-Mar-2006 OP Hospital Headache Meperidine/Promethazine Inj

010566503 Powell, Ralph No Y 01-Mar-2006 Emergency Room Headache

010566503 Powell, Ralph No Y 01-Mar-2006 OP Hospital Headache Hydroxyzine Hcl Inj

010566503 Powell, Ralph No Y 01-Mar-2006 Emergency Room Headache Emergency Dept Visit




DOB: 04-Oct-1972 CRG S/S: 6-5


WV_DW_DSS-103_4

NDC Number Description Fill Date Brand Name (Y/N) Quantity Fills Provider Name Cost Pharmacy Name

006033881 Hydrocodone-Acetaminophen 30-May-2006 Y 25 4 Johnson, Robert L $37

575999877 Precision Xtra 30-May-2006 Y 50 18 $1,629

005970033 Catapres-Tts 3 26-May-2006 Y 4 3 Johnson, Robert L $495

575998547 Precision 26-May-2006 Y 8 5 $115

599301560 Albuterol 26-May-2006 N 17 13 Johnson, Robert L $186

000027510 Humalog 26-May-2006 Y 10 10 $789

001730446 Zofran 22-May-2006 Y 50 3 Johnson, Robert L $3,401

003783475 Nifedipine Er 22-May-2006 Y 30 7 Johnson, Robert L $262

370000455 Prilosec Otc 22-May-2006 Y 28 11 Johnson, Robert L $295

501110430 Metoclopramide Hcl 22-May-2006 N 60 14 Johnson, Robert L $178

003782074 Lisinopril 19-May-2006 N 60 8 Johnson, Robert L $125

006035468 Propoxyphene Napsylate w/Apap 19-May-2006 Y 65 18 $309

000930864 Ciprofloxacin Hcl 16-May-2006 N 16 1 $10

007812613 Amoxicillin 15-May-2006 Y 30 6 $59




DOB: 04-Oct-1972 CRG S/S: 6-5




Figure 10-12. PHR Diagnostic Testing Results Tab.

Figure 10-13. Health Status History Tab.

Lab

Test Type Date of Service Result Range

HGB A1c 17-Aug-2006 4.8 14.0-18.0

HBG 17-Aug-2006 L7.0* 42.0-52.0

HTC 17-Aug-2006 L21.1 4.3-6.1

Radiology


Diagnostic Procedures


Key L = Abnormal Low, H = Abnormal High, WNL = Within Normal Limits, * = Critical Value

WV_DW_DSS-106_3




DOB: 04-Oct-1972 CRG S/S: 6-5


WV_DW_DSS-107_4

Analysis Period CRG-SS CRG Description DB Total CostProj Cost

(PMPM)IP Adm Gaps In Care Product

June 2005 to May 2006 6-5 Diabetes and Asthma 4.530 $53,305 $42,382 10 3 FFS

June 2004 to May 2005 6-3 Diabetes and Asthma 3.890 $32,521 $36,859 5 6 FFS




DOB: 04-Oct-1972 CRG S/S: 6-5




Figure 10-14. PHR Member Experience Summary Tab.

Inpatient Parameters AP Value

Admits 4

Days 18

Actual/Expected LOS 0

Outpatient Parameters AP Value

ER Visits 29

OP Hospital Visits 0

Office Visits 12

Lab Tests 53

Radiology 13

Therapy 1

Consults 10

Quality Parameters AP Value

Preventable Admits 0

Gaps In Care 3

Re-admits < 30 days 0

Disease Progression 0

Disease Complication 0

Financial Parameters AP Value

Total Cost $53,305

Inpatient $25,594

Outpatient $18,243

Hospital Outpatient $5,232

Lab $554

Radiology $474

Therapy $40

Office $271

Pharmacy Cost $9,469

Out of Network $0

Pharmacy Parameters AP Value

Total Scripts 62

Brand Scripts 41

Projected/Actual Cost Parameters AP Value

Projected Cost (PMPM) $3,532

Actual Cost (PMPM) $4,442

Projected / Actual Cost Ratio 0.8

Gaps In Care

Asthma Pharmacotherapy

Influenza Vaccine

Pneumonia Vaccine


WV_DW_DSS-108_3



DOB: 04-Oct-1972 CRG S/S: 6-5





Operations Management is the focal point of the Medicaid enterprises. It includes the payment to providers,

Managed Care Organizations (MCOs), pharmacy program, etc. This business area focuses on payments

and receivables and owns all information associated with service payment and receivables.

The DSS analytics will focus on service utilization, service deliver and service financials. These information

analyses will be developed through easily accessible series of defined reports along with the ability to create

and run ad hoc queries.

The core Operations Management need of any Medicaid agency is insight into the utilization and financial

picture. Many systems will provide lists of claims for summing costs; i-Portal does much more, by showing

the reasons for cost trends. It is designed to return accurate information that can be used to see the holistic

view of the entire Medicaid operation. Some of its financial reporting capabilities encompass the following:

Financial analyses to match utilization to costs from the entire population view down to the individual

member and provider.

Hundreds of pre-calculated and customizable measures, such as sums, rates and ratios (per member,

PMPM, per 1000, per admit).

Benchmark to aggregate information to perform comparison analyses.

Utilization analyses to view resource consumption across populations.

The following reports demonstrate the level of detail designed in the BCI-DSS solution. These reports

generally provide an overall view of the population level but can be filtered to focus the information down to

the detail level. These reports support the drilldown to the next level of detail to the individual personal health

record.

Figure 10-15. Population Cost Overview by Health Status Category.

