A Clinical Aspect of the Computer-Based Patient Record: Free Text versus Coded Data. Omar Al-Ghamdi, MD, MS. Medical Informatics.

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1 A Clinical Aspect of the Computer-Based Patient Record: Free Text versus Coded Data. Omar Al-Ghamdi, MD, MS Medical Informatics

2 2 Table of Contents: 1. Introduction. 2. CPR(Computer-Based Patient Record) 2.1. Definitions Advantages of CPR. 3. Free Text and Medical narratives. 4. Coded Data Abstracting patient data 4.2. Provision of controlled vocabulary to support coding of detailed patient data 5. Discussion. 6. Conclusion. 7. References 1. Introduction: In the fifth century B.C., medical reporting was highly influenced by Hippocrates. He advocated that the medical record serves two goals: I. It should accurately reflect the course of the disease. II. It should indicate the possible causes of the disease (Bemmel, Musen, & Helder, 1997). The medical record has been used for more than a century as a tool to assist clinicians in the care of patients. Today, the medical record has a comprehensive purpose: to recall

3 3 observation, to inform others, to instruct students, to gain knowledge, to monitor performance, and to justify intervention(tang, LaRosa, & Gorden, 1999). Several studies have shown that paper based records can not adequately support the task of providing patient care in an efficient manner(wigertz, 2001). Moreover, the overload of general and patient specific information from many resources, seen in bulging files of patients with chronic disease, intensifies the physical and conceptual problems of maintaining paper based patient records(liaw, Radford, & Maddocks, 1998). Many researchers believe that the electronic patient record will significantly change healthcare, rather than merely replacing the paper-based record. This change allows data to be used for a wide variety of purposes ranging from direct patient care, decision support, quality assurance, scientific research, and management of health care facilities(van der Lei, Moorman, & Musen, 1999). The transition to a computer-based record necessitates fundamental changes in the way clinical information is expressed(shiffman, Branndt, & Freeman, 1999). This is one of the challenges facing health care computing since the presentation of patient data has to be in a usable form(cimino, 1996). This paper focuses on the clinical aspects of the CPR, mainly on the clinical data handling, i.e., free text versus coded data.

4 4 2. CPR: 2.1. Definitions Computer-based patient record(cpr): is a repository of electronically maintained information about an individual s lifetime health status and health care, stored such that it can serve the multiple legitimate users of the record(shortliffe & Perreault, 2001). Electronic Medical record, as defined by Medical Record Institute(USA): a repository of clinical data within one single healthcare enterprise that is characterized by direct data entry and integration from different sources(tange, 1999). Computer-based patient record system: adds information management tools to provide clinical reminders and alerts, linkage with knowledge sources for healthcare decision support, and analysis of aggregate data(shortliffe & Perreault, 2001) Advantages of CPR Simultaneous access from multiple locations: CPR has a great advantage over the paper-based record since it can be accessed from multiple locations within the health care facility. Depending on the system security configuration, CPR may also be accessible from distant locations e.g. a physician dialing in to the hospital information system from his/her office or home.

5 5 Legibility: documentation in a CPR is more legible because it is recorded as printed text rather than as hand writing, and it is better organized because software display structure is imposed on the input. Variety of views on data: with the current computer technology, data can be displayed in many different formats e.g. laboratory data can be displayed as numerical figures or graphical representation against time (flow sheets). Support of structured data entry: this usually results in collection of more reliable and more complete data. The design of the structured data input interface plays a major role in a successful implementation of structured data entry. Decision support: CPRs commonly supports both decisions related to the diagnosis of a disease on the basis of individual data, and decision related to the therapy on the basis of the available data evidence. Electronic data exchange and sharing care support: as the patient care is becoming more distributed among multidisciplinary health care professionals, communication and exchange of patient data that are attached electronically to the CPR is becoming an essential part of health delivery systems (Bemmel et al., 1997) Support to Clinical Epidemiological Research: CPR systems are used in epidemiological researches in three ways. First, as a sampling tool, where patients are selected from a trial population using the existing database. Second, as a data collection tool, where certain specific clinical data of the selected sample need to be retrieved. Third, as a registration tool, when the CPR system has the capabilities to register data that are used for research project, it assists the researcher in professional data management (Bemmel et al., 1997).

