KSA IT Insights - Shotgun Wedding

JANUARY 2003

A SHOTGUN WEDDING | BUSINESS DECISION SUPPORT MEETS CLINICAL DECISION SUPPORT

FOREWORD

A ROSE BY ANY OTHER NAME MAY STILL BE A ROSE, BUT DECISION-SUPPORT

SYSTEMS BY ANY OTHER NAME ARE MOST LIKELY KISSING COUSINS AT BEST.

ON ONE SIDE OF THE FAMILY FENCE ARE THE VARIOUS BUSINESS DECISION

SUPPORT SYSTEMS THAT SUPPORT BUDGETING, EXECUTIVE DECISION-MAKING,

FINANCIAL ANALYSIS, QUALITY MANAGEMENT, AND STRATEGIC PLANNING, TO

NAME BUT A FEW. On the other side of the fence are the evolving clinical decision support

systems that support results reporting, pharmaceutical ordering and dispensation, differential

diagnoses, real-time clinical pathways, dynamic literature research, and clinical alerts. These

two types of decision support systems, business and clinical, differ significantly both in intent

and content, but are all too often incorrectly referenced interchangeably.

The purpose of this paper is twofold. The first is to define the high-level delineation

of the very different intent, content, and methods of the business and clinical decision-making

functions. As form must follow function, the information technology and methodologies for

data modeling, management, and presentation to the user also differ for business and clinical

decision-making. While the intent, content, and methods may differ, there are still many common

elements of the two decision-support approaches that can be shared to great benefit.

The concepts of commonality and sharing lead us into the second purpose of this paper.

When clinical decision support (CDS) is integrated with business decision support (BDS), a

marriage occurs that is mutually beneficial. This marriage is not an easy matter, and will

occur and succeed only under pressure of well-planned integrated data and decision support

system strategies.

KURT SALMON ASSOCIATES | INSIGHTS, JANUARY 2003

THIS ARTICLE WAS ORIGINALLY PUBLISHED IN THE FALL 2002 ISSUE OF THE

JOURNAL OF HEALTHCARE INFORMATION MANAGEMENT PUBLISHED BY THE

HEALTHCARE INFORMATION AND MANAGEMENT SYSTEMS SOCIETY. IT IS

REPRINTED WITH PERMISSION.

A B O U T T H E A U T H O R

JASON OLIVEIRA, MBA, IS A HEALTHCARE DECISION SUPPORT SPECIALIST AND

MANAGER WITH THE HEALTH CARE CONSULTING GROUP OF KURT SALMON

ASSOCIATES. FOR MORE INFORMATION, PLEASE EMAIL JASON AT

[email protected].

CONTENTS

A SHOTGUN WEDDING: BUSINESS DECISION SUPPORT MEETS CLINICAL DECISION SUPPORT

SECTION ONE

A DECISION-MAKING MODEL

The core objective of both clinical and business decision-support systems is to enhance

a decision-making process. Their differences are clear, yet widely misunderstood.

SECTION TWO

THE DECISION-SUPPORT ARCHITECTURE

Bringing the clinical and business decision-making processes together requires a

sophisticated architecture. This architecture must include acquisition, organization and data

exploitation functions. These functions are driven by Computer-based Patient Record,

data warehousing, and the clinical data repository.

SECTION THREE

LET’S HAVE A SHOTGUN WEDDING

Various data exploitation tools deployed to decision makers produce a decision loop of the

business and clinical decision-support systems creating improvements in the decision-

making process.