Figure 10-15 demonstrates the BCI-DSS ability to provide a financial overview of the population by health

status. This report shows the cost on both a per member per month (PMPM) and per member per year

(PMPY) basis, but links it to the clinical component of the data. This report can be run against any population

through the filter settings. Users will have the ability to view this report by defined subpopulations (i.e., FFS,

MCOs, products, eligibly members, age categories, regions, etc.). This report has drill down capabilities,

Health Status Mem % Mem Avg MM Cost

(PMPM) Cost

(PMPY) Proj Cost (PMPM)

Proj Cost (PMPY)

Avg DB

Total Population 331,318 100 7 $531 $3,966 $682 $8,182 1.124

1 Healthy 185,858 56.10 7 $49 $322 $54 $647 0.173

2 Significant Acute 10,555 3.19 7 $236 $1,723 $196 $2,356 0.316

3 Single Minor Chronic 18,670 5.64 7 $203 $1,479 $216 $2,593 0.499

4 Multiple Minor Chronic 5,088 1.54 8 $341 $2,621 $432 $5,185 0.840

5 Single Significant Chronic 43,679 13.18 8 $518 $4,178 $734 $8,813 1.340

6 - Two Significant Chronic 43,283 13.06 9 $1,277 $12,111 $2,138 $25,662 3.190

7 Multiple Significant Chronic 17,118 5.17 10 $2,024 $19,623 $3,452 $41,424 5.133

8 Complex Malignancies 3,210 0.97 8 $2,178 $17,744 $2,098 $25,174 3.990

9 Catastrophic 3,857 1.16 9 $2,703 $24,808 $4,436 $53,228 6.748



where the user can see the financial breakdown at a granular level, i.e. severity levels, as well as drill downs,

as shown in Figures 10-16.

Figure 10-16. Expansion to View Severity Level Details.

Deloitte, through the BCI-DSS solution, is not only producing the reports but the data files that underlie the

reports. During the business requirements gathering effort, we will work with the business area users and

determine the types of specific reports they require to develop as defined queries. We will also train the

users and enable them to conduct their own analytics, so that users can conduct ―what-if‖ analyses and other

querying and reporting on the data. During the requirements gathering we will review a list of MARS reports

that BMS would like to have in the BCI-DSS system, for post claims adjudication analysis.

BMS has identified in the RFP that there are approximately 300 MARS reports they would like to transition

over to the new DSS system. In reviewing the list of these reports, we have come to the opinion that many of

these static reports can be redesigned and reprogrammed to provide a more innovative and dynamic

delivery of the required information to the end users. Deloitte will work with BMS to design the post claims

adjudication MARS reports in our BCI-DSS solution in order to minimize the number of reports to be

developed, while still providing the required information to guide decision making and guide operations.

Care Management

Care Management is the most advancing business area as the Medicaid program evolves. Care

Management includes the processes that support both individual care management and population

management. This area collects information about the needs of the individual members, their health status,

plans of care, and outcomes. It is focused on identifying members that require special needs, assesses

needs, develops treatment plans, monitors and manages the plan, and reports outcomes.

The BCI-DSS analytics will focus on the population and member health status, quality indicators, service and

resource utilization, and outcomes analysis. The DSS will provide a Patient Health Record that provides a

detailed view of member’s service-level detail information.

Health Status Mem % Mem Avg MM Cost

(PMPM) Cost

(PMPY) Proj Cost (PMPM)

Proj Cost (PMPY)

Avg DB

Total Population 331,318 100 7 $531 $3,966 $682 $8,182 1.124

1 Healthy 185,858 56.10 7 $49 $322 $54 $647 0.173

2 Significant Acute 10,555 3.19 7 $236 $1,723 $196 $2,356 0.316

3 Single Minor Chronic 18,670 5.64 7 $203 $1,479 $216 $2,593 0.499

4 Multiple Minor Chronic 5,088 1.54 8 $341 $2,621 $432 $5,185 0.840

5 Single Significant Chronic 43,679 13.18 8 $518 $4,178 $734 $8,813 1.340

Severity Level 1 29,969 9.05 8 $377 $2,971 $519 $6,224 1.038

Severity Level 2 8,228 2.48 8 $572 $4,767 $882 $10,589 1.479

Severity Level 3 3,619 1.09 9 $985 $8,625 $1,579 $18,947 2.622

Severity Level 4 974 0.29 9 $1,366 $11,861 $1,897 $22,758 2.909

Severity Level 5 491 0.15 8 $1,107 $8,557 $1,410 $16,916 2.539

Severity Level 6 398 0.12 9 $2,082 $18,235 $2,561 $30,733 4.236

6 - Two Significant Chronic 43,283 13.06 9 $1,277 $12,111 $2,138 $25,662 3.190

7 Multiple Significant Chronic 17,118 5.17 10 $2,024 $19,623 $3,452 $41,424 5.133

8 Complex Malignancies 3,210 0.97 8 $2,178 $17,744 $2,098 $25,174 3.990

9 Catastrophic 3,857 1.16 9 $2,703 $24,808 $4,436 $53,228 6.748



Our solution for care management analytics has been developed by a wealth of experienced clinical and

analytical experts that have worked with commercial, state Medicaid and federal Medicare operations. Our

experts bring experience in providing targeted interventions and supporting continuously measured

outcomes, integrating medical management initiatives, creating a holistic and longitudinal view of the

member across the continuum, allowing the clinical care manager to engage members and providers, and

providing a modular approach to promote continuous quality/performance improvement. Below are a few

examples of our clinical reporting.

Our BCI-DSS solution will enable end users with a wealth of analytical reports and drilldown capabilities.

BCI-DSS builds on the understanding of each member’s health status, to create the underlying foundation for

the population analyses. These analyses aggregate at the member level and group individuals by clinically

meaningful categories, based on their disease or combination of diseases (co-morbidities), state of

progression (disease burden), and severity of illness (e.g., clinical complexity). Such grouping creates a

common, clinically based foundation of comparative data that can be used as the basis for effective decision

support and program management.

Through the population’s clinical stratification, users can easily identify the diseases and combinations of

diseases that are placing the highest burden on BMS. Figure 10-17, Top 5 Diseases by Disease Burden

Impact, illustrates population grouping and reporting according to highest impact statistics. Figure 10-17

displays the top five diseases that are placing the highest burden on the specific population.

For each disease, we case mix and severity adjust the members categorized with chronic diseases to

develop an individual disease burden score (Avg DB). As we look for the diseases that are placing the

highest burden, we weight the average disease burden with the percent of the population categorized as

actually having the chronic diseases. For example, in Figure 10-17, Diabetes has an Avg DB of 3.0, where

Congestive Heart Failure (CHF) has an Avg DB of 3.7. Just because CHF has a higher Avg DB than

Diabetes does not mean that the burden to the state is more significant. To determine the direct impact, one

has to look at the number of individuals that actually have the chronic disease; therefore, Diabetes which has

more members (69,404) than CHF (24,558) has a higher Disease Burden (DB) Impact.

Figure 10-17. Top 5 Diseases by Disease Burden Impact.

Similarly, Figure 10-18, Diabetes Drilldown Reflecting Top 15 Co-morbidities by Disease Burden Impact,

shows the next level of detail available by drilling down into the Diabetes disease category.

