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DESIGN AND DEVELOPMENT OF THE PUBLIC HEALTHCARE LABORATORY INFORMATION

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The ER-diagram: Medical Diagnostic Laboratory The ER-diagram: Medical Diagnostic Laboratory
Research order form Research order form
Ficha de los resultados de los exámenes Ficha de los resultados de los exámenes
Ablameyko S.1,2, Mozheyko D.2
 
1Belarusian State University, 2 United Institute of Informatics Problems of the National Academy of Sciences of Belarus
6, Surganova str., Minsk, 220012
Belarus
ablameyko@bsu.by, mdl@newman.bas-net.by <>
Abstract
We present our experience of creating and deploying a medical laboratory information system, designed to support medical personnel by effective tools of control laboratory workflow using a variety of modern information technologies. Actual tasks of laboratories automation and directions of their solution are offered. Requirements to construction of laboratory information system and features of its integration into structure of hospital information system are considered. The ER-diagram of medical laboratory is proposed. Results of development laboratory information system are represented.
 
Keywords:Laboratory Information, Hospital Information, Medical Laboratory, Medical Personnel
 
1.   INTRODUCCIÓN
The time that physicians and nurses spend finding and organizing clinical information is excessive and increasing as the volume of patient information expands. As the field of medicine develops, the information related to examinations, management, and treatment of patients is increasing at a geometric rate [1]. One hospitalization can produce tens of thousands of separate observations, counting laboratory tests, physiologic monitor output, diagnostic imaging, patient surveys, nursing assessments, and visit notes [2]. Therefore, it is important that a hospital has an information system to arrange the medical information that originates from patients and their medical services.
It is extremely important in medicine, more than in any other field, that the accuracy is comparable to experts. Diagnoses that are incorrect, or other diagnoses which are missed, may result in serious consequences for the patients. HIS provide support to medical personnel improving the reliability and quality of treatment.
A Medical Diagnostic Laboratory (MDL) occupies a large part of the structure of diagnostic research, both in the quantity of research and the clinical importance of test results – which are an important source of diagnostic information for modern medical diagnostic processes. According to world statistics, in previous decades the quantity of performed clinical laboratory tests and their diagnostic importance exponentially increased – and continues to increase [3]. The use of Laboratory Information Systems (LIS) has now become the standard of MDL activity, with MDLs using a variety of automated information systems.
Although the MDL provides most of the information used by physicians in the treatment of patients, it is also an auxiliary service where opportunities exist for cost reduction. Laboratory costs include labor, reagents, testing materials, disposables, overhead, and other allocated expenses. To remain competitive and to supply physicians with the most accurate, up-to-date information about their patients, the laboratorian must keep up with new advances in technology [4]. The outpatient and inpatient revenues for hospitals that introduced a Laboratory Information System (LIS) were significantly greater than those of the hospitals that did not [1, 4].
As a result, the automation of MDL processes is an actual problem with significant practical value.
In this paper, we describe issues of development LIS, its integration into Hospital Information System (HIS), propose the ER-diagram of MDL and present some results.
  
2.   METODOLOGÍA
HIS. HIS being introduced today are moving away from the monolithic centralized systems of earlier days and now accumulate medical information in electronic medical records, support networked interaction among heterogeneous components, with broad conventions and policies governing communication (e.g., HL7 as a network communication format), queries (e.g., SQL for access to stored data), and interactions with other hospital responsibilities [5, 6].
EHR. Electronic health record (EHR) is central components of HISs in particular with respect to the integration of information. The purpose of EHR is to store the information about patient that is generated by physicians, nurses, hospital administrators, etc. Goals of digitizing medical records are, for instance, improving medical treatment of patients and the computerized evaluation of patient data to support research in medicine. EHRs are not merely automated forms of today's paper-based medical records, but encompass the entire scope of health information in all media forms. Thus EHRs may include medical history, current medications, laboratory test results, etc. A HIS can positively impact patient care in several ways. Some advantages involve increased efficiency and higher quality documentation while others involve automated checks and reminders to assist a physician in providing optimum care.
The EHR has several advantages over the conventional paper-based medical record [7], including:
  • Patient information is available at several working places at the same time.
  • The information is available within a short time. This is important in case of emergency.
  • Acquisition of data may be improved by the use of advanced user interfaces.
  • Reuse of results of medical operations is supported, even over the lifetime of a patient. This may relieve patients from being checked with the same medical operations several times.
  • Medical research is supported. An application area is the control of the results of specific therapies. However, the EHR also has its disadvantages:
  • It requires a larger initial investment than its paper counterpart because of hardware, software and training costs for the personnel.
  • Capturing the physician-collected data for an EHR can require a lot of time and effort: physicians often use a great deal of information to make one decision.
  • Data security.
 