1

5

13

OVERVIEW

THE CHANGE IN ORGANIZATIONAL CULTURE AND THE REDESIGN OF BUSINESS

AND CLINICAL PROCESSES THAT ALLOW THE USE OF EMPOWERED DECISION-MAKING

TOOLS IS BY FAR THE MORE DIFFICULT TASK FACING INFORMATION TECHNOLOGISTS

AND ORGANIZATIONAL LEADERS. KSA THOUGHT P IECE , “A SHOTGUN WEDDING:

BUSINESS DECIS ION SUPPORT MEETS CL INICAL DECIS ION SUPPORT,” OFFERS A

GLIMPSE OF HOW A GREAT BENEFIT OCCURS DESPITE THE VERY DIFFERENT

INTENT, CONTENT, AND METHODS OF THE BUSINESS AND CL INICAL DECIS ION-

MAKING FUNCTIONS. By effectively closing the loop between the data, analytics,

processes, and methods supporting business and clinical decision-making, a health care

organization closes the loop between its knowledge generation activities and its actions

at the bedside: knowledge guiding actions, actions generating knowledge.


1

THE DECISION-MAKING MODEL IS COM-

PRISED OF FIVE STEPS: 1) INTELLIGENCE

GATHERING, 2) DEVISING SOLUTION ALTER-

NATIVES, 3) CHOOSING THE BEST SOLU-

TION, 4) IMPLEMENTING THE SOLUTION,

AND 5) EVALUATING ITS EFFECTIVENESS.

Intelligence gathering denotes situa-

tional fact finding in order to better define

what is happening. The description of what

is happening will coalesce into the design

of a concrete issue that requires one or

more decisions to be made. The heart of

decision-making is to then devise, evaluate,

and choose from numerous alternative

solutions the one that best addresses the

formulated issue. With presumably the best

alternative solution in hand, then implement

it in the hopes to positively address the

issue. Finally, in order to improve the quality

of future decisions, evaluate the results of

the decision to assess how well it

addressed the issue at hand.

This decision-making model remains

the same whether you are deciding whether

to acquire a community hospital (i.e., BDS),

or you are deciding on the best therapeutic

regimen for a cancer patient (i.e., CDS).

What does differ between the two types of

decisions under discussion are the charac-

teristics of their decision-making processes

within the model. These differing process

characteristics include temporal use, goal-

orientation, and the level of structure

involved in business and clinical decision-

making.

AS THE GOAL OF BOTH BDS AND CDS IS TO ENHANCE A DECISION-

MAKING PROCESS, A MODEL OF THAT PROCESS WILL FACILITATE THE

DISCUSSION OF THEIR DIFFERENCES. AT ITS MOST BASIC LEVEL, A

DECISION IS A CHOICE BETWEEN ALTERNATIVE COURSES OF ACTION DEALING

WITH AN ISSUE.

A DECISION MAKING MODEL

The heart of decision-

making is to then devise,

evaluate, and choose

from numerous alternative

solutions the one that

best addresses the formu-

lated issue.

T H E C L I N I C A L D E C I S I O N -

M A K I N G P R O C E S S

The typical clinical decision-making

process, not the overarching decision-making

model, differs significantly from the typical

business decision-making process. Clinical

decisions by their nature are real-time and

are often performed at the point of care. A

patient presents indications, and a series

of decisions need to be made now to save

a life, alleviate the symptoms, and cure the

underlying disease/condition.

Clinical decisions are specifically

goal oriented, that is, a cure and/or the

alleviation of symptoms are sought. The

clinical goal is first reached through intelli-

gence gathering and the making of a diag-

nosis, often using Bayesian probability.

Simply stated, Bayesian probability

strengthens the determination of a disease

as the symptoms that are most probably

present given a disease are determined

through evaluation and diagnostic testing.1

The clinician approximates the probabilities

of symptom/sign and disease combina-

tions through an understanding of the

underlying physiology, experience with pre-

vious similar cases, and literature review of

similar cases. An intelligence gathering

process called case-based reasoning.

The clinical decision-making process

can be characterized as being very structured

and goal oriented within a real-time clinical

context, which is significantly different then

the characterization of the business decision-

making process.

T H E B U S I N E S S D E C I S I O N -

M A K I N G P R O C E S S

The issues and problem solving process of

a health care business strategist presented

with the desirability of a business goal differ

from those of the clinician presented with a

sick patient. For the purposes of this paper,

a healthcare business strategist encom-

passes all decision processes other than

direct patient-care delivery, even if clinical in

nature. These business decision processes

include strategic planning, budgeting, and

financial analysis as well as, quality man-

agement programs, clinical process

improvement, and clinical benchmarking.