Population Mem MM Avg DB DB Impact

Total 1,228,893 12,390,712 1 100

Disease Mem MM Avg DB DB Impact

Schizophrenia 69,404 758,032 3.0 16.9

Diabetes 58,108 644,147 2.2 10.4

Asthma 96,401 1,081,159 1.2 9.4

COPD and Bronchiectasis 30,717 343,094 3.3 8.2

Congestive Heart Failure 24,558 261,594 3.7 7.4

WV_DW_DSS-050_2



Figure 10-18. Diabetes Drilldown Reflecting Top 15 Co-Morbidities by Disease Burden Impact.

From this information, BMS can both identify the diseases for primary

focus and develop the programs and interventional strategies to

address the impacts. For example, when reviewing the diseases that

are placing the highest burden on the state, e.g., Diabetes, one can drill

down into each specific disease and determine the burden drivers. This

drill down capability will allow BMS to determine if disease and/or case

management programs need to focus on the individual disease, such

as Diabetes alone, or whether it should focus on co-morbidities (e.g.,

Diabetes and Advanced Coronary Artery Disease (CAD)). This

understanding creates the basis to identify the appropriate individuals

for enrollment into various programs.

The BCI-DSS solution will also provide detailed analyses comparing

various populations to an aggregate benchmark. Figure 10-19 shows

the FFS and MCO1 populations’ dominating diseases compared

against the overall population. This analysis provides a view of not only

the dominant diseases but also provides the disease burden. For example, in Figure 10-19 it shows that

Schizophrenia is the dominant chronic disease for all 3 populations, but all three have a different disease

burden. The overall population has an average disease burden of 1.966 compared to FFS’s 2.759 and

MCO1’s 1.909. This means that the FFS has a sicker population of Schizophrenia then MCO1 and above the

total population benchmark. Another view of the analysis shows that MCO1 has a disease burden impact of

Total Mem MM Avg DB DB Impact

Total 1,228,893 12,390,712 1 100

Disease Mem MM Avg DB DB Impact

Diabetes 69,404 758,032 3.0 16.9

Diabetes 8,967 89,505 1.6 20.7

DM-Other Mo Chron 4,420 50,678 3.0 19.1

DM-Adv CAD-Oth Dom Chron 3,407 37,977 3.8 18.7

DM-HPT-Oth Dom Chronic 3,710 41,719 3.0 16.0

Diabetes – Advan CAD 3,710 40,312 2.9 15.5

CHF – Diabetes – COPD 2,242 24,672 4.2 13.6

DM – Other Sig Chronic 3,124 33,997 3.0 13.5

CRF-DM-Oth Dom Chron 1,878 19,471 4.4 11.9

CHF-Diabetes 2,245 23,600 3.5 11.3

Diabetes – HPT(1) 3,290 33,399 2.0 9.5

DM-1+ Other Dom. Chron Dis. 1,617 17,703 3.8 8.9

CHF-DM-Other Dom Chron 1,412 14,303 3.9 7.9

DM-Adv CAD-Oth Dom Chron (3) 1,340 15,151 4.1 7.9

DM-Adv CAD-Oth Dom Chron (4) 1,165 13,645 4.1 6.9

Diabetes – Asthma 1,632 18,922 2.7 6.3

WV_DW_DSS-010_2

Disease Burden

Using 3M’s CRGs as the

categorical classification model

provides the means to case mix

and severity adjust the members

that have common disease(s).

For example, not all Diabetics

are grouped in the same bucket,

they will be distributed to

separate buckets based on the

progression of their disease. So

when you case mix members

within a disease they will vary

in progression, i.e., the severity

adjustment.



1.366 for Schizophrenia, higher than the FFS and above the total population benchmark. This is due to the

fact that they have more members with Schizophrenia and this places a larger burden on them from a

resource consumption stand point. With this information, users can start to do more drill down analysis to

determine the cost associated with the impacts.

Figure 10-19. Population Comparison of Diseases and Disease Burdens.

Based on the detailed stratification of the data stored in the data warehouse, BMS operations and end users

will be able to query various detailed reports and trending analyses. The following are some examples of the

various member level reports that the care managers can view.

The BCI-DSS solution has 4 main focus areas, Quality, Utilization, Pharmacy, and Financial analyses. The

quality reports focus on quality of care and service deliver, looking at gaps in care, preventable inpatient

admissions and re-admissions. This enables care managers with the ability to effectively stratify the


(PMPM)

Proj Cost

(PMPM)

Total 331,318 7 1.124 112.380 $531 $846

Disease

Schizophrenia 254 8 1.966 0.153 $1,817 $2,030

Diabetes 120 8 1.550 0.062 $576 $1,156

Substance Abuse 106 9 .811 0.032 $331 $485

Hypertension 83 8 1.074 0.033 $324 $210

Asthma 367 8 .281 0.034 $297 $356


(PMPM)

Proj Cost

(PMPM)

FFS 218,301 8 1.562 109.920 762 $1,250

Disease

Schizophrenia 17 9 2.759 0.142 $1,367 $2,030

Hemi- and Quadriplegia 4 9 7.167 0.087 $6,784 $5,602

Diabetes 7 9 2.867 0.061 $631 $1,156

Asthma 13 5 0.578 0.023 $501 $356

COPD and Bronchiectasis 3 7 2.131 0.019 $485 $1,995


(PMPM)

Proj Cost

(PMPM)

MCO 1 113,017 7 0.277 9.46 $565 $1,169

Disease

Schizophrenia 237 8 1.909 1.366 $1,637 $2,030

Diabetes 113 8 1.468 0.501 $832 $1,156

Asthma 354 8 0.27 0.289 $302 $356

Hypertension 81 9 1.063 0.260 $289 $210

Substance Abuse 106 9 0.811 0.259 $331 $485

WV_DW_DSS-109_4



population and select appropriate members for intervention based on disease burden and indicators of

disease progression or risk factors of disease progression and/or complications.

Quality Analysis

Deloitte developed the gaps in care logic using external evidence based guidelines (e.g., HEDIS, CDC, ADA,

etc.). Our gaps in care focus on the following categories:

Screenings which includes Breast, Cervical and Colorectal Cancer, and Chlamydia;

Immunizations which includes all childhood and adolescent immunizations, pneumonia and influenza

vaccines; and

Disease Specifics which include Beta-blockers post MI, Lipid testing post Cardiovascular event,

Osteoporosis management post hip fracture, Asthma medication and Multiple Diabetes treatment gaps

such as vision screening, recency of Hgb A1c, foot exams, nephropathy screening and lipid testing.