Standards. Common goal of use of computer based systems in medicine is one global information system that integrates all levels of medical care independent of the current location of either a patient or a care provider. With that in mind, the system has to be able to communicate with other systems that are of interest. This also includes the need for the systems to be able to adopt important medical communication standards such as HL7, DICOM and clinical code standards such as SNOMED, RCC and LOINC [2]. Logical Observation Identifiers, Names, and Codes (LOINC) is a freely available database of names, synonyms, and codes for clinical observations including laboratory tests, and other measurements.
Without standardization, none of the promises of electronic data processing can be met, because we cannot afford the costs of manual translation of data from systems that produce patient data (for example, laboratories) to systems that need them (for example, office practices’ information systems).
 
3.   Domain analysis
Domain analysis in the process of software development is the activity of identifying the objects and operations of a class of similar systems in a particular problem domain.
To provide a basis for the development of LIS which really meet the requirements of health care workers, a domain analysis for EHRs has been undertaken in close cooperation of computer scientists with several domain experts [8].
Firstly, based on field studies in some Belorussian hospitals, a generic hospital laboratory structure is derived and the relevant entities for MDL are identified. Secondly, hospital specific laboratory workflow problems are discussed. Sources of the information for construction of information model are document circulation and experts of laboratory. As well as in any other organization, passage of documents through MDL is accompanied by the certain procedures of the coordination, the statement and signing of documents and the control over their origin [9].
A macro model of MDL functioning follows a certain sequence of events. First, during input, research orders and biomaterial samples are registered and brought into correspondence with each other. Next, analyses (a set of laboratory tests) are carried out automatically or manually. Then, the obtained results of these tests are passed to a requester. The following peculiarities can be outlined at this stage:
  • Test results (and their dynamics) are of great diagnostic importance.
  • There is significant document circulation between clinical departments and laboratories.
  • There are a great number of tests to perform.
  • There is an availability of efficient automatic analyzers, information from which can be transferred.
  •  There is a necessity to improve the reliability and quality of laboratory research.
  •  There is a great deal of routine work completed by laboratory employees.
  •  The necessity of laboratory operational statistics preparation and availability of scientific statistics.
All of these factors work together to propel the necessity to solve the problems of transferring and storing data, as well as the need to act responsibly to ensure the reliability and quality of publicly available laboratory research results. Therefore, the best solution to these problems is the use of modern IT technologies and facilities in laboratory activities.
 
