Executive Summary

 

Integrated Delivery Network System for Ambulatory Electronic Medical Records

 

Company/Business Overview

 

The intention of this company is to provide an integrated delivery network for electronic ambulatory medical records using a federated virtual database methodology. This system runs on an artificial intelligence system originally developed for the department of defense by Dr. Eric Mettala. It is currently in use in the state of Connecticut for aggregating medical records form 410 disparate databases in hospitals all over the state. This technology is a result of an eleven man-year research project in the area of artificial intelligence. 

 

Dr. Eric Mettala leads a group of scientists that have developed a suite of tools known as ACT Ontology that can be used for the aggregation of information across disparate data silos and apply business intelligence functionality such as diction support systems and management reporting. The unique value proposition of this technology is an enterprise integration solution with business intelligence that does not use a data warehouse.

                

This new business model will exploit this technology to develop and sell an off the shelf enterprise solution for hospitals. This solution will aggregate medical records across disparate data sources or silos, including unstructured data, in order to make patient information available in real time to clinicians in the examining room. It will also apply that information to clinical decision support system.  The major factor that makes this solution different is the combination of the following attributes:

·        Aggregation of medical records across disparate data sources in real time.

·        Business intelligence functionality.

·        No data warehouse.

 

Basic functionality:

 

 

An object-oriented “metadata” representation can be used to support queries, browsing, and many kinds of analyses. For example, the artificial intelligence capabilities will also allow doctors, administrators, and researchers to run ad hock queries against patient information. This capability will allow users to develop corollary and causal relationships between different factors effecting patient health.

 

By enabling doctors to have access to patient information in the examining room, this can increase the number of patients that a doctor can visit by as much as 30% and at the same time increase the amount of time a doctor can spend with each patient. Because the system is intelligent it will be able to assist in the diagnosis and treatment process. ACT*Ontology will allow the doctors to access information about associated corollary factors effecting illness as well as the effectiveness of different treatment methods.

 

o       30% increase in patient visits.

o       Increase in amount of time spent with each patient.

o       Assist doctors in the process of diagnosis.

o       Correlate multiple independent patient attributes and environmental factors with probable illnesses.

o       Provide doctors with information concerning the effectiveness of treatment methods.

o       Provide hospital administrators and researchers with the cost and effectiveness of differing treatment methods.

 

o       Middleware independent of a data warehouse.

o       Reduced legal issues concerning data ownership.

o       Implementation through existing integrated development environment utilized by non-technical analysts.

o       Java based platform independent system.

o       Utilization of intranet networking capability through a web portal.

o       Intelligent translators to integrate existing databases using built in drivers.

o       Greater processing speed of any existing business intelligence or middleware system.

o       The model is non-evasive, since neither the source applications nor their data are modified by the system.

o       Provides dynamic browsing among related objects, expert system analysis, or flexible growth of the model over time.

o       Seamless integration will all known data sources.

 

 

 

 

 

 

Existing Hospital Environment

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


Complex Hospital Systems Simplified

 

The diagram above depicts many different departments or islands of data within an organization. Each departments needs to share information with many other entitles. The processes that define the relationships of these disparate entities often rely on physical documentation that must be transported from one location to another.

 

As the number of entities increase, the number of connections required to link these entities increase exponentially. Likewise the processes that govern the flow of information will grow in complexity. Even a seemingly simple process such as a doctor visit can be become very complex taking into consideration the possibility of lab tests, follow up visits, and scheduling specialists.

 

All of these factors can result in delays in the process of diagnosis and treatment. Not only will patient care suffer as a result of these inefficiencies, but costs will increase do to reduces patient turnover. The longer a patient stays within the system the greater the opportunity for degradation in patient care likewise every service associated with the direct cost of patient care will increase in direct proportion.

 

Aggregating information through a single portal or gateway will reduce the complexity of the system and the number of connections or processes needed gather information and share that information with other members in the system.

 

As information flows more freely through the system patients will be treated more accurately, safely, and with a greater standard of care. Patients will experience a faster response times and the hospital will in turn process a greater number a patients at a lower cost.

 

            Overall effectiveness:

o       Reduce processing time

o       Reduce costs

o       Increase quality of patient care and safety.

o       Increase time staff can spend with each patient.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


The existing networked environment in a hospital can be described by Metcalfe’s law as follows :

·        The number of possible network connections = {[n (n+1)]/2} - n

In a networked environment utilizing a portal there is a 1 to 1 relationship between the number of entities and the number of networked connections. In this simple example one can realize a 33% increase efficiency through the use of an enterprise web portal to aggregate patient information.

