Difference between revisions of "Problem List Automation"
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| − | Problem list population in Electronic Health Records (EHR)is integral in driving many key functions of clinical decision support systems. Despite this, problem lists are often underutilized and lack the necessary data to unlock the benefits of programmed support. The reasons for this vary, but many factors for this failure revolve around the time constraints and workflow of clinical care providers. Providers often only view patient care in an episodic fashion rather than around a longitudinal framework in which a problem list would seem to hold more merit. Methodologies surrounding secondary entry of problem list data have been proposed to address this deficit. Goals of these methodologies include timely, precise, and accurate data entry with minimal workload burden on responsible providers. Natural | + | '''Introduction/Background''' |
| + | Problem list population in Electronic Health Records (EHR)is integral in driving many key functions of clinical decision support systems. Despite this, problem lists are often underutilized and lack the necessary data to unlock the benefits of programmed support. The reasons for this vary, but many factors for this failure revolve around the time constraints and workflow of clinical care providers. Providers often only view patient care in an episodic fashion rather than around a longitudinal framework in which a problem list would seem to hold more merit. Methodologies surrounding secondary entry of problem list data have been proposed to address this deficit. Goals of these methodologies include timely, precise, and accurate data entry with minimal workload burden on responsible providers. Natural Language Processing (NLP) has been proposed as a potential solution to this issue, particularly because of the attractive notion that natural language serves as the input medium in these systems. | ||
| + | Natural Language Processing is a newer research technique that attempts to automate the understanding of natural human language via the processing and complex computation of unstructured free text. Several research groups have postulated and attempted to demonstrate that refined NLP systems can be successful in parsing structured data from the clinical narrative text that resides in EHRs. NLP systems process text looking for patterns and candidate phrases that can be mapped to controlled terminologies such as SNOMED and the UMLS Metathesaurus. Codified terms can then drive clinical decision system support to provide beneficial care that may otherwise have been overlooked or forgotten. | ||
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| + | '''Early Attempts At Computerized Problem List Generation''' | ||
| + | Scherpbier et al (1) created a very early and very simple computerized system to capture a clinical problem list in their EHR. they utilized pick lists of frequently used diagnoses that were generated based on provider type and displayed depending on provider role. One limitation of this method is that the number of items on the pick list was limited, representing a technical limitation that was present in their clinical information system at the time. The incentive for the user to utilize the pick list was that in doing so they increase their efficiency in billing and discharge documentation. Franco et al devised a similar method for computerized entry utilizing patient data to drive pick lists in a neonatal system (2). | ||
| + | Wang, Bates et al's group at Partners Healthcare Systemtook a different approach, developing a local problem list dictionary and a data entry tool that was integrated into an outpatient EHR system. The system employed a search algorithm that was employed via three different interfaces. Their study included an analysis of coding and utilization rates and the effect the various interfaces had on these rates.they found that the user interface had a very significant effect on user acceptance and deficiencies in the data dictionary was also a large contributor to uncoded problems. In addition, their group also struggled increasing utilization rates despite implementing a number of decision-support incentives into their system (3). | ||
| + | '''MetaMep and MedLee''' | ||
| + | Perhaps the two best known NLP mapping systems are MetaMap and MedLee. MetaMap was developed by Alan Aronson at the UUS National Library of Medicine and maps free text to UMLS terms. MedLee (Medical Language Extraction and Encoding) is Friedman et al's alternative approach that differs by parsing entire documents. | ||
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| + | '''Current Limitations of Natural Language Processing''' | ||
| + | scalability | ||
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| + | '''References''' | ||
| + | 1.) Scherpbier, H., et al., A simple approach to physician entry of patient problem list. Proc Annu Symp Comput Appl Med Care, 1994: p. 206-10. | ||
| + | 2.) Franco, A., et al., "NEONATE"--an expert application for the "HELP" system: comparison of the computer's and the physician's problem list. J Med Syst, 1990. 14(5): p. 297-306. | ||
| + | 3.) Wang, S., et al., Automated coded ambulatory problem lists: evaluation of a vocabulary and a data entry tool. Int J Med Inform, 2003. 72(1-3): p. 17-28. | ||
| + | 4.) | ||
Revision as of 20:57, 22 November 2010
Introduction/Background Problem list population in Electronic Health Records (EHR)is integral in driving many key functions of clinical decision support systems. Despite this, problem lists are often underutilized and lack the necessary data to unlock the benefits of programmed support. The reasons for this vary, but many factors for this failure revolve around the time constraints and workflow of clinical care providers. Providers often only view patient care in an episodic fashion rather than around a longitudinal framework in which a problem list would seem to hold more merit. Methodologies surrounding secondary entry of problem list data have been proposed to address this deficit. Goals of these methodologies include timely, precise, and accurate data entry with minimal workload burden on responsible providers. Natural Language Processing (NLP) has been proposed as a potential solution to this issue, particularly because of the attractive notion that natural language serves as the input medium in these systems. Natural Language Processing is a newer research technique that attempts to automate the understanding of natural human language via the processing and complex computation of unstructured free text. Several research groups have postulated and attempted to demonstrate that refined NLP systems can be successful in parsing structured data from the clinical narrative text that resides in EHRs. NLP systems process text looking for patterns and candidate phrases that can be mapped to controlled terminologies such as SNOMED and the UMLS Metathesaurus. Codified terms can then drive clinical decision system support to provide beneficial care that may otherwise have been overlooked or forgotten.
Early Attempts At Computerized Problem List Generation Scherpbier et al (1) created a very early and very simple computerized system to capture a clinical problem list in their EHR. they utilized pick lists of frequently used diagnoses that were generated based on provider type and displayed depending on provider role. One limitation of this method is that the number of items on the pick list was limited, representing a technical limitation that was present in their clinical information system at the time. The incentive for the user to utilize the pick list was that in doing so they increase their efficiency in billing and discharge documentation. Franco et al devised a similar method for computerized entry utilizing patient data to drive pick lists in a neonatal system (2). Wang, Bates et al's group at Partners Healthcare Systemtook a different approach, developing a local problem list dictionary and a data entry tool that was integrated into an outpatient EHR system. The system employed a search algorithm that was employed via three different interfaces. Their study included an analysis of coding and utilization rates and the effect the various interfaces had on these rates.they found that the user interface had a very significant effect on user acceptance and deficiencies in the data dictionary was also a large contributor to uncoded problems. In addition, their group also struggled increasing utilization rates despite implementing a number of decision-support incentives into their system (3). MetaMep and MedLee Perhaps the two best known NLP mapping systems are MetaMap and MedLee. MetaMap was developed by Alan Aronson at the UUS National Library of Medicine and maps free text to UMLS terms. MedLee (Medical Language Extraction and Encoding) is Friedman et al's alternative approach that differs by parsing entire documents.
Current Limitations of Natural Language Processing
scalability
References 1.) Scherpbier, H., et al., A simple approach to physician entry of patient problem list. Proc Annu Symp Comput Appl Med Care, 1994: p. 206-10. 2.) Franco, A., et al., "NEONATE"--an expert application for the "HELP" system: comparison of the computer's and the physician's problem list. J Med Syst, 1990. 14(5): p. 297-306. 3.) Wang, S., et al., Automated coded ambulatory problem lists: evaluation of a vocabulary and a data entry tool. Int J Med Inform, 2003. 72(1-3): p. 17-28. 4.)