Business decision-making can occur at a

strategic, tactical, and operational level.

This paper addresses the information and

system needs of strategic and tactical deci-

sion-making only. Applications supporting

daily operational decisions and processes

such as ADT, registration, scheduling, and

patient billing are not included in our con-

sideration. Clinical decisions, by definition,

are operational in nature.

What remains for our consideration

are business decisions that are batch ori-

ented in nature. That is, business problems

which are addressed occasionally and not

real-time. The business decision is not

concerned with a singular element, such as

a sick patient, but large aggregations of

many elements that address an ill-defined

problem such as how can costs be

reduced, or clinical outcomes improved.

The aggregation of data is largely for the

purposes of intelligence gathering, as

opposed to the purposes of addressing an

already known specific goal. These charac-

teristics make the business decision-making

process unstructured, goal searching, and

long range in nature. Figure 1 summarizes

the characteristics of the business and

clinical decision-making process.


2

Bayesian probability

strengthens the determi-

nation of a disease as

the symptoms that are

most probably present

given a disease are deter-

mined through evaluation

and diagnostic testing.1


3

C H A R A C T E R I S T I C

TEMPORAL USE

GOAL ORIENTATION

STRUCTURE OF DECISION

B U S I N E S S

■ Retrospective, batch,long-range

■ Unspecified intelligencegathering, goal searchingoriented

■ Unstructured

C L I N I C A L

■ Real-time, case based,operational

■ Specified goal seeking

■ Very structured,Bayesian

Source: KSA Analysis

F I G U R E 1D E C I S I O N - M A K I N G P R O C E S S C H A R A C T E R I S T I C S Clinical decisions, by defi-

nition, are operational in

nature.


4


5

THE FUNCTIONALITY DELIVERED BY THE

DECISION-SUPPORT ARCHITECTURE, AS

DEPICTED IN FIGURE 2, SHOULD INCLUDE

THE FOLLOWING:

■ ACQUISITION. The means to acquire data

from the numerous internal operational

information systems supporting the real-

time clinical, financial and administrative

processes. Also to be included is the

acquisition and integration of external data

sources such as supplied by data vendors,

state and federal based data agencies,

best-practice sources, and by the organiza-

tion’s external business partners.

■ ORGANIZATION. The ability to efficiently

model, store and retrieve the data with

applied business and clinical rules and

semantics at both a logical data model

and physical database layer. ■ EXPLOITATION. The various retrieval,

reporting, analysis and decision support

tools used to derive and deliver informa-

tion from the acquired and organized data.

These three functions have been

addressed by various health care informa-

tion technology initiatives. These initiatives

include the Computer-based Patient Record

(CPR)3, data warehousing, and the clinical

INTEGRATING AND MANAGING THE CLINICAL AND BUSINESS DECISION-

MAKING PROCESSES OF A HIGHLY DIVERGENT REPUBLIC OF PROFESSIONAL

DISCIPLINES REPRESENTED IN EVEN THE SMALLEST OF HEALTH CARE

ORGANIZATIONS REQUIRES A ROBUST AND SOPHISTICATED DECISION-

SUPPORT ARCHITECTURE.2 AT ITS MOST BASIC LEVEL, EMPOWERING THE CLINICAL

AND BUSINESS DECISION-MAKERS OF THE ORGANIZATION INVOLVES THE

ACQUISITION, ORGANIZATION AND EXPLOITATION OF HIGH QUALITY INFORMATION AT

THE RIGHT TIME, THROUGH THE RIGHT MEDIUM, AND TO THE RIGHT DECISION-MAKER.

THE DECISION-SUPPORTARCHITECTURE

At its most basic level,

empowering the clinical

and business decision-

makers of the organization

involves the acquisition,

organization and exploita-

tion of high quality infor-

mation at the right time,

through the right medium,

and the right decision-

maker.


