PatientNarr: Towards generating patient-centric summaries of hospital stays (original) (raw)
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Towards Generating Personalized Hospitalization Summaries
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop
Most of the health documents, including patient education materials and discharge notes, are usually flooded with medical jargons and contain a lot of generic information about the health issue. In addition, patients are only provided with the doctor's perspective of what happened to them in the hospital while the care procedure performed by nurses during their entire hospital stay is nowhere included. The main focus of this research is to generate personalized hospital-stay summaries for patients by combining information from physician discharge notes and nursing plan of care. It uses a metric to identify medical concepts that are Complex, extracts definitions for the concept from three external knowledge sources, and provides the simplest definition to the patient. It also takes various features of the patient into account, like their concerns and strengths, ability to understand basic health information, level of engagement in taking care of their health, and familiarity with the health issue and personalizes the content of the summaries accordingly. Our evaluation showed that the summaries contain 80% of the medical concepts that are considered as being important by both doctor and nurses. Three patient advisors (i.e individuals who are trained in understanding patient experience extensively) verified the usability of our summaries and mentioned that they would like to get such summaries when they are discharged from hospital.
Generating Clinical Notes for Electronic Health Record Systems
2010
Clinical notes summarize interactions that occur between patients and healthcare providers. With adoption of electronic health record (EHR) and computer-based documentation (CBD) systems, there is a growing emphasis on structuring clinical notes to support reusing data for subsequent tasks. However, clinical documentation remains one of the most challenging areas for EHR system development and adoption. The current manuscript describes the Vanderbilt experience with implementing clinical documentation with an EHR system. Based on their experience rolling out an EHR system that supports multiple methods for clinical documentation, the authors recommend that documentation method selection be made on the basis of clinical workflow, note content standards and usability considerations, rather than on a theoretical need for structured data.
mGen - An Open Source Framework for Generating Clinical Documents
Studies in health technology and informatics, 2005
As formalised electronic storage of medical data becomes more and more wide spread the possibility and need for creating human readable presentations for various purposes increases. This paper presents the mGen framework - a general framework for text generation, written in Java and in the process of being open-sourced, that has been developed within the MedView projectThe work presented in this paper was supported by the Swedish Agency for Innovation Systems (VINNOVA).. The framework has been used for several years to generate literally thousands of clinical documents at an oral medicine clinic in Sweden.
Incorporating Personalization Features in a Hospital-Stay Summary Generation System
Proceedings of the 52nd Hawaii International Conference on System Sciences, 2019
Most of the currently available health resources contain vast amount of information that are created by keeping the "general" population in mind, which in reality, might not be useful for anyone. One approach to providing comprehensible health information to patients is to generate summaries that are personalized to each individual. This paper details the design of a personalized hospital-stay summary generation system that tailors its content to the patient's understanding of medical terminologies and their level of engagement in improving their own health. Our summaries were found to cover around 80% of the health concepts that were considered as important by a doctor or a nurse. An online survey conducted on 150 participants verified that our algorithm's interpretation of the personalization parameters is representative of that of a larger population.
Patient Education and Counseling, 2013
We assess the efficacy and utility of automatically generated textual summaries of patients' medical histories at the point of care. Method: Twenty-one clinicians were presented with information about two cancer patients and asked to answer key questions. For each clinician, the information on one of the patients comprised their official hospital records, and for the other patient it comprised summaries that were computer-generated by a natural language generation system from data extracted from the official records. We measured the accuracy of the clinicians' responses to the questions, the time they took to complete them, and recorded their attitude to the computer-generated summaries. Results: Results showed no significant difference in the accuracy of responses to the computergenerated records over the official records, but a significant difference in the time taken to assess the patients' condition from the computer-generated records. Clinicians expressed a positive attitude towards the computer-generated records. Conclusion: AI-based computer-generated textual summaries of patient histories can be as accurate as, and more efficient than, human-produced patient records for clinicians seeking to accurately identify key information about a patients overall history. Practice implications: Computer-generated textual summaries of patient histories can contribute to the management of patients at the point-of-care.
On Evaluation of Automatically Generated Clinical Discharge Summaries
2014
Proper evaluation is crucial for developing high-quality computerized text summarization systems. In the clinical domain, the specialized information needs of the clinicians complicates the task of evaluating automatically produced clinical text summaries. In this paper we present and compare the results from both manual and automatic evaluation of computer-generated summaries. These are composed of sentence extracts from the free text in clinical daily notes – corresponding to individual care episodes, written by physicians concerning patient care. The purpose of this study is primarily to find out if there is a correlation between the conducted automatic evaluation and the manual evaluation. We analyze which of the automatic evaluation metrics correlates the most with the scores from the manual evaluation. The manual evaluation is performed by domain experts who follow an evaluation tool that we developed as a part of this study. As a result, we hope to get some insight into the r...
Using Natural Language Generation Technology to Improve Information Flows in Intensive Care Units
2008
In the drive to improve patient safety, patients in modern intensive care units are closely monitored with the generation of very large volumes of data. Unless the data are further processed, it is difficult for medical and nursing staff to assimilate what is important. It has been demonstrated that data summarization in natural language has the potential to improve clinical decision making; we have implemented and evaluated a prototype system which generates such textual summaries automatically. Our evaluation of the computer generated summaries showed that the decisions made by medical and nursing staff after reading the summaries were as good as those made after viewing the currently available graphical presentations with the same information content. Since our automatically generated textual summaries can be improved by including additional content and expert knowledge, they promise to enhance information exchange between the medical and nursing staff, particularly when integrated with the currently available graphical presentations. The main feature of this technology is that it brings together a diverse set of techniques such as medical signal analysis, knowledge based reasoning, medical ontology and natural language generation. In this paper we discuss the main components of our approach with a critical analysis of their strengths and limitations and present options for improvement to address these limitations.