6 6 Errors improvement & prevention: CPR systems must apply validity checks. A number of different types of checks apply to clinical data. Range checks can detect or prevent entry of values that are out of range. Pattern checks verify that entered data have a required pattern. Computed checks can verify that the values have correct mathematical relationship. Consistency checks can detect errors by comparing entered data. Delta checks warn of large and unlikely differences between the values of new result and the previous one. Spelling checks verify the spelling of individual words(shortliffe & Perreault, 2001). Improves both the completeness of clinical documentation and documentations of clinical decisions.(tang et al., 1999) May improves compliance with recommended preventive services: the use of structured data entry in CPR results in statistically significant improvement in the documentation of key elements of health maintenance examinations (Shiffman et al., 1999). Computer generated patient held record (PHR), can facilitate information and responsibility sharing to improve patient decision making, and participation in health promotion, prevention and disease management(liaw et al., 1998). 3. Free text and Medical narratives:

7 7 Clinicians have been using words, terms, and clinical vocabularies since the modern patient record began. Each health care discipline taught the students about the correct vocabulary to use in the processes of documenting the identification of a patient problem, goals for treatment, interventions or therapies to use, and patient response to treatment. The advent of the CPR has fundamentally changed this practice. Standardized vocabularies are needed to support the entry of clinical data, and enhance communication between clinicians. Coded terminologies are needed by the computer database in order to store and retrieve data. As standardized terminologies occur, the richness of the natural language decreases and the documenting habits of the clinician is changed. Clinician do not cope well with loss and forced habits changes(warren, Collins, Sorrentino, & Campbell, 1998). Medical narratives are flexible, expressive, and familiar to clinicians. In addition, it has also been shown that the use of medical text narratives in medical record improves patient physician communication(lovis, Baud, & Planche, 2000). However, with current technology, computers are able to make only limited use of information stored in free text format. Even with the evolution of Natural language processing (NLP), it is still considered a nascent science(shiffman et al., 1999). Computerized Medical narratives can be fully retrieved if the narrative is divided into labeled segments that can be arranged according to both source oriented and problem oriented format, however, there is a trade-off between searching and reading: too much detailed segments will delay the retrieval(tange, 1999).

8 8 4. Coded Data: Computer-based patient data which are represented in coded form have a variety of uses, including direct patient care, statistical reporting, decision support, and clinical research. The typical approach is to encode the information using some standard terms taken from a controlled vocabulary. This need for a controlled vocabulary has been recognized for decades. Although many coding schemes have been developed and used in CPRs, None of those schemes support all the desired functions. The lack of standards for coding patient data is one of the greatest impediments to medical computing today. This has forced application developers to create their own coding schemes for systems, some of these schemes have been proposed as possible standard for the future. There are two main uses of Coding schemes Abstracting patient data: This has been carried out long before the advent of computers. Because the coding represents only a simplified synopsis of information extracted from the record, this kind of coding is referred to as abstraction. The archetypal coding system is International Classification of Disease (ICD), which was first published in It has been revised at roughly 10 years intervals. Other Major coding schemes are usually presented in terms of their compatibility with ICD and their ability to resolve some of the ICD s problems with increased granularity or coverage of a particular domain.

9 9 Major coding schemes, include : 1) ICD-9-CM (clinical modification) 2) DRG (Diagnosis Related Group) 3) ICPC (International Classification of Primary care) 4) CPT (Current Procedural Terminology) 5) DSM (Diagnostic and Statistical Manual of Mental Disorders) 6) SNOMED (Systemized Nomenclature of Medicine) 7) UMLS (Unified Medical Language System) 8) MeSH (Medical Subject Headings) The Details of above coding schemes are beyond the scope of this paper, and are will described by J. J. Cimino in his review article(cimino, 1996) Provision of controlled vocabulary to support coding of detailed patient data The first coding system which attempted to provide terms for a broad range of clinical domains was (SNOMED), in its greatly expanded version: the systematized Nomenclature of Human and Veterinary Medicine SNOMED international. SNOMED consists of set of axes, each of which serves as a taxonomy for a specific set of concepts (organisms, disease, procedures, etc.). Coding patient information is accomplished through combing terms from multiple axes (post-coordination) to represent complex terms. The main problem with SNOMED is that it is too expressive. Because there are few rules about how the post-coordination coding should be done, the same clinical findings could be represented differently by different codes. This is very frustrating to system developers.