6

data repository (CDR). The CPR is an

over-arching vision that includes all the

elements of capturing, storing, processing,

communicating, and presenting patient

record information and related data and

knowledge bases. Supporting the data

and knowledge base of the CPR vision

are both data warehousing and the clinical

data repository.

T H E D A T A W A R E H O U S E

Data warehousing is an old concept that

has taken on new strategic implications

within the health care industry. A data ware-

house is, simply stated, the physical and

logical separation of a health care institu-

tion’s operational data systems and its ret-

rospective analytical decision-support activi-

ties. The fundamental requirements of the

operational and analytical decision support

systems are very different. The operational

information systems need peak perform-

ance for a set of small structured real-time

transactions. Whereas, the analytical deci-

sion support system needs flexibility and

broad scope for yet to be defined retro-

spective analytical needs. It is undesirable

to have retrospective analysis interfere with

and degrade the performance of opera-

tional systems. The primary concept of

Decisio

nS

upport

To

ols

Extra

ct&

Tra

nsf

or

m

Internaland

externalsystems

Datawarehouse

Outcomes

Planning

Finance

QualityIndicators

Clinicaldata

repository

■ Planning/ marketing

■ Research

■ Performance evaluation

■ Outcome/ disease management

■ Finance plus more


F I G U R E 2D E C I S I O N - S U P P O R T A R C H I T E C T U R E

A data warehouse is, simply

stated, the physical and

logical separation of a

healthcare institution’s

operational data systems

and its retrospective ana-

lytical decision-support

activities.

A C Q U I S I T I O N O R G A N I Z A T I O N E X P L O I T A T I O N

data warehousing is to most effectively

access data stored for business and clinical

analysis by separating and integrating it

from the data in numerous internal and

external operational information systems.

Data warehouses are most successful

when data is integrated from more than

one operational system as well as with

external market, benchmarking, and com-

petitor data sources. Another key attribute

of the data in a data warehouse is that it

has become mostly non-volatile. This

means that after the data is loaded into the

data warehouse, there are little to no mod-

ifications made to this information. While

an ICU monitoring system, an operational

clinical system, can capture and trend

blood pressure readings continuously, it

would only be desirable to capture, for busi-

ness analysis, the admission and dis-

charge BP measures of a patient.

The two remaining key attributes of a

data warehouse are its logical and physical

data models. The warehouse logical data

model aligns with the analytical, versus

operational, data needs of the health

organization. The data entities defined and

maintained in the data warehouse parallel

analytical entities such as product lines,

catchment areas, clinical services, provider

groups, referral sources, costs, and profits.

This is as compared to operational data

models that contain entities designed for

processes such as charge posting, ordering,

resulting, discharging, and cash posting.

At a physical level, the warehouse is

designed to efficiently deliver information for

analytical purposes, versus operational trans-

action processing purposes. This efficiency is

gained through the use of several techniques,

among which include the de-normalization,

aggregation, hyper-indexing, and standardiza-

tion of data. These data transformation tech-

niques allow, for example, the simple and fast

identification of all Medicare patients in a

health network for the past ten years no

matter which of dozens of Medicare insur-

ance codes were used in five different oper-

ational billing systems.

As evidenced above, business data

analysis has a need for a huge breadth and

depth of data — and not just data, but

information. Turning data into information

involves reorganizing operational data,

deriving new data, integrating disparate

data, and delivering information to busi-

ness decision-makers through various data

exploitation tools to be discussed later.

Conversely, the needs of clinicians delivering

real-time clinical care to patients require

structured, defined, goal-oriented support

from clinical decision-support systems. As

the business decision-support system is

built on the informational foundation of a

data warehouse, so is the clinical decision

support system built on the foundation of a

clinical data repository.

T H E C L I N I C A L D A T A

R E P O S I T O R Y

Clinical professionals, information officers,

and medical informaticians have differing

notions of what clinical data repositories

should do and how they differ from other

types of databases, namely the business

data warehouse described above. Depending

on whether health care organizations are

trying to support real-time clinician needs or

strategic and research objectives, two very

different types of databases are required.