The majority of these gaps are based on HEDIS requirements with the rest following CDC or ADA

recommendations. The design of our BCI-DSS is driven from dynamic configuration tables, so it easily

supports flexibility to add or modify gaps in care as BMS requires.

Figure 10-20. Member Quality Analysis.

Figure 10-20 shows a standard member quality analysis. This analysis can be filtered to view members

within certain subpopulations, as will be defined during the business requirements gathering. This analysis

not only shows the number of gaps in care, preventable inpatient (IP) admissions (Prev Admits) and re-

admissions (Re Admits) but also shows the progression of the member’s disease(s), in the Δ DB (delta

disease burden) column. For example, Jane Smith, a 68 year old female, has 6 gaps in care, 2 preventable

admits and 1 readmission. From the last analysis period to the current analysis period, she progressed in

disease burden by 4.523. This is an indicator that she may be at high risk for further progression. The

preventable admission could be caused by her not getting the appropriate level of care, as evident with the

gaps in care. To further investigate this potential case to figure out the root cause, a care manager could drill

down into this Jane’s PHR to view the details that led to the disease progression.

Member ID Name Product Age Sex CRG S/S

DB Δ DB MM Cost

(PMPY) Proj Cost (PMPY)

Gaps in Care Prev

Admits Re

Admits

17500663 Smith, George L FFS 62 M 7-4 5.319 0.000 12 $41,056 $49,765 5 6 4

14206214 Smith, Jane FFS 55 F 8-5 6.793 1.272 3 $62,781 $0 2 6 15

21579620 Smith, George L FFS 48 M 7-5 5.559 0.000 12 $64,952 $52,006 7 4 3

16000599 Smith, George C FFS 68 M 9-6 8.694 0.000 12 $20,193 $81,342 8 1 7

04882818 Smith, Jane J FFS 48 F 9-4 7.016 3.549 12 $160,815 $65,641 11 4 7

12844850 Smith, Jane MCO 2 63 F 7-4 5.281 0.000 12 $15,467 $49,406 4 2 2

24004517 Smith, Jane FFS 47 F 7-4 6.016 0.000 12 $23,892 $56,282 7 2 2

03936377 Smith, Jane R MCO 2 68 F 7-3 4.523 2.658 9 $15,547 $42,314 6 2 1

61733632 Smith, Jane S MCO 1 4 F 5-1 0.11 0.000 12 $750 $0 8 0 3

16330566 Smith, George T FFS 64 M 7-5 6.732 0.000 12 $25,824 $62,981 8 3 2

32006563 Smith, George MCO 1 79 M 9-6 21.135 0.000 12 $13,705 $197,734 4 3 5

62457113 Smith, George W MCO 1 65 M 7-4 6.411 0.000 9 $208,775 $59,982 8 2 4

13697141 Smith, Jane FFS 61 F 7-5 5.78 2.498 12 $64,603 $54,079 8 2 3

49198618 Smith, George W MCO 2 47 M 7-3 6.25 0.000 9 $106,335 $58,474 3 8 12

10070720 Smith, George FFS 62 M 7-5 6.732 0.000 12 $6,326 $62,981 7 3 4

56277059 Smith, Jane S MCO 2 60 F 7-6 6.296 0.000 12 $35,568 $58,901 8 4 8



The BCI-DSS solution will allow users to run ad hoc queries and do various ―what if‖ analyses to easily

identify members that warrant further investigation. For example, leveraging the underlying stratification, a

user could run a query to identify all members, for a particular population, that has over 6 gaps in care with a

disease progression greater than 2. The results of this type of query will provide a care manager with a

wealth of information on the population and provide a queue of individuals to analyze.


Figure 10-21. Member Utilization Analysis.

The Utilization Analysis provides an understanding of the resources and services consumed by the members

in the Medicaid population. Figure 10-21 shows the number of IP admissions, IP days, emergency room (ER)

visits, office visits, and laboratory, radiology, and therapy units consumed during the last 12 months. This will

again help identify areas and members that may require further analysis and potential investigation as to the

root cause of the identified impact(s). For example, using Jane Smith again, she has 5 short stay IP

admissions (6 days total), 2 of them where preventable and 1 was a re-admission, according to the quality

analysis in Figure 10-20. This means that over 50 percent of her admissions where preventable, counting the

re-admit as preventable. Jane has seen her primary care physician (PCP) in the office 15 times but also went

to the ER 4 times. She has 6 gaps in care and has had no lab tests done, but had 4 radiology services.

The BCI-DSS can be used to paint a very detailed picture of the members’ quality of care, looking at the

potential impact of delivery of care and the utilization of services. These analyses will provide the business

and clinical actionable information for care and program managers to conduct detailed population

management. The goal is to minimize the impacts identified above and improve the outcomes of services

and programs to improve the quality of care, the quality of life, and the care delivery.

Sample Financial and Pharmacy Analyses are shown in Appendix A, Sample Reports.

Population Management Analysis

Member ID Name P

rod

uc

t

Ag

e

Sex

CR

G

S/S

DB

MM

Co

st

(PM

PY

)

Pro

j C

os

t (P

MP

Y)