10 10 Other schemes were The Read Clinical Codes, Gabrrieli Medical Nomenclature, and the GALEN project in Europe. For sometimes the NLM developed the Unified Medical Language System (UMLS), however, the NLM has acknowledged that UMLS doesn t serve clinical coding well (Cimino, 1996) Finally, Vocabulary servers have become a research issue in their own right. The servers are intended to provide open, distributed health care systems with information about up-to-date vocabulary content. Groups working on Vocabulary servers are GALEN, NLM, the University of Utah, and Stanford University(Cimino, 1996). 5. Discussion: The capturing format of clinical data is an important problem in the design and development of CPR. Clinical data can be captured as free text, coded data, or a combination of both. From what was stated earlier in this paper, it is obvious that clinicians would prefer to continue with the traditional method of capturing clinical data as free text. Free text has the flexibility and the richness of expression; however, when used in CPR, it has two major disadvantages. First, with the current state of computer technology, free text can not be utilized in an efficient manner for other CPR applications, although the evolution of natural language processing may eventually ameliorate this problem. The

11 11 second disadvantage is that free text fields can change the meaning of coded data. In their study (Hogan & Wagner, 1996) found out that free text entry made by clinician during the recording of medication data changed the meaning and lowered the accuracy of coded data for the decision support system. Coded data capturing has the advantages of acquiring classified and standardized data, thus it is easy to retrieve data and query the data base for very specific information and repots, in addition to making the information readily available for other application in the CPR, especially epidemiological researches. Coded data has some major disadvantages. First, it limits the user to a predefined vocabulary which might not meet his or her goal. Second, Coding can be time-consuming and tedious. Third, there is a potential for coding error which might be difficult to detect(shortliffe & Perreault, 2001). In general developers of health care applications have difficulty using the existing coding systems. For example, the developers of TMR (The Medical Record) at Duke University have explicitly rejected standard vocabularies and resorted to developing their own vocabularies. In some cases, they are created in an add hoc manner, adding coded terms as needed. The HELP system in use at the LDS Hospital in Salt Lake City, Utah encodes almost all the data in the PTXT data dictionary. This dictionary is a strictly hierarchical. While PTXT

12 12 is used successfully by the on-line decision support capabilities of the help system, it has proven difficult to use for diagnostic expert system developed by the same research group. The Implementation of RMRS (The Regienstrief Medical record systems) at the University of Indiana also uses controlled vocabulary for representing a portion of its data. This particular vocabulary construction task was complicated by the need to coordinate terminologies from four different hospitals. Despite the effort expended to make RMRS inter-institutional, it remains institution-dependant (Cimino, 1996). This particular example demonstrates the difficulty to establish and implement standardized controlled vocabularies. All of the above examples make use of coding schemes, which while varying in their richness of details, all share a strict hierarchical structure. The approach at the University of Manchester has been quite different. In the PEN & PAD (Practitioner Entering Notes and Practitioner Accessing Data), the vocabulary model is based on Structured Meta Knowledge (SMK), which allows for a variety of vocabulary-related information and allows multiple hierarchies. Although there is extra effort exerted in developing the system, it ultimately pays off in terms of the richness of expressivity and flexibility obtained (Cimino, 1996). The paper of (van Mulligen, Stam, & van Ginneken, 1998), describes a prototype clinical data entry application that combines free text and structured data entry in one single application and allows clinician to smoothly switch between these two different input

13 13 styles. A knowledge base utilizing semantic network was used. This application was installed at various centers in Europe, and the first response to this mixture of structured data entry and free text was positive. 6. Conclusion: The use of coded data versus free text remains a great challenge in the design of CPR. There is a need for an internationally standardized controlled vocabulary in order to make coding of data expressive, versatile, and acceptable by the clinical users. The use of free text might become more yielding in the near future as the technology of free text utilization advances. At this time, with the available technology and resources, supplementing coded data with free text might be a temporary solution, to get a reasonable balance between users needs and the ability to integrate different components of the computer-based patient record system.