Clinical data repositories are designed for

immediacy and support real-time, struc-

tured, integrated clinical decision-support.

Data warehouses are designed to support

batch, retrospective, and unstructured busi-

ness decision support, including clinically

oriented business decisions.4 All to often

what is really a data warehouse is


7

Data warehouses are

most successful when

data is integrated from

more than one opera-

tional system as well as

with external market,

benchmarking, and com-

petitor data sources.

described as a clinical data repository, or

vice versa, a clinical data repository is

claimed to be able to effectively support

analytical decision making.

A CDR is a complementary technology

for a Computer-based Patient Record. The

CDR can be viewed as a patient-focused

clinical data store for the CPR. The CDR is

designed to provide the clinical view of a

patient to a clinician in real-time to support

clinical decision-making. The CDR consoli-

dates and integrates the disparate sources

of operational clinical data that reside in

laboratory, radiology, ambulatory care,

dietary, and numerous other clinical infor-

mation systems. Presenting to the clinician

at the bedside, the whole clinical picture of

the patient under their care.

The field of medical informatics fos-

tered in the world’s academic medical cen-

ters is creating the infrastructure to realize

the CDR, and through its application, the

CPR. The CDR is the culmination of years of

research developing the components

required to build it. These components

include the structured medical vocabulary

systems such as ICD-9-CM, CPT4,

SNOMED, Arden Syntax, Medical Logic

Modules, and LOINC. The components also

include the basic mechanisms of data inter-

change, which include CORBAMed, HL7,

DICOM, and ASTM protocols. Last, but likely

to be the most difficult to achieve, is the

standardization of encoding and represent-

ing medical knowledge itself, such as the

Intermed Common Model and Guideline

Interchange Format (GLIF).

The data warehouse and the clinical

data repository are, at their core, data man-

agement technologies. However, the two data

management technologies are designed to

support two very different decision-making

processes. The acquired and organized

data now needs to be exploited by decision-

makers through the use of software tools

and methods that transform the data into

actionable information.

D A T A E X P L O I T A T I O N

Data exploitation refers to the various data

retrieval, reporting, decision support, and

analysis tools used to derive and deliver

information from the acquired and organ-

ized data in the data warehouse and the

clinical data repository. These data

exploitation tools are the means through

which business analysts, operational man-

agers, and clinicians view, integrate, and

analyze the various data stores that have

been discussed above. It is through the

tools that data is transformed into action-

able information through targeted subject

specific algorithms, analysis, measure-

ment, summarization, reports, and specific

decision-support logic. ■ QUERIES. Queries are the basic mecha-

nism, typically using the Structured

Query Language (SQL), to efficiently

search and retrieve detail data from the

two organized data stores. The CDR is

optimized to answer queries that retrieve

the clinical data of a single patient. The

data warehouse is optimized to answer

queries that retrieve the data for thou-

sands of patients over numerous years.■ REPORTS. Reporting is the ubiquitous

tool of displaying detail and summarized

data both online and through printing.


8

The CDR is designed to

provide the clinical view of

a patient to a clinician in

real-time to support clinical

decision-making.

Reporting tools are typically integrated

with query tools. The later retrieves the

data, the former summarizes, formats,

and displays the data to the user. ■ ON-LINE ANALYTICAL PROCESSING (OLAP).

On-Line Analytical Processing includes

those tools that summarize data in pre-

determined manners to allow the effi-

cient navigation of that data during a

free-form data analysis session. This

capability is most commonly associated

with multi-dimensional data cubes where

data is summarized into analytical

dimensions such as fiscal period, cost

center, corporate division, budgeted and

actual expenses. The OLAP tool then

allows the user to quickly and easily ‘drill-

down’ between the data dimensions at

any level of summarization, from corporate

overview down to the cost center level. ■ DATA MINING. Data mining is the collec-

tive term of the numerous techniques

and methodologies that have found their

origin in several fields of study including

artificial intelligence, machine learning,

pattern recognition, advance statistical

modeling, and data visualization. These

fields of study have coalesced from theory

into the targeted application of modeling

techniques to the discovery of knowledge

in large databases. Data mining derives

its name from the imagery of having to

dig through gigabytes or terabytes of

‘rock’ (i.e., raw data) to find that small

nugget of actionable information ‘gold’.