 

Many of the connections as defined in the existing hospital network are not currently automated. Physical processes define many of the network connections, to disparate user groups in the current system. A patient record or results from a lab test being couriered to a specialist for review and diagnosis can illustrate one example of this process.

 

At Kiser Permanente this process resulted in a 10 day delay in doctors receiving the results of a lab test. After automating this process doctors could get lab results in a matter of hours.

 

With the use of ACT*Ontology results for a lab test can be made available to doctors in real time. This system will reduce the complexity of the existing system and automate the process thereby greatly reducing inefficiencies and administrative costs. The current system may require staff to place a phone call, make a fax, currier a document, or perform multiple data entries. Through the implementation of ACT*Ontology these processes can be automated and information can flow freely and instantaneously between user groups.

 

 

Competition and alternatives

 

·        Data Aggregation:

Currently there are a variety of solutions available that can aggregate patient information and allow doctors real time access in the examining room. This is particularly true with regard to lab records and radiology reports.

 

Generally these solutions represent 80% to 90% services and 10% software. The reason for this is due to variety of disparate data sources and procedures that are unique to every hospital or clinic. A consulting firm such as IBM or E&Y will build a system that will conform to whatever existing processes, procedures, and data that are persistent in a hospital.

 

Development tools such as Optimal J by Compuware utilize an easy graphical interface and all of the necessary drivers to drill down into existing databases.  Objects that can define abstractions of patient information can be quickly and easily built. These objects exist on a web server and can be accessed remotely through a secure intranet. In this was disparate data can be aggregated from many sources and made available to doctors in an easy to use graphical interface.

 

With this system information can be accessed timely and accurately, however intelligent analysis combined with real time data is not possible with other existing solutions.

 

·        Business Intelligence:

The industry standard in business intelligence is IBM’s business intelligence group. Again the majority of their work consists mostly of business services where systems are built to comply with an existing process.

 

The implementation of an IBM business intelligence system requires the implementation of a new data warehouse. Data is mined from existing disparate sourced or extracted, transformed, and loaded into a data warehouse. The intelligent system then runs off of the historical data in the data warehouse. The major issue with this type of system is that it relies on historical data. Real time access to transactional data is not possible. For this reason information such as lab reports can not made available in a timely fashion. Most importantly, the lack of explicit representation of semantic relations in relational databases also severely restricts data warehousing solutions, which makes it difficult for them to provide capabilities such as dynamic browsing among related objects.

 

The ACT*Ontology intelligent system combines the advantages of these two solutions. Data can be accessed in real time, transformed into usable information, and made available to users over a secure intranet.

 

Through the use of intelligent capabilities data can be analyzed and transformed into usable information. Business rules can be applied to prompt doctors as to a potential diagnosis as well as different treatment options including appropriate medication based on patient demographics such as age and weight.

 

Unlike other aggregation tools the ACT*Ontology solution will allow complex data analysis without the implementation of a data warehouse. The following chart compares these attributes:

 

Functional Requirements

ACT*Ontology

Business Intelligence

IBM Business Intelligence

(Data Warehouse)

Java based integration tool

Real time data access

a Yes

NO

a Yes

Transactional data access and real time aggregation

a Yes

NO

a Yes

Data Analysis Tools

a Yes

a Yes

NO

Statistical Analysis tools

a Yes

a Yes

NO

Data Discovery and Mining

a Yes

a Yes

NO

Decision support

a Yes

a Yes

NO

Management Reporting

a Yes

a Yes

NO

 

 

“ACT*Ontology uses an object-oriented knowledge representation as its federation model.  Using ACT*Epilog Transformer, developers construct a common object-oriented enterprise model that represents data from legacy data sources, such as relational databases, object databases, documents, CORBA applications, spreadsheets, XML and so forth.  

 

In our approach each data source is accessed by a data access agent called a “ACT*Data Access Agent” or DAA.   A DAA is a software module that knows how to use the data/information source’s Application Programmer’s Interface (APIs) to retrieve specific information as required by the Enterprise model.  The DAA also knows how to translate source data into the enterprise model’s object representation.  Each data source, together with its DAA, makes up one “federate” in the federation model. The federates, together with the common enterprise model, make up the federation.  The benefits are:

 

 

 

 

 

A further value of this approach is that data is truly integrated as semantically linked objects in the enterprise model.   In other words, a “part” object from the inventory database can be linked to a “purchase order” object from the back office database through a semantic relation “ordered-by”, as illustrated in the figure below. The inverse relation, “orders,” provides a means of getting from the purchase order object to the part object.  These semantic linkages provide the means of integrating information from different data sources into a meaningful enterprise context.