IP A

dm

IP D

ays

ER

Vis

its

Off

ice

Vis

its

OP

-La

b

Un

its

OP

-Rad

Un

its

OP

-Th

er

Un

its

17500663 Smith, George L FFS 62 M 7-4 5.319 12 $41,056 $49,765 7 36 9 4 25 5 14

14206214 Smith, Jane FFS 55 F 8-5 6.793 3 $62,781 $0 18 58 9 3 92 21 0

21579620 Smith, George L FFS 48 M 7-5 5.559 12 $64,952 $52,006 8 20 67 1 180 49 1

16000599 Smith, George C FFS 68 M 9-6 8.694 12 $20,193 $81,342 9 29 4 2 0 3 0

04882818 Smith, Jane J FFS 48 F 9-4 7.016 12 $160,815 $65,641 10 76 7 0 113 16 0

12844850 Smith, Jane MCO 2 63 F 7-4 5.281 12 $15,467 $49,406 6 11 11 3 0 1 0

24004517 Smith, Jane FFS 47 F 7-4 6.016 12 $23,892 $56,282 5 16 5 0 33 5 0

03936377 Smith, Jane R MCO 2 68 F 7-3 4.523 9 $15,547 $42,314 5 6 4 15 0 4 0

61733632 Smith, Jane S MCO 1 4 F 5-1 0.110 12 $750 $0 6 12 6 0 15 1 15

16330566 Smith, George T FFS 64 M 7-5 6.732 12 $25,824 $62,981 6 18 8 0 79 6 3

32006563 Smith, George MCO 1 79 M 9-6 21.135 12 $13,705 $197,734 9 72 15 0 0 0 0

62457113 Smith, George W MCO 1 65 M 7-4 6.411 9 $208,775 $59,982 5 199 1 0 0 0 0

13697141 Smith, Jane FFS 61 F 7-5 5.780 12 $64,603 $54,079 6 37 16 5 50 23 9

49198618 Smith, George W MCO 2 47 M 7-3 6.250 9 $106,335 $58,474 15 51 23 0 112 44 0

10070720 Smith, George FFS 62 M 7-5 6.732 12 $6,326 $62,981 7 40 4 0 0 3 0

56277059 Smith, Jane S MCO 2 60 F 7-6 6.296 12 $35,568 $58,901 10 42 6 0 67 12 1



Deloitte’s BCI-DSS solution provides an innovative approach to conducting population management. It

leverages the pure clinical stratification of 3M’s CRGs and the severity adjustment to stratify the population

and to identify the appropriate level of programs and interventions required to improve the health status of

the members.

Typical DSS tools identify only high cost members as the cases that require management, i.e., high cost

procedures, etc. Some tools may promote the use of the grouper output to identify the right population, as

does our BCI-DSS solution. The primary difference between the CRGs and other groupers is that other

groupers use financial data (i.e., cost of services) as an input to the grouping process, and therefore the cost

of service variations are now part of the output, the members’ risk scores. In CRGs, the cost component is

excluded in the grouping of the member into the right status and severity, therefore making the health status

the sole driver. Cost variations are introduced post CRGs grouping.

The BCI-DSS solution approach is proactive in that it also focuses on the cause of the high cost, i.e., disease

progression and quality and utilization of service delivery. Deloitte will assist BMS’ care management

operations by providing the analyses to identify the members that will benefit the most from program and

interventions, touching the entire population through population management.

One goal of proactive identification is to identify those individuals early on in their disease state and hold

back disease progression. This will enhance the individual’s quality of life through improving the quality of

care and delivery of services, which can reduce expenditures.

To accomplish this, we leverage the clinical data model where each member is placed into a mutually

exclusive health status and severity. This data model forms the basis from which BMS can focus its efforts

on the right group of individuals that will benefit the most from care management, i.e., education programs,

disease management, case management, etc.

Through the understanding of each individual’s health status, various clinical and business analyses and

studies can be conducted to provide the picture as to the state of the population – the population’s disease

burden, disease progression, and resource consumption outliers.

The Population’s Health Status and Severity Distribution, shown in Figure 10-22, illustrate the disease status

and severity distribution. Through our experience, we know that the members who fall into the higher status

categories (e.g., 8 and 9) and higher severities in middle status categories (e.g., 5, 6 and 7), illustrated in the

red shaded cells, are the members that have progressed within their disease(s) and are having

complications that drive resource consumption (e.g., inpatient admissions, high utilization of services, etc.),

and these members usually fall into utilization management.

The appropriate members that will benefit the most from program intervention, i.e., disease or case

management, fall into the middle disease statuses and lower severity levels (e.g., 3-1/2, 4-1/2/3/4, 5-1/2/3, 6-

1/2, and 7/1), illustrated in the green and yellow shaded cells in Figure 10-22. The goal is to manage and

educate the member to hold back his /her progression into higher severities and/or movement into high

statuses.



Figure 10-22. Population’s Health Status and Severity Distribution.

The BCI-DSS applies analytical techniques can further refine the member identification process to target the

most appropriate individuals for program intervention. For example, we know that Diabetes is a disease that

is placing a large burden on the state, from Figure 10-19. The BCI-DSS tool filters could focus just on that

particular disease, shown in Figure 10-23. The BCI-DSS tools can even break that down by what

subpopulation is affected the most, i.e., FFS, MCOs, etc., enabling BMS with the information that could drive

decision making.

Figure 10-23. Population’s Health Status and Severity Distribution for Diabetics.

Analyzing Figure 10-23 shows that 46 percent of the Diabetic population falls into the lower severity levels

and early onset of their disease and may be ideal candidates for either some level of Diabetes disease

management or for participation in an educational program to get them on track to manage their disease.

In addition to the above query and report types, the BCI-DSS tool includes the COGNOS’ SPSS analytical

modeling module. This will enable care managers and medical management users with the ability to build out

predictive analyses, where they can program triggers, based on historical progression trends, to continuously

scan the member’s data for various series of events that could potentially lead to health status progression

and service impacts. The BCI-DSS tool would alert users when these events are identified.

Health Status /Severity 1 2 3 4 5 6 Total

1 – Healthy 649,248 649,248

2 – Significant Acute 91,192 91,192

3 – Single Minor Chronic 54,886 5,696 60,582

4 – Multiple Minor Chronic 8,204 1,969 3,428 1,000 14,601

5 – Single Significant Chronic 153,939 23,415 10,185 2,989 1,329 708 192,565

6 – Two Significant Chronic 64,960 38,546 23,432 19,124 19,124 1,930 157,652

7 – Multiple Significant Chronic 7,236 8,197 15,966 5,101 5,101 1,491 42,680

8 – Complex Malignancies 424 1,845 2,774 3,234 3,234 9,430

9 – Catastrophic 835 2,738 2,314 2,045 2,045 1,553 10,943

WV_DW_DSS-005_3

Health Status /Severity 1 2 3 4 5 6 Total

1 – Healthy

2 – Significant Acute

3 – Single Minor Chronic

4 – Multiple Minor Chronic

5 – Single Significant Chronic 7,124 1,877 708 7 386 10,102

6 – Two Significant Chronic 11,333 6,500 5,250 3,717 3,717 573 33,271

7 – Multiple Significant Chronic 4,488 4,926 8,991 3,219 3,272 1,135 26,031

8 – Complex Malignancies

9 – Catastrophic



The power of using the BCI-DSS’s clinical and business data analyses, along with the various ways to filter

and drilldown the queries, will provide the most intelligent information to the care and medical managers and

program decision makers. All queries and drilldowns would lead to the lowest level of detail, the individual

member’s PHR.