The combination of modeling techniques

enables the discovery of relationships,

patterns, trends, and predictive models in

the data warehouse and clinical date

repository not easily found through tradi-

tional decision-support tools.■ DECISION SUPPORT SYSTEM (DSS).

Those routine decisions that are struc-

tured enough can be embodied in the

logic of a targeted decision support sys-

tem. Examples of these certainly include

diagnosis expert systems, clinical alerts,

and assisted prescription ordering on the

clinical decision-support systems end.

Business decision support systems

include clinical pathway development,

enterprise resource management, budg-

eting, strategic planning, and cost

accounting systems. Decision-support

systems are usually comprised of the

query, reporting, OLAP, and data mining

technologies described above. These

technologies are in a sense the develop-

ment components for an application

designed to support a specific set of

decision-making processes.


9

Data mining derives its

name from the imagery of

having to dig through giga-

bytes or terabytes of

‘rock’ (i.e., raw data) to

find that small nugget of

actionable information

‘gold’.


10


11

WHILE CONTENT AND INTENT MAY DIFFER,

THERE ARE COMMON ELEMENTS OF THE

TWO DATA AND SYSTEM STRATEGIES THAT

CAN BE SHARED. Non-volatile, historical

clinical data from a CDR can feed a data

warehouse to support an OLAP clinical-

pathway utilization tool. Cost data from a

data warehouse can feed a CDR to support

a cost-effectiveness driven case management

decision-support system. The remaining

section of this paper highlights the synergies

that can be realized from well-planned, inte-

grated data store and data exploitation

strategies.

CLOSED -LOOP DEC IS ION MAKING

The marriage of business and clinical

decision support is realized through a

decision loop that is made evident in the

various data exploitation tools deployed

to decision-makers, both business and

clinical. The decision loop refers to the

fact that decisions as recorded in a clinical

decision-support system can feed a busi-

ness decision-support system. The deci-

sions as recorded in a business decision-

support system then, in turn, can feed the

clinical system. The decision loop creates

improvements in the decisions made on

both sides of the decision process fence.

THE DATA WAREHOUSE, THE CLINICAL DATA REPOSITORY, AND THE SET

OF DATA EXPLOITATION TOOLS ARE COMPLEMENTARY INFORMATION

TECHNOLOGIES EACH DESIGNED FOR DIFFERENT DECISION-SUPPORT

NEEDS. SOME ARE FOR RETROSPECTIVE F INANCIAL, CLINICAL, AND

OPERATIONAL BUSINESS ANALYSIS. Some are for real-time, integrated delivery of

patient-centric clinical data and medical knowledge to the clinician.

The decision loop refers

to the fact that decisions

as recorded in a clinical

decision support system

can feed a business deci-

sion support system.

LET’S HAVE A SHOTGUN WEDDING


12

There are numerous examples of decision

loops that would benefit from integrated data

and application strategies. The decision loop

of case management will be discussed in

detail. Additional decision loops would

include outcomes management, strategic

planning, benefits management, capitation

management, disease management, and

contract modeling to name but a few.

C A S E M A N A G E M E N T

Because case management requires timely

access to patient data that is currently

collected and stored in many different

places by many different operational clinical

information systems across multiple settings

of care (i.e., hospitals, physician offices,

nursing home, patient’s home), case man-

agement is hastening the development of

linkages between these fragmented data

sources into the clinical data repository dis-

cussed above. Managing the effectiveness

of a case management strategy requires

the development of significant and effective

care plans and measuring compliance to

those plans. The data warehouse is in the

best position to support the analysis of

case management effectiveness across

multiple clinical services, providers, and

patients.