4.   The ER-diagram of MDL
The database of a MDL contains relevant information concerning entities and relationships in which the MDL is interested. A complete description of an entity or relationship may not be recorded in the database of an MDL. It is impossible (and, perhaps, unnecessary) to record every potentially available piece of information about entities and relationships [10]. From now on, we shall consider only the entities and relationships (and the information concerning them), which are to enter into the design of a database. Entity Relationship Diagrams illustrate the logical structure of databases. An entity is an object or concept about which you want to store information. Relationships illustrate how two entities share information in the database structure.
On the basis of domain analysis, the high-level (not detailed) ER-diagram of MDL is presented in Fig. 1. This diagram incorporates some of the important semantic information about the MDL. Its components are rectangles – representing entity and diamonds – representing relationship.
Fig. 1. The ER-diagram: Medical Diagnostic Laboratory.
An explanation of the model in Fig. 1 is given as follows. Analysis (entity ANALYSIS) is a set of the laboratory tests (entity TEST). For example, biochemical blood analysis includes whole protein, albumin, glucose, etc.
Reference range of the test depends on method of testing, patient’s gender and age.
Result values of some tests can be verbal. Verbal values are collected in the entity TERM.
In some cases, entities can be self-linked. For example, tests can include other tests (relationship component).
The entity FORM collects all information on analysis (patient ID, doctor, date of sampling, etc.). All patients’ analyses are contained in the entity FORM, which is linked to the entity EHR by relationship form-EHR. The entity RESULT is intended to store the results of laboratory tests. The list of laboratory employees is represented as the entity LABORATORIAN.
Representation of TEST as a separate entity lays in the basis of our approach [11]. according to this approach the database scheme does not depend on quantity and structure of laboratory subdivisions and tests in mdl.
 
5. REQUIREMENTS OF A LIS
There are two main directions of laboratory activity automation [12]. The first direction provides the use of computers for automating information and technical processes inside laboratories. The purpose of this direction is to increase laboratory productivity and research quality, to take into account the use of reagents and materials, and to reduce the amount of routine tasks performed by laboratory personnel. The second direction of laboratory activity automation deals with solving the problems of the interaction of laboratories with clinical departments. Included amongst these problems are the automation of processes of laboratory research orders registration and the transferring of results to clinical departments, and the implementation of expert systems for attending physicians based on laboratory diagnostics. The main purposes of this direction aim to support attending physicians, reduce the delivery time of research orders to the laboratory, reduce the quantity of unreasonable analyses, and represent test results in a full and correct form. Advanced LIS should support the functions of both of these directions.
Because MDL is the department that carry out many of the examinations performed at hospitals and produce a great deal of medical information, a clinical LIS is essential to an integrated HIS. The general requirements of an LIS as a subsystem of an HIS include:
  • Conformity to domestic and international standards.
  • Binding of EHR primary data and laboratory data.
  • Support attending physicians with test results and their dynamics, and managers with statistic reports.
  • Access restriction control to laboratory data on ethic and functional rules.
General requirements for an LIS should allow for:
  •  The integration (interface with and Public Health Information Network (PHIN) networks, as well as other local and state organizations).
  • The security (LIS must be configured to address the extremely confidential nature of the database).
  • Easy-of-use for field personnel.
  • The input of data manually and from analyzers.
  • A unified, scalable and customisable platform for any specialization (biological, clinical, bacteriological, cytological, etc.).
  • Research order creation from outside (physicians) as well as from inside (laboratory registrar).
  • Data export in various data formats.
  • The ability to manage input data flows of research orders.
  • The ability to control the traffic of samples and the status of analyses.
  • Registering checkpoint analysis times.
  • L/H visualisation and calculated indexes support, norms bounds checking in accordance with patient’ age, gender and used reagents.
  • The generation of laboratory workbooks and operation plans on the basis of analysis data (order, results, laboratorians).
  • To provide accounting of time expenses of test performing.
Preparation of operational and statistical reports in different slices.
 
6. IMPLEMENTATION AND RESULTS
Following the principles and the requirements summarized in the previous sections, we have designed a medical diagnostic laboratory information system (MDLIS) based on the data model shown in Fig. 1.
MDLIS is integrated into hospital informational analytical system on the basis of EHR. Common information space gives optimal way to solve a task of construction of intellectual analytical systems on the basis of the information from a database.
The multilevel modular principle [12] of MDLIS construction provides an opportunity of gradual growth of number of workstations and workgroups.
We aimed to keep algorithm of work of laboratory employees and sequence of technological operations in order to avoid excessive difficulties on learning of new approaches. The developed algorithms are directed on simplification of procedures of search, input of results, laboratory information visual perception.
In a research order (Fig. 2) the following data are taken into account: biographical particulars of patient, number of a patient card, patient status (inpatient care, outpatient care, advisory commission, paid medical service, etc.), department and also laboratory, type of research, lists of tests, attending physician and notes.
 