Care Management Program Performance Monitoring

Supporting care management program activities, BMS’ internal and external outsourced programs, the BCI-

DSS tool can monitor performance. The data warehouse can capture the program enrollment information,

which will become a filter in any of the member analyses. This will enable BMS care managers and program

managers to monitor the performance of the programs to hard outcomes. For example, if BMS contracts with

an MCO to develop a disease management program, BMS’ program oversight could have the ability to

monitor performance to outcomes. These outcomes could include, but not limited to quality of care (i.e., gaps

in care, preventable events, etc.), utilization of services and delivery-of-care patterns as well as health status

progression. The BCI-DSS tool can also be used to either identify the appropriate members for enrollment

into the various programs or could monitor the efficiency of the program’s selection methods.

The goal of care management programs is to improve the health status of the members and hold back the

progression, which can be monitored over time by benchmarking enrolled members to the overall population

in similar disease categories but not enrolled. This begins to form the bases of various cohort studies.

The information obtained from the BCI-DSS solution can be used to negotiate program contracts, as it could

form the basis to establish outcomes based performance service level agreements (SLAs).

Program Management

Program Management is the heart of the Medicaid enterprises. It establishes the strategic direction, defines

policies, monitors activities and provides oversight of all operations. This business area includes a wide

range of planning, analysis, and decision-making activities and depends heavily on access to timely and

accurate actionable information.

The analytics will focus on service utilization, financial and budget analysis, quality and performance analysis

and monitoring and outcomes analysis that will provide the means to perform benefit plan design, rate

setting, healthcare outcome targets, and cost-management decisions.

The BCI-DSS solution will provide the underlying analytics to deliver intelligent information to the users to

make informed decisions. In addition to the analytics discussed above in the Care Management section, this

section will demonstrate the ability to analyze impacts and monitor performance at the provider/program view

as well as establishing information to guide operations, i.e., rate setting, policies making, etc.

The core program management need of any Medicaid agency is insight into the financial picture—what is

driving cost and use trends? Are we tracking to budget? Are we paying our providers appropriately? What is

the likely result of a change in benefit, eligibility, or payment policy? Many systems will provide lists of claims

for summing costs; the BCI-DSS solution does much more by showing the reasons for these cost trends. It is

designed to return accurate information that is well documented and defendable. The financial reporting

capabilities build on the data foundation stored in the data warehouse, which has (i) been carefully validated

and matched to the source data and (ii) adjusted details are carefully managed so that net pay and services

are accurate and consistent.



Deloitte has designed the BCI-DSS analytics to view the aggregate overall population view, allowing analysts

and decision makers the ability to drilldown and segregate the information for subpopulations. As described

in the operational management section above, the BCI-DSS analytics presents a view of the overall

population with breakdowns by FFS and MCOs, shown in Figure 10-24.

Figure 10-24. Comparison of Subpopulations to the Total Population.

Figure 10-24 shows the financial impacts by entity, which can then be further analyzed to measure sums,

rates and ratios (i.e., per member, PMPM, per 1000, per admit, etc.). The following analyses demonstrate

the detailed elements (i.e., service utilization, resource consumption and costs) captured at the member

level, which can be used to aggregate at various defined grouping levels. Figure 10-25 shows a sample of

the aggregate financial elements processed from the claims detail.

Figure 10-25. Member Financial Analysis.

While this reports views the costs at an aggregate level, i.e., IP costs, outpatient (OP) costs, Rx costs, etc.

the details under each are captured in the data elements. During the data processing activities, to load data

into the data warehouse, we capture costs by various Type-of-Service as well as Site-of-Service levels,

distinguishing between in-network and out-of-network.

WV_DW_DSS-064


(PMPM)

Proj Cost

(PMPM)

Total 331,318 7 1.124 112.380 $531 $846

FFS 218,301 8 1.562 102.920 $762 $1,250

MCO 1 113,017 7 0.277 9.46 $565 $1,169

Member ID Name

Pro

du

ct

Ag

e

Se

x

CR

G S

/S

Dis

ea

se

Bu

rde

n

MM

Co

st

(PM

PY

)

Pro

jec

ted

Co

st

(PM

PY

)

IP-T

ota

l C

ost

(PM

PY

)

OP

-To

tal

Co

st

(PM

PY

)

OP

-Su

rg C

os

t

(PM

PY

)

OP

-La

b C

os

t

(PM

PY

)

OP

-Rad

Co

st

(PM

PY

)

OP

-Th

er

Co

st

(PM

PY

)

Rx

Co

st

(PM

PY

)