Strategic decisions■ Identify high cost

populations

■ Compare against regional best practice benchmarks

■ Choose a high volume population with a high variance Tactical decisions

■ Critical pathway development

■ Best practice resource utilization profile

■ Variance reporting

■ Physician reportingOperational decisions■ Critical alerts

■ Critical pathway enabled order entry/results

■ Approved formularies at prescription

■ Dynamic literature searches

Analytical information use


F I G U R E 3C L O S E D L O O P E D D E C I S I O N M A K I N G F O R C A S E M A N A G E M E N T

Managing the effective-

ness of a case manage-

ment strategy requires the

development of significant

and effective care plans

and measuring compliance

to those plans.

The decision loop for case manage-

ment, as depicted on page 12 in Figure 3,

starts at the identification of a patient

group for whom the application of case

management will result in significant

improvements in clinical and cost effective-

ness. Data mining tools can apply statisti-

cal clustering techniques against the data

warehouse to determine categories of

patients that have similar clinical indica-

tions and high costs.5 The source of the

clinical data being the clinical data reposi-

tory, and of the patient costs being the data

warehouse. Statistical regression tools of

data mining can then identify which clinical

factors are most highly correlated to high

costs. Patient age, high-blood pressure,

and pharmaceutical utilization being exam-

ples. This data then can be used to a devel-

op a cost-effective clinical pathway for this

patient group.

The clinical pathway is deployed

through a clinical decision support system

used by both clinicians and case managers.

The real-time clinical data needs of the

pathway are supported by the clinical data

repository. Furthermore, the pathway can

be integrated with the organization’s opera-

tional Order Entry and Results Reporting

application to ensure pathway suggestions

of lab tests and approved formularies are

adhered to at the point-of-care. The meas-

urement of costs, clinical outcomes, and

quality as captured by those respective

decision support systems are fed back to

the data warehouse, and now available for

aggregated clinical pathway utilization and

cost-effectiveness analysis using OLAP and

reporting tools. The decision loop is closed

as new clinical pathways are created and

existing ones improved at the retrospective

business decision-support level, and

deployed at the real-time operational clinical

decision-support level.

C O N C L U S I O N

As we always advise, data and application

strategies are only a collection of tools, it is

essential that the health care organization

is prepared to take advantage of them. The

change in organizational culture and the

redesign of business and clinical processes

that allow the use of empowered decision

making tools is by far the more difficult

task facing information technologists and

organizational leaders. A firm understanding

of business improvement methods, corpo-

rate business and clinical goals, and the

information strategies themselves is a

requirement to realize significant benefits.

But most importantly, the realization that

clinical and business processes are not

mutually exclusive, therefore, neither are

their decision-support strategies.

At no other time in the history of the

health care industry have market impera-

tives demanded the marriage of business

and clinical decision support. Clinical out-

comes research and the care delivery

process were clearly the domain of white-

coated clinicians. Cost cutting and reim-

bursement maximization were clearly the

purview of business-suited MBAs and

CPAs. The future of information technology

and its integrated application to both sides

of the decision-support fence will serve as

the proverbial shotgun to bring these two

disciplines together in marital bliss. This

marriage will not be an easy matter. It will

require a lot of marriage counseling on part

of information technologists and enlight-

ened health organization leaders, but the

result will be years of financial health and

clinical care improvements.


13

The future of information

technology and its inte-

grated application to both

sides of the decision-

support fence will serve

as the proverbial shotgun

to bring these two disci-

plines together in marital

bliss.

F O O T N O T E S1 START, State of the Art: Oncology in Europe.

www.oncoweb.com/start/chapt-05/chap5-2.htm. Section 2. Decision Theory, 1998, p. 5.2 Oliveira, J.D., and Lederman M. Decision Support and Executive Information Systems.

Advance for Healthcare Information Executives, August 1998, p. 46. 3 Dick, R.S., and Steen, E.B. (Eds.). The Computer-based Patient Record: An Essential

Technology for Health Care. Washington, DC: National Academy Press, 1991.4 Morrisey, John. Differing perceptions about CDRs complicate purchases, impede

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