Fig. 2. Research order form.
 
Fig. 3. Tests results form.
 
The form of research results (Fig. 3) includes biographical particulars of patient, tests names, tests results, units of measure, normal values, date of carrying out, state (research order, in process, completed, printed, etc.), notes and the executors.
For the organization of safety and security of the information in a database it is used dynamic configuration of workstations. Function - the minimal action translating system from one steady state in another.
We consider function as unit of configuration of a workstation and as unit of definition of the access rights to the information. Configuration of workstations occurs at a level of carried out functions. The information about configuration workstations can be stored only in a database.
Mdlis was implemented both as stand-alone and integrated into an his in hospitals across belarus and russia, with the number of beds ranging from 200 to 1500, as well as in a variety of different kinds of laboratories.
 
7. CONCLUSIONS
We have reported in this paper our experience in building MDLIS, a laboratory information system intended for use in hospitals.
The past three-and-a-half years we have developed and operated health information analytical systems for the various medical projects carried out at the Information Analytical Systems Laboratory of the United Institute of Informatics Problems of the National Academy of Sciences of Belarus. Taking into account our national public healthcare services system we have designed health information system on the basis EHR.
A MDLIS improves the quality of medical treatment by increasing the satisfaction of medical personnel and patients, improving the laboratory process, and improving decision-making assistance.
The main goal for future work in this area is the realisation of information exchange between the diagnostic laboratories of multiple hospitals.
8. REFERENCES BIBLIOGRAFICS
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  2. C.J. McDonald, Need for standards in health information, Health Affairs, 17(6), 1998, 44-46.
  3. Y. Mikhailov, Information computer technologies - an actual and inevitable step of perfection of laboratory diagnostics (by the example of creation and use of automated workgroup “Hematology”), Clinical laboratory diagnostics, 7, 2001, 25-32. (in Russian).
  4. R. Seaberg, B. Statland, R. Stallone, Planning and implementing total laboratory automation at the North Shore-Long Island Jewish Health System Laboratories, Medical Laboratory Observer, 31 (6), 1999, 46-54.
  5. W. Hasselbring, Federated integration of replicated information within hospitals, Int. J. Digital Lib, 1997, 192-208.
  6. P. Szolovits, J. Doyle, W.J. Long, I. Kohane, S.G. Pauker, Guardian Angel: patient-centered health information systems, MIT Laboratory for Computer Science, Technical Report - 604, 1994.
  7. W. Hasselbring, System design and architecture - Federated integration of replicated information within hospitals, 1997, url: http://citeseer.ist.psu.edu/hasselbring97system.html.
  8. L.E. Perreault, G. Wiederhold, System design and evaluation, Medical informatics: computer applications in health care, Addison-Wesley Publishing Company, Chapter 5, 1990, 151-177.
  9. Ya.I. Guliev, M.I. Khatkevitch, Process and document in Healthcare Information Systems. A tutorial review, Proc. of the Int’l Conf. Program Systems: theory and applications, Pereslavl, 2004, 169-187. (in Russian).
  10. P. Chen, The entity-relational model. Toward a unified view of data, ACM TODS, 1, 1976, 9-36.
  11. D. Mozheyko, A. Anishchanka, Integrating LIS in clinical laboratories, Healthcare IT management, 1(1), 2006, 26-29.
  12. V. Truhan, D. Mozheyko, A. Anishchenko, Comprehensive approach to automation of laboratory researches in hospital. A tutorial review, Proc. of the Int’l Conf. Modeling and Simulation, Minsk, 2004, 220-226.

 
 
 
 

 

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