30494926 Smith, Jane N FFS 15 F 8-4 4.649 10 $405,168 $0 $163,170 $100,983 $0 $3,417 $426 $126 $141,015

62449682 Smith, Jane M FFS 1 F 6-4 3.201 6 $404,099 $29,949 $122,058 $282,041 $0 $75 $0 $0 $0

22184907 Smith, George L FFS 19 M 5-4 5.200 10 $364,781 $123,499 $0 $530 $0 $61 $118 $32 $364,250

62430785 Smith, Jane R FFS 1 F 6-5 3.697 1 $356,486 $34,591 $352,415 $1,910 $0 $25 $842 $0 $2,160

15856365 Smith, George V FFS 24 M 9-4 6.970 9 $356,312 $65,207 $0 $5,992 $0 $0 $2 $0 $350,320

24459233 Smith, George D FFS 18 M 6-3 4.643 12 $329,261 $43,437 $0 $2,484 $0 $61 $41 $57 $326,777

19153618 Smith, George

W MCO 1 39 M 6-1 2.253 12 $321,227 $21,081 $0 $3,060 $0 $276 $1,767 $0 $318,167

28837451 Smith, George D MCO 1 46 M 9-5 9.330 9 $272,334 $87,293 $0 $440 $0 $350 $0 $0 $271,894

61662138 Smith, Jane J FFS 3 F 9-6 21.135 8 $253,600 $197,734 $113,616 $119,963 $359 $100 $153 $350 $20,021

03381952 Smith, George E FFS 46 M 9-6 21.135 12 $248,987 $197,734 $244,764 $4,223 $132 $11 $261 $0 $0

62465735 Smith, George A MCO 1 1 M 5-5 1.119 8 $247,292 $10,467 $246,602 $595 $0 $0 $0 $0 $95

45221942 Smith, George P MCO 1 17 M 6-1 3.467 12 $244,367 $32,438 $0 $237,126 $0 $0 $0 $0 $7,241

27789314 Smith, George P FFS 22 M 9-4 10.139 12 $240,332 $94,855 $166,505 $58,347 $0 $797 $1,723 $0 $15,480

02342989 Smith, Jane L FFS 41 F 6-5 4.182 12 $239,519 $39,122 $227,595 $11,246 $0 $1,497 $972 $0 $678

59705883 Smith, Jane MCO 2 19 F 9-4 10.139 5 $238,431 $94,855 $188,360 $48,685 $0 $125 $1,099 $0 $1,386

03864271 Smith, George D MCO 2 29 M 9-2 8.700 12 $232,680 $81,390 $7,032 $213,998 $0 $193 $1,384 $0 $11,650



The BCI-DSS financial analytics will support the actuarial analysis and reporting by leveraging the clinical

risk information and actual and predictive cost information. Historical trends in cost, utilization and risk by

group, combined with predictive capabilities, make this a very powerful tool for actuaries. The BCI-DSS

analytics will support risk profiles, including impact on risk and cost using the following data elements: SIC

codes, age/gender, policy age, medical cost trend, member enrollment and diagnosis/severity of illness. The

BCI-DSS solution will provide the ad hoc reporting capabilities to view the information in various dimensions.

The underlying clinical power of using 3M’s CRGS is setting and reviewing payment rates. For payments,

particularly capitation rates, to be equitable they should reflect the clinical needs of the covered population. A

provider who delivers low cost care may not really be a low cost provider if its costs are controlled by

avoiding risk rather than by delivering services efficiently. Indeed, the provider may actually be inefficient and

rely on favorable risk selection rather than clinical oversight to contain costs. With CRGs, BMS can review

provider performance after accounting for risk selection. You can identify efficient providers and you can offer

equitable payments. Detailed provider analytics will be discussed in the following section of this proposal.


Program Integrity Management is a business area that is in its infancy stage. It will continue to mature as

agencies increase access to clinical data to improve the capability to identify program abuse cases.

Currently, this business area primarily focuses on the utilization of Surveillance and Utilization Review (SUR)

activities and program compliance to include auditing and tracking medical service necessity,

appropriateness of care delivery, quality of care, fraud and abuse, and erroneous payments.

The BCI-DSS solution will provide information about individual providers and members and identify different

types of service, cost and quality outliers (both high and low) that will require a more detailed analysis and

drill down into the root causes. These actions will form the basis to develop cases, investigate activities,

establish interventions, report on findings and resolve cases. The BCI-DSS solution will provide the means to

move traditional retrospective SUR activates to a concurrent and prospective means of analysis as our

solution includes the COGNOS SPSS analytical and predictive modeling module.

Deloitte’s BCI-DSS solution approach is a combination of surveillance and utilization review and fraud and

abuse detection. The data warehouse will store the vast amounts of data that can be used to develop

normative service delivery patterns to benchmark current activities to identify and isolate suspicious practice

patterns. Our approach begins with the analysis of providers to identify outliers of both over- and under-

utilization of services, quality of services, service/drug prescription and referral patterns, and billing practices.

The BCI-DSS tool will provide the means to analyze providers for these various service patterns while also

providing the means to drilldown to investigate the outliers in order to establish causes. Use of exception

profiling as a starting point for case development is a viable technique in the detection and control of fraud

and abuse. For example, in our designed provider analytical analyses, program integrity (PI) managers will

have access to easily identify outliers, shown in Figure 10-26.



Figure 10-26. Provider Utilization Analysis.

In Figure 10-26, it is identified by provider the services and utilization provided to their patient panels. This

can be used to compare service utilization against the aggregate as well as peers. For example, this report

shows that Dr. Robert Johnson has 3 patients in his panel with an average disease burden of 1.977. His

panel had an average of 8 emergency room visits and 1 office visit for the last 12 month period. This would

flag the question why do his patients go so often to the emergency room and why is the number of office

visits so low, when noticing that he has a sicker population than most of his peers. This information would

flag the need to drilldown further and view this provider’s performance and view his patients PHR to identify

further investigative needs.

Figure 10-27, takes a similar approach except that it looks at the financial analysis to identify cost outliers.

Total Providers Panel Avg DB

Avg PI Risk Score

IP Admits

P1kMPY

IP Avg LOS

ER Visits

(PMPY)

Office Visits

(PMPY)

Referrals

(PMPY)

Lab Tests

(PMPY)

Rad Units

(PMPY)

Ther Units

(PMPY)

106 203 0.733 7 162.6 0.4 0.7 0.2 .08 2.9 1.2 1.1

Provider ID Provider Name Panel Avg DB PI

Risk Score

IP Admits

P1kMPY

IP Avg LOS

ER Visits

(PMPY)

Office Visits

(PMPY)

Referrals

(PMPY)

Lab Tests

(PMPY)

Rad Units

(PMPY)

Ther Units

(PMPY)