 

 


 

 

 

 

 

 


Representation of semantic links among objects in enterprise model.

 

 

This integrated enterprise model provides an object-oriented “metadata” representation that can be used to support queries, browsing, and many kinds of analyses.  It also serves as an agreement between all stakeholders (federation participants) about how they will communicate and share information.

 

This is not possible in point-to-point or EAI solutions, since there is no common model of the enterprise to support analysis, data management or general information sharing.  The lack of an explicit representation of semantic relations in relational databases also severely restricts data warehousing solutions, which makes it difficult for them to provide capabilities like dynamic browsing among related objects, expert systems analysis, or flexible growth of the model over time.

 

Once a federation has been established, clients can search the enterprise model based on their own criteria, as though it were a single virtual knowledge source available over the web.  Using web presentation methodologies, such as Java, HTML or XML, this semantically linked network of objects can also be navigated using typical web-enabled browsers.  This is important both for navigating from one related object to another and for discovering new implicit relationships in the enterprise knowledge base.

 

Advantages of the federation approach over data warehousing include:

 

 

 

 

Source: The ACT Product Suite White Paper, see appendix

 

 

 

The following diagrams illustrate differences between the ACT*Ontology system and traditional business intelligence solutions.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


Benefits of ATC Ontology

 

Integrated Delivery Network System for Ambulatory Electronic, Medical Records

1966 study of New York hospitals found that information handling to satisfy the requirements of clinicians to make accurate diagnosis accounted for approx 25% of the hospital’s total operating costs (Jydstrup and Gross), 1966.  On average hospital workers spent three-fourths of their time handling information.  Nurses spent about one fourth of their time on these tasks. 

 

Pabst and colleagues (1996) found that an automated system designed to replace just forty percent of manual documentation decreased the time required for documentation by one third.  Nurses using an automated system spent more time in direct patient care and were more likely to complete documentation during their shifts rather than staying over than Adderley and Associates 1997 described the benefits of a paperless record as related to accessibility of the record.  Verbal orders were eliminated and progress notes were more likely to be entered.  Communication among care givers were enhanced prospective rather than retrospective reviews of clinical data provided current accessibility of patient progress, care planning, medication use, and ancillary services.  Annual cost avoidance was also significant through the use of electronic patient records.  Cost, time, job satisfaction and hire order thinking with regard to patient care are all intangible benefits to the use of this type of system.  J.G. Ozbolt and S. Bakken Medical Informatics Computer Applications in Healthcare and Biomedicine:  2001,  440.

 

Collection, storage, retrieval and analysis and dissemination of clinical information necessary to support daily operations of a hospital are too many times maintained by manual processes where duplication and redundancies create errors and affect the safeties of patients.  By automating these systems, information will be made available real-time to clinicians, ensuring the accuracy and safety of patient care.  At the same time, doctors will be able to spend more time with the patients and less time handling the paperwork. 

 

Traditionally, computer information systems or medical informatics systems have developed separately within different departments.  Accounting has its own system and radiology and labs have their own system.  For this reason, it is difficult to aggregate all of this information into one data repository.  There are two basic options for aggregating information. 

 

The first option is to replace all of the legacy systems with one all encompassing enterprise system.  This option is generally very disruptive to the existing operations in the hospital.  When you implement new technology you are just not changing technology you are changing the processes by which people do their job.  For this reason, it is disruptive and not desirable to scrap the legacy systems.  Secondly, large enterprise systems cannot do each task as well as a specialized system for that department. 

 

The second option is to leave the legacy systems in place and aggregate patient records in a data warehouse.  This option implies that data within the data warehouse is historical and requires extract transform and load at periodic intervals.  This process, ETL, many times requires manual processing to maintain consistency within the data warehouse.  A data warehouse likewise requires maintenance and support.  However, the fundamental difficulty in building a data warehouse is that it does not aggregate data in real-time.  Data warehouse contains historical data.  For this reason, a doctor who wishes to review the results of a lab test, will not get the most recent and accurate information from a data warehouse. 

 

ACT Ontology combines best practices of the data warehouse at the same time allowing access to real time data.  This is accomplished through the use of a federated virtual database by aggregating data through a secured intranet-based portal.  Legacy systems remain in place and workflow processes, which have been developed over time, and work well with these legacy systems, are not affected.  Administrators and hospital employees who are familiar with these legacy systems can continue to use them.  There workflow processes will not be disrupted by the implementation of this new system.  ACT Ontology drills down into the legacy systems in real-time as if each disparate database were part of a virtual data warehouse. 