14 14 7. References: Bemmel, J. H. v., Musen, M. A., & Helder, J. C. (1997). Handbook of medical informatics. AW Houten, NetherlandsHeidelberg, Germany: Bohn Stafleu Van Loghum ;Springer Verlag. Cimino, J. J. (1996). Review paper: coding systems in health care. Methods Inf Med, 35(4-5), Hogan, W. R., & Wagner, M. M. (1996). Free-text fields change the meaning of coded data. Proc AMIA Annu Fall Symp, Liaw, S. T., Radford, A. J., & Maddocks, I. (1998). The impact of a computer generated patient held health record. Aust Fam Physician, 27 Suppl 1, S Lovis, C., Baud, R. H., & Planche, P. (2000). Power of expression in the electronic patient record: structured data or narrative text? Int J Med Inf, 58-59, Shiffman, R. N., Branndt, C. A., & Freeman, B. G. (1999). Transition to a Computer-Based Record Using Scannable Encounter Forms. In Yearbook of Medical Informatics 1999 (pp ). Stuttgart: Schattauer Verlagsgesellschaft mbh. Shortliffe, E. H., & Perreault, L. E. (2001). Medical informatics : computer applications in health care and biomedicine (2nd ed ed.). New York: Springer. Tang, P. C., LaRosa, M. P., & Gorden, S. M. (1999). Use of computer-based records, completeness of documentation, and appropriateness of documented clinical decisions. J Am Med Inform Assoc, 6(3), Tange, H. J. (1999). Consultation of medical narratives in the electronic medical record. Methods Inf Med, 38(4-5), van der Lei, J., Moorman, P. W., & Musen, M. A. (1999). Electronic patient records in medical practice: a multidisciplinary endeavor. Methods Inf Med, 38(4-5), van Mulligen, E. M., Stam, H., & van Ginneken, A. M. (1998). Clinical data entry. Proc AMIA Symp, Warren, J. J., Collins, J., Sorrentino, C., & Campbell, J. R. (1998). Just-in-time coding of the problem list in a clinical environment. Proc AMIA Symp, Wigertz, O. B. (2001). Computer-based Patient Records. In Yearbook of Medical Informatics (pp ). Stuttgart: Schattauer Verlagsgesellschaft mbh. Bemmel, J. H. v., Musen, M. A., & Helder, J. C. (1997). Handbook of medical informatics. AW Houten, Netherlands Heidelberg, Germany: Bohn Stafleu Van Loghum ; Springer Verlag. Cimino, J. J. (1996). Review paper: coding systems in health care. Methods Inf Med, 35(4-5), Hogan, W. R., & Wagner, M. M. (1996). Free-text fields change the meaning of coded data. Proc AMIA Annu Fall Symp, Liaw, S. T., Radford, A. J., & Maddocks, I. (1998). The impact of a computer generated patient held health record. Aust Fam Physician, 27 Suppl 1, S39-43.

15 Lovis, C., Baud, R. H., & Planche, P. (2000). Power of expression in the electronic patient record: structured data or narrative text? Int J Med Inf, 58-59, Shiffman, R. N., Branndt, C. A., & Freeman, B. G. (1999). Transition to a Computer-Based Record Using Scannable Encounter Forms, Yearbook of Medical Informatics 1999 (pp ). Stuttgart: Schattauer Verlagsgesellschaft mbh. Shortliffe, E. H., & Perreault, L. E. (2001). Medical informatics : computer applications in health care and biomedicine (2nd ed ed.). New York: Springer. Tang, P. C., LaRosa, M. P., & Gorden, S. M. (1999). Use of computer-based records, completeness of documentation, and appropriateness of documented clinical decisions. J Am Med Inform Assoc, 6(3), Tange, H. J. (1999). Consultation of medical narratives in the electronic medical record. Methods Inf Med, 38(4-5), van der Lei, J., Moorman, P. W., & Musen, M. A. (1999). Electronic patient records in medical practice: a multidisciplinary endeavor. Methods Inf Med, 38(4-5), van Mulligen, E. M., Stam, H., & van Ginneken, A. M. (1998). Clinical data entry. Proc AMIA Symp, Warren, J. J., Collins, J., Sorrentino, C., & Campbell, J. R. (1998). Just-in-time coding of the problem list in a clinical environment. Proc AMIA Symp, Wigertz, O. B. (2001). Computer-based Patient Records, Yearbook of Medical Informatics (pp ). Stuttgart: Schattauer Verlagsgesellschaft mbh. 15

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