143303 Hancock, Joan K 7 0.298 8 142.9 0.4 0.9 1.0 2.1 0.9 1.7 0.1

146844 Harris, Rosemary A 6 0.536 6 0.0 0.0 0.0 1.2 0.0 1.8 0.0 7.7

150251 Hancock, Joan P 1 1.602 5 0.0 0.0 0.0 2.0 0.0 0.0 3.0 0.0

153545 Hancock, Joan J 4 1.130 7 0.0 0.0 0.5 0.0 0.0 18.0 14.8 0.0

142446 Johnson, Robert D 1 0.186 4 0.0 0.0 0.0 7.0 0.0 0.0 0.0 0.0

151397 Hancock, Joan L 1 3.586 11 0.0 0.0 3.0 2.0 3.0 6.0 6.0 3.0

153519 Hancock, Joan R 5 0.111 7 0.0 0.0 0.6 0.4 0.0 3.0 1.2 0.2

146668 Johnson, Robert L 1 0.077 12 0.0 0.0 1.0 4.0 2.0 9.0 0.0 0.0

140333 Hancock, Joan R 1 0.236 10 0.0 0.0 3.0 1.0 0.0 4.0 1.0 0.0

146318 Johnson, Robert J 1 1.603 13 0.0 0.0 1.0 3.0 0.0 3.0 1.0 0.0

146603 Hancock, Joan L 6 4.536 18 166.7 1.0 0.8 3.0 2.0 0.7 1.3 0.0

152220 Johnson, Robert C 7 0.105 3 428.6 2.0 0.9 0.0 0.0 1.3 0.4 0.0

145671 Hancock, Joan L 9 0.464 4 111.1 1.1 2.4 1.1 1.0 1.6 0.6 0.9

146910 Harris, Rosemary S 3 0.801 7 100.0 1.3 0.7 0.0 1.0 6.0 2.7 0.0

150310 Johnson, Robert H 1 0.841 6 0.0 0.0 2.0 0.0 0.0 7.0 4.0 0.0

143792 Johnson, Robert P 3 1.977 12 333.3 0.3 8.0 1.0 0.3 2.7 3.7 0.0

146378 Johnson, Robert M 1 0.920 8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

142647 Hancock, Joan H 5 0.276 3 0.0 0.0 0.2 0.8 1.0 1.4 0.2 0.0

146216 Johnson, Robert K 4 1.258 12 0.0 0.0 1.3 0.0 0.3 1.3 2.5 0.0

153674 Harris, Rosemary J 6 0.378 10 166.7 0.8 1.0 0.0 1.2 4.0 2.2 0.0

145062 Johnson, Robert E 3 1.174 9 0.0 0.0 0.7 0.0 0.0 3.7 0.0 0.0

151482 Johnson, Robert M 1 0.236 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0



Figure 10-28. Provider Financial Analysis.

In Figure 10-28, it shows the total cost of delivering care by provider to the patient panels. In this analysis it

shows that overall average disease burden is .733 with an average total cost of $2,314 PMPY. When

comparing the detailed list of providers, it is easy to see the outliers that may require further investigation

regarding the reason that their costs are so high. Users can view the disease burden, which is a case mix

and severity adjusted index, to compare performance patterns. For example, Dr. Rosemary Harris has 6

patients in her panel with an average disease burden of .536, healthier than the population average of .733.

Her cost for care delivery is almost 7.5 times higher than average (i.e., aggregate benchmark). This would

raise the question why her costs were so high compared to her peers when her patients are not sicker.

Further investigation into the reasoning could be accomplished by drilling down into her patients’ PHR to

determine the cause.

The detailed analysis required to focus efforts in program integrity is to be innovative in the development of

various reports. While PI Programs look at outliers, PI managers must be able to not only identify high

outliers but also the low, the underutilization of services that are affecting the quality of care of the members.

For example, the implementation and expansion of MCOs in state health care programs have forced

changes in the performance of utilization control and program integrity. The financial incentive for abuse of

the Medicaid program in FFS is for overutilization of services to increase payments; whereas in MCO, it

changes to underutilization of service. The financial incentive for the MCOs or individual providers who are

primary care case managers is to provide less service since their payments are not based on the number of

services provided. This has also changed the focus of utilization review since service providers are now

motivated to withhold services (and thus reduce costs) rather than to over use them as in traditional FFS.

Total Providers Panel Avg DB

Total Cost (PMPY)

IP Cost (PMPY)

OP - Total Cost

(PMPY)

OP - RX Cost

(PMPY)

OP - Lab Cost

(PMPY)

OP - Rad Cost

(PMPY)

OP - Ther Cost

(PMPY)

106 203 0.733 $2,314 $0 $1,761 $553 $22 $93 $45

Provider ID Provider Name Panel Avg DB Total Cost

(PMPY) IP Cost (PMPY)

OP - Total Cost

(PMPY)

OP - RX Cost

(PMPY)

OP - Lab Cost

(PMPY)

OP - Rad Cost

(PMPY)

OP - Ther Cost

(PMPY)

142446 Johnson, Robert D 1 0.186 $209 $0 $0 $209 $0 $0 $0

143303 Hancock, Joan K 7 0.298 $172 $0 $154 $17 $4 $36 $6

146668 Johnson, Robert L 1 0.077 $0 $0 $0 $0 $0 $0 $0

146844 Harris, Rosemary A 6 0.536 $17,320 $0 $16,612 $708 $20 $0 $381

150251 Hancock, Joan P 1 1.602 $4,127 $0 $158 $3,969 $0 $130 $0

151397 Hancock, Joan L 1 3.586 $22,571 $0 $17,522 $5,049 $67 $1,244 $191

153519 Hancock, Joan R 5 0.111 $662 $0 $620 $42 $10 $495 $0

153545 Hancock, Joan J 4 1.13 $14,767 $0 $14,569 $198 $113 $1,392 $0

140333 Hancock, Joan R 1 0.236 $99 $0 $99 $0 $74 $0 $0

143792 Johnson, Robert P 3 1.977 $2,991 $0 $1,473 $1,519 $0 $118 $0

145671 Hancock, Joan L 9 0.464 $2,009 $0 $1,319 $690 $18 $119 $67

146318 Johnson, Robert J 1 1.603 $3,523 $0 $597 $2,927 $30 $114 $0

146378 Johnson, Robert M 1 0.92 $898 $0 $285 $614 $0 $0 $0

146603 Hancock, Joan L 6 0.536 $436 $0 $417 $19 $7 $208 $0

146910 Harris, Rosemary S 3 0.801 $5,102 $0 $4,979 $123 $133 $82 $0

150310 Johnson, Robert H 1 0.841 $2,745 $0 $1,854 $891 $54 $846 $0

152220 Johnson, Robert C 7 0.105 $31 $0 $31 $0 $10 $0 $0

141432 Johnson, Robert A 1 2.386 $8,508 $0 $6,776 $1,732 $39 $0 $0

141808 Johnson, Robert R 1 0.106 $0 $0 $0 $0 $0 $0 $0

142647 Hancock, Joan H 5 0.276 $1,656 $0 $1,548 $108 $14 $0 $0

145062 Johnson, Robert E 3 1.174 $3,231 $0 $540 $2,691 $41 $0 $0

145546 Johnson, Robert A 3 0.083 $3 $0 $0 $3 $0 $0 $0



During the business requirements gathering phase of the DW/DSS implementation, we will work with the

Program Integrity program and identify the requirements to develop innovative approaches to query the data

and generate the analytics to easily identify outliers. We will create the analytics to identify variation in

delivery patterns and billing activities and program them into the BCI-DSS SPSS module. This will leverage

the power of the designed solution to actively and continuously identify outliers.

Documents

Business Needs Section 10