 

ACT Ontology does not replace legacy systems.  It adds functionality to the systems that are currently in use at the hospital or group of hospitals.   The basic functional areas for which data is aggregated are:  accounting and administration, radiology, labs, pharmacology as well as any existing ambulatory and clinical medical records.

 

Computer assisted decision support is also a functional component of the ACT system.  The artificial intelligence capabilities of the system recognize patterns within the historical records of past patient visits.  By looking for corollary relationships between certain patient attributes within all of the databases, and successful diagnosis of certain diseases. 

 

For instance, the system would find a corollary relationship between patients exhibiting certain characteristics such as age, weight, reported symptoms, and past history.  Only by aggregating large amounts of data, will the system be able to recognize these patterns.  This system could identify people who are at high risk for type two diabetes or colon cancers. 

 

Furthermore, the intelligent system will be able to prompt doctors to potential errors that may occur.  For example, validity checks to ensure that prescriptions are ordered correctly given age and weight characteristics of a patient.  Range checks can detect and prevent entry of values that are out of range of normal healthy individuals.  Pattern checks can verify that data has been conducted correctly.  Computed checks can verify correct mathematical relationships and delta checks can warn of large and unlikely differences between the values of historical records and those of a particular patient. 

 

By providing workstations within the examining room, data is captured at the point of discovery.  Doctors and nurses can input symptomatic information describing the patient at the time of examination.  When data is captured close to the source, fewer errors will occur.  For instance, a doctor can type notes into the workstation or order a lab test, write a prescription in the examining room.  When data is captured at its source, error detection or correction can be facilitated at the point of capture.    If the doctor types in a decimal point in the wrong place, the system will prompt the doctor for correction at that point of time it is written.  However, the doctor or clinician will always be able to override the system.  It is only a system provided recommendation that can be followed or not. 

The system will assist the clinician in selection of diagnostic tests, interpretation of test results, and choice of treatment for the patient. 

 

“Patient care information systems in use today represent a broad range in evolution in the field.  Some of the earlier systems are in use.  These systems are generally designed to speed documentation and increase legibility and availability of the records of patient currently receiving care.  Most lack the capacity to aggregate data across patients, to query the data about subsets of patients or to use data collection for clinical purposes to meet informational needs of administrators or researchers.  These shortcomings seem glaring today but they were not apparent when the very idea of using computers to store and communicate patient information required a leap of imagination. 

 

More recently developed systems attempt with varying success to respond to the ‘edict collect once use many times.’  Selected items of data for patient records are abstracted manually or electronically to aggregate databases where they can be analyzed for administrative reports or for clinical or health services research period.” J.G.Ozbolt and S.Bakken, Medical Informatics Computer Applications in Healthcare and Biomedicine:  2001,  433.

 

 

Implementation methodology and risk mitigation

 

In order to design and implement an enterprise tool for the aggregation of patient records a methodology for design and implementation will be utilized. The major components in this process will incorporate continuous feedback and input from functional user groups. When implementing an enterprise solution there are two major components used to describe the process. One component will be the existing “off the shelf” software and the second will be the services required to design, configure, test, and install the system.

 

Software will consist of 50% of the project and services will consist of another 50% of the project. The services will be governed by a systems development life cycle methodology as defined below.

 

Implementation will begin will extensive requirements gathering. This will include discovery of all known data sources, definition of current technology driving business processes within the organization. In conjunction interviews with user groups will be facilitated. Some of these groups will include Doctors, Nurses, and administrators.

 

Once the current processes and future requirements are defined a statement of work will be prepared to define the scope and functionality of the project. This document will also include milestones to track the progress of the implementation.

 

Advanced Coordination Technologies will be contracted to work in conjunction with the implementation staff to install the core components of the software and all associated hardware when appropriate as defined by the requirements gathering phase. Advanced Coordination Technologies will receive payment in the amount of $5 million dollars as part of the implementation and licensing agreement upon completion of the implementation.  

 

ACT suite of tools consists of an integrated development environment that will be used by a team of analysts consisting of between 7 and 12 people including a project manager depending on the scope and functionality as defined in the requirements gathering phase. This team of analysts will utilize the tools in the ACT suite to perform data access to the existing system. The ACT suite consists of a fourth generation unified modeling language UML that will be used to define objects, processes, and events. The team will then map the existing data schema within each disparate source to the objects, and processes, and events as defined by the internal UML in the ACT suite.

 

Once all of the appropriate data silos have been aggregated testing can begin. The team will begin developing a user interface to present information to appropriate user groups. Upon completion of the testing of the data aggregation phase the implementation team will link the data to the user interface.

 

Test and redesign of the user interface will occur in conjunction with user groups as the work flow of operations processes are more clearly defined. Towards the completion of this process training and final implantation will take place.


 

  1. Requirements gathering.
  2. Design.
  3. Configuration and development.
  4. Test.
  5. Installation and live test in parallel with existing systems and processes.
  6. Training, transition to new processes, and “Go live”.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


Project Timeline

 

 

 

 

 

 

 

 

 

1

2

3

4

5

6

Project Planning

System capabilities and Scope

 

 

 

 

 

Define Metadata Strategy

 

 

 

 

 

 

Identify User Community

 

 

 

 

 

 

Requirements Gathering of

Business Processes

 

 

 

 

 

Define Information Requirements

 

 

 

 

 

 

Begin UI design

 

 

 

 

 

 

Identify Data Silos or Repositories

 

 

 

 

 

 

Identify information access and security constraints and procedures

 

 

 

 

 

Define system Infrastructure and Architecture

 

 

 

 

 

Establish Technical Infrastructure

 

 

 

 

 

 

Apply Data Access Agents to disparate silos.

 

 

 

 

 

Define Federation nodes with in the enterprise model

 

 

 

 

 

Identify Objects, Processes, and events.

 

 

 

 

 

Map data from data silos (federation model) to objects, processes, and events

 

 

 

 

 

Test Implementation

 

 

 

 

 

 

Live implementation

 

 

 

 

 

 

Training

 

 

 

 

 

 

 

 

 

 

 

 

 

Market & Marketing Strategy

 

The market for the ACT*Ontology solution will consist of hospitals and HMOs throughout North America.  The market can be further expanded in Europe and Latin America as the company grows.  This product will be sold as an Enterprise solution for creating efficiencies in doctor patient processes. 

 

Sales and distribution will be subcontracted out to existing consulting firms in the health services industry.  There will initially be an in house sales representative with extensive years of experience in the medical sales and consulting. This person will negotiate contracts and develop leads with hospitals through existing relationships. 

 

Management Team

 

The management team will initially consist of six people:

 

Andrew Lammers, SMU MBA, with over eight years in application development and a Masters of Information Science with research in financial modeling.

 

Roger Lammers, Wharton MBA with 15 years management experience at the CEO and board level. Total assets under management generated greater than $1/2 billion in sales annually.

 

David Lanners, Harvard MBA, Project manager, consultant, 25 years management experience in enterprise level technology implementation.

 

Eric Mettala, PHD in Operations and Computer Science, program manager at DARPA during the development of the DARPA-Net, which later became known as the Internet. He is formerly Dean of Engineering at UT Arlington.  He has worked as a subcontractor for the department of defense in the area of artificial intelligence.

 

Natalie Nespeca, SMU MBA, formerly with the Business Process Consulting at Accenture.

 

Charice Thomas, a Yale Computer Science Graduate, with vast Business Intelligence Consulting experience. 

 

Business Model

 

Even though this type of system would be very difficult to construct, it has been developed and is currently in existence in the state of Connecticut.  A team of computer scientists and engineers using this artificial intelligence technology developed a system that would allow patient information to be agitated in real time.   The chief scientist, Eric Mettala, who developed this technology, continues to enhance the core technology for the department of defense. 

 

Eric Mettala is willing to license this technology for commercial use.  The product will be a derivation of the application that was developed in Connecticut for making patient records available to doctors in the examining room.  By working with the original developers the company will create an enterprise computing solution that can be sold to hospitals.

 

The solution will aggregate patient information over disparate data structures and make that information available to doctors in the examining room.  The goal of the business plan is to take the original technology and evolve it into an “off the shelf product” that can be sold to a hospital and be implemented by a small number of analysts in a short period of time. 

 

Concept

 

Existing business intelligence solutions such as IBM’s Intelligent Miner and DB2 Warehouse consists of a single driver and data transfer through one channel.  All of the existing business intelligence solutions require data to first be aggregated into a single data warehouse and the intelligent solution runs queries against that warehouse.

 

The ACT*Ontology Technology differs from this in that it does not require the information to first be aggregated into one source. The ACT*Ontology solution uses a set of cooperating management agents that carry out tasks autonomously and in parallel. These agents share results in real time and can be generate inputs for other tasks. Users will be able to generate results in real time as objects interact intelligently while retrieving data directly from the source. This solution does not require the creation or implementation of a new database or data warehouse. It is comprised of several intelligent nodes that that interact in a network similar to the neurology of a brain. 

 

In addition to being efficient, the software has a faster speed of processing than any current solution.  Where an existing solution processes 65 transactions per second, the ACT*Ontology technology processes 1900 transactions per second.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

How it works

3 tier metaphorical representation within the development environment

 

1.      Functional level view of the model, Defines the organizational relationships architectural hierarchy, also defines inputs, outputs, and sub functions.

a.       Customers. 

                                                   i.      Patients,

                                                 ii.      Doctors,

                                                iii.      Clinics,

                                               iv.      Any user group might be considered a customer of the information.

b.      Services or products.

                                                   i.      Blood tests,

                                                 ii.      Examinations,

                                                iii.      Lab tests,

                                               iv.      Radiology tests.

                                                 v.      All of the services that can be performed by a hospital or doctor would be considered a type of service.

c.       Organizational processes.

                                                   i.      A patient receives a blood test.

                                                 ii.      A doctor administers a blood test.

                                                iii.      A lab processes the blood test.

                                               iv.      Any relationship between that may exist between the other objects is represented as a process.

 

 

 

 

 


2.      Object level view. Describes the structure of the entities or objects. This is requires for the enactment of he processes.

a.       Patients

                                                   i.      Gender

1.      Male

2.      Female

                                                 ii.      Preexisting conditions

1.      Obesity     

2.      Diabetes

                                                iii.      Current symptoms

1.      Shortness of breath

2.      Fatigue

                                               iv.      Diagnosis

                                                 v.      Consults with a doctor

                                               vi.      Visits a hospital

                                              vii.      Has a disease

 

 

 

 

 


3.      Coordination level view. This defines the workflow and the procedural rules that govern how the processes will work together. This view is made up of activities that tie together objects and processes that make up the workflow.

a.       Patient Visit

b.      Lab processes a blood test.

c.      

The following diagram illustrates an example of the functional level view of the metaphor that exists in the development environment.
 
Doctor or specialist makes a diagnosis.

 

 

 

Examination

 

Lab results
 

 

 

Lab tests
 

 

Lab
 

Consultation

 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Financial Information

 

The company will require $300,000 initial working capital. The majority of this money will be used to pay the salaries for the annalists and technical personnel during the first implementation.  

 

 

 

 

 

 

 

APPENDIX

 

 

Methodist Hospital Case Study

 

During the l970’s Methodist Hospital purchased a database management system and operations management system from TDS Healthcare Systems.  The type of software they purchased was described as a “turn-key” system.  It was designed to perform certain functions and focus initiatives to follow with changes in operations to conform to the pre-defined processes. Turn-key implies that it can be implemented as easily as turning a key, but this also means that most of the functions are preset and can not easily be reconfigured. The advantage of this kind of system is the implementation is much quicker and easier and the overall cost is much less than producing an entirely new system that is specific to your company’s needs. Current ERP have very similar problems particularly in regard to integrating e-commerce solutions. 18, 19, 22

 

Despite of the advantages discussed above, several problems arose after the system implementation rollout.  First of all, the system did not meet the specific needs of every department in the hospital.  Two departments in particular had a great deal of difficulty and were unable to integrate the system into their operations.  These departments were radiology and lab.  As a result, they developed their own systems that were incompatible with the TDS system that was supposed to be running the hospital.  Networks could not talk to each other; raw data about patients could not be shared with the different systems.

 

The only way to track patient information was through a very inefficient paper trail.  Information was created about information, such as a small database was placed on a person’s desktop to hold a weekly report.  Doctors and Nurses could not pull up all the information needed on a patient simply by logging on to the computer.  This was a major problem and defeated the whole idea of having a computer system to help the hospital run more efficiently.  Lastly, the information systems department was constantly overwhelmed with work simply trying to keep the old systems running and never had time to get anything updated or develop a new plan. 18, 19, 22


For a temporary solution, the IS manager at Methodist hospital was able to develop a system referred to as Exchange Platform. This centralized database or data warehouse gathered information from all the hospital systems and converted the data to a common format and made information available to doctors and nurses who needed it. This allowed all of the different systems to warehouse particular pieces of data about patients in a single location.  The hospital could then use data mining techniques to gather information and display it on PC terminals throughout the hospital.  Initially, this allowed doctors and nurses to obtain relevant information concerning all data related to that patient. 18, 19, 22

 

The disadvantages of a shared warehouse soon became apparent.   By creating a new database, this would duplicate data already in the system.  If the centralized database was used as a repository for historical data, the solution might be more viable.  However, if they tried to use a centralized database as a way of integrating all of the systems in a total database management system, one might still run into many of the same data integrity issues that one would find trying to integrate all of the disparate data sources.  Secondly, a data warehouse of this type would not solve all of the administrative problems incurred by the old system.  All of the different computer systems would still be autonomous and create a great deal of duplication.  There would also be potential problems in data integrity and potential errors in data capture.  18, 19, 22

 

This system would be able to allow doctors and nurses to view patient information but would not allow the hospital administration to integrate these disparate systems with accounting, human resources, and supply chain management.  The hospital would still be stuck with old antiquated systems.  Administrative problems would be even greater because of the introduction of an entirely new database system to an already confusing situation.  These problems are symptomatic of many hospitals and other companies in the health care industry. This situation also highlights existing problems with ERP’s and the ability of firms to gather large amounts of comprehensive data about health care and the effectiveness of different treatments.

 

Kaiser is currently in the process of spending $2 billion in attempting to utilizing technology for a majority of their operations beginning with web portals. Previously, patient records were kept maintained solely in paper form. Every time a patient was referred to a specialist a hard copy of the patients records would be shipped overnight using a fleet of vans. Many times records would be lost or misplaces. Tests would have to be performed multiple times and sometimes doctors did not have complete or up-to-date patient records. Most importantly Kaiser had no way of tracking the long term health of its patients or determining what tests were most effective and cost efficient. 26(p.80-91)

 

As the benefits of technology become apparent, Kaiser has begun to convert patient records to digital form from a variety of different locations and different systems within a single hospital. This information is made available on the Internet to primary care doctors, specialists, and patients. Individuals can even make and check appointments over the Internet. This has reduced the cost of making appointments from $70 to $10. Doctors are able to have up to date information about patient records as well as a complete history of care and treatment. 2(p.102135)

 

One of their goals is for Doctors to be prompted to prescribe the most cost effective drugs once a diagnosis has been formulated. Efficiencies occur as a result  of this such as allowing Kaiser to track the costs of this treatment and the costs of referring patients to specialists. This system has also greatly reduced the response time of tests down form eight days to a matter of hours. Doctors were able to double the amount of time spent with patients by simply putting a computer in the examining room. Kaiser is now looking at allowing administrators to start ordering supplies over the Internet using the same system. 2(p.102135)

 

This model can be taken one step further using distributed object oriented network processing and business intelligence.  Statistical models created by aggregating data would allow for information flow about diseases and the most efficient form of treatment. This information can then be used determine what tests should be performed on patients based on demographic profiles of individuals.

 

If a business intelligence system were applied to this application one could define objects such as patients, illnesses, treatments, procedures, and more from the data already existing in the hospital.  Once these objects are mapped to the existing data structure they interact with one another within the nuero-net or artificial intelligence system that makes up the ACT system. Information is shared with the intelligent translators about how these objects interact and the system begins to learn the functionality of these objects and more precisely define their characteristics as well as the rules governing the relationships of those objects.

 

Once a patient is identified as being a particular risk for a certain disease the most effective preventative care can be applied thereby reducing long-term coasts as well as pain and suffering. It is conceivable that a patient could walk into the examining room and a series of simple questions would be asked and input into the computer combined with historical patient records.

 

It would be helpful for a doctor to receive current information about what illnesses to look out for and what tests should be performed. Once the tests are performed and a diagnosis formulated information about specialists would be available in the area covered by a person’s insurance. The computer could also assist in determining the most effective treatments and coasts that are or are not covered by their insurance.

 

With this kind of information the primary care physician can become an active participant with the patient in advising and determining the best solution for their long-term care. Currently the patient must educate him or herself as to viable alternatives that are available for treatment effectiveness.

 

The idea of object oriented data processing over a distributed network could fundamentally change the way businesses use the Internet and could solve many of the problems discusses previously. This idea says that an object has both attributes and functionality that it can be inherited from other objects. These objects can then interact with each other through network communications using CORBA architecture. 2(p.102-135)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Endnotes

 

 

  1. Carl Shapiro, and Hal R. Varian, Information Rules  (Boston: Harvard Business School Press, 1998) 19 – 316.

 

  1. Mohanbir Sawhney, and Steven Kaplin, “Lets Go Vertical B to B E-commerce”, Wired, September 1997, pp 102 –135.

 

  1. John Hagel III, and Arthur G. Armstrong, McKinsey & Company, Inc., Net Gain (Boston: Harvard Business School Press, 1997).

 

  1. Amir Hartman, John Sifonis, and John Kador, Net Ready Strategies for Success in the E-conomy (New York: McGraw-Hill, 2000).

 

  1. Larry Downes and Chunka Mui, Unleashing the Killer App, digital strategies for market dominance (Boston: Harvard Business School Press, 1998).

 

  1. Author not available, “STORAGE: COMPACT DISC RECORDABLE DRIVE ALLOWS USERS TO RECORD LARGE”. Cambridge Work-Group Computing Report, 12-25-1995.

 

  1. Brain W. Arthur. “Increasing Returns and the New World of Business”, Harvard Business Review 47, no.4 (July-August 1996): 100-109.

 

  1. Toni M. Sacconaghi. “Who will benefit from virtual Information?” McKinsey Quarterly, no1 (1996): 4-18.

 

  1. James M. Buchanan and Toon J. Young. The turn to increasing returns. Ann Arbor: University of Michigan Press, 1994.

 

  1. George Gilder. Microcosm: The quantum Revolution in Economics and Technology. New York: Simon and Schuster, 1989.

 

  1. Christine M. Boyle. Cybercities: Visual Presentation in the age of Electronic Communication. Princeton: Princeton Architectural Press, 1996

 

  1. Michael Benedict, ed. Cyberspace: First Steps. Cambridge: MIT Press, 1991.

 

  1. Scott Kirsner, “All The News That’s Fit to Pixel” Wired February 2000: 126 – 141.

 

  1. Geoffrey Colvin Reporter Associate Ahmad Diba, “Features/Cover Stories/Most Admired: America's Most Admired Companies How frequently and creatively do they use the Internet? The range among these ultra-esteemed outfits is remarkably wide--for now”, Fortune, 02-21-2000, pp 108+.

 

  1. Julia Angwin. “McAfee Sweeps Away Viruses”. San Francisco Chronicle, 14 August 1997.

 

  1. Bart Ziegler, and Don Clark. “Microsoft Gives Technology Away to beat Rival.” Wall Street Journal, 2 October 1996, B1.

 

17.  Tom Stein and Jeff Sweat, “Cover Story: Killer Supply Chains -- Six Companies Are Using Supply Chains To Transform The Way They Do Business.” InformationWeek, 11-09-1998, pp 36.

 

  1. Author not available, “Hyperion Ships Hyperion Application Link; Integrates Packaged Analytic Applications with Valuable Data in ERP, CRM and Other Transaction-Processing Systems.” Business Wire, 09-01-1999

 

  1. CONTRIBUTORS, “TEST CENTER COMPARISON: Web-based project management; Project meets portal; Combining solid project management tools with Web collaboration for team members, three enterprise project management solute”. Vol. 22, InfoWorld, 01-31-2000.

 

  1. Maggie Biggs, “InfoWorld Test Center, Enterprise Computing: ForeSite masters integrations”. Vol. 21, InfoWorld, 09-27-1999. 

 

  1. Warren Brown Washington Post Staff Writer, “Ford, GM Drive Onto Information Highway”, The Washington Post, 01-15-2000, pp E01.

 

 

  1. Mark Hagland, “Enterprise Integration: Practice made perfect”, Health Management Technology, 09-01-1998.

 

23.  Ellis Booker, “Trends -- Part 1: Self-Service Apps: ERP's Next Frontier.” InternetWeek, 03-15-1999, pp 31.

 

  1. Author not available, “ecFood.com and SCT to Join Forces and Supply-Chain Technologies to Benefit Food Manufacturers; Demonstrate Unique B2B Solution in Vitamin-e Briefing.” Business Wire, 02-07-2000.

 

  1. Author not available, “Intel & Excite: E-Shop 'Til You Drop’”, Newsbytes News Network, 04-13-1999.

 

  1. Douglas Gatemen, and Marcia Stephanie, “Kaiser Takes The Cyber Cure”, BuisnessWeek February 7, 2000, 80-91

 

  1. Tania Chau; Jon Goldstein; Anita Hamilton; Melissa Scram With Reporting By Silke Abele, John Colmey, Ashley D'mello, Michael Kepp, “Features: Cyber Elite Technology Has Ushered In A New Ruling Class. They're Into Everything. From Music To Multinationals, And All Of Them Are Changing The Way The World Moves. Here's Our First Annual”, Time, 10-01-1997, pp 33+.

 

  1. Paul Klemperer. “Markets with consumer Switching Costs.” Quarterly Journal of Economics 102, no.2 (1987): 375-394

 

  1. Edward H. Shortliffe; Leslie E. Perreault; Gio Wiederhold; Lawrence M. Fagan, Medical Informatics Comouter Applications in Health Care and Biomedicine (New York: Springer-Verlang, 2001).