Summarization from Medical Documents: A Survey

Objective: The aim of this paper is to survey the recent work in medical documents summarization. Background: During the last decade, documents summarization got increasing attention by the AI research community. More recently it also attracted the interest of the medical research community as well, due to the enormous growth of information that is available to the physicians and researchers in medicine, through the large and growing number of published journals, conference proceedings, medical sites and portals on the World Wide Web, electronic medical records, etc. Methodology: This survey gives first a general background on documents summarization, presenting the factors that summarization depends upon, discussing evaluation issues and describing briefly the various types of summarization techniques. It then examines the characteristics of the medical domain through the different types of medical documents. Finally, it presents and discusses the summarization techniques used so far in the medical domain, referring to the corresponding systems and their characteristics. Discussion and conclusions: The paper discusses thoroughly the promising paths for future research in medical documents summarization. It mainly focuses on the issue of scaling to large collections of documents in various languages and from different media, on personalization issues, on portability to new sub-domains, and on the integration of summarization technology in practical applicationsComment: 21 pages, 4 table

Towards Semantic Search and Inference in Electronic Medical Records

Background This paper presents a novel approach to searching electronic medical records that is based on concept matching rather than keyword matching. Aims The concept-based approach is intended to overcome specific challenges we identified in searching medical records. Method Queries and documents were transformed from their term-based originals into medical concepts as defined by the SNOMED-CT ontology. Results Evaluation on a real-world collection of medical records showed our concept-based approach outperformed a keyword baseline by 25% in Mean Average Precision. Conclusion The concept-based approach provides a framework for further development of inference based search systems for dealing with medical data

Tinjauan Aspek Ergonomi Pada Ruang Penyimpanan Berdasarkan Standart Nasional Akreditasi Rumah Sakit (SNARS) Edisi 1 Di RSU Tere Margareth Medan Tahun 2020

In SNARS edition 1, it is known that medical record documents in paper or electronic form must be kept safe and confidential so they must be stored in a location that is protected from water, fire, heat, and other damage and protected from interference with access and unauthorized use. The purpose of this study was to determine the ergonomics aspect based on the National Standard for Hospital Accreditation (SNARS) Edition 1. The method used was observation and interviews with a descriptive type of research located at Tere Margareth General Hospital Medan in July 2020. The population in this study was the physical aspect. Ergonomics and medical records officers in the storage room as many as 2 people using the total sampling technique. is the storage room for medical record files at the Tere Margareth hospital that does not meet accreditation standards because there are still problems that occur related to room security which can be assessed based on the standard of ergonomic aspects. Ask the hospital to pay more attention to the state of the medical record storage room in order to meet the standard assessment elements of information management and medical records in SNARS Edition 1

DEXTER: An end-to-end system to extract table contents from electronic medical health documents

In this paper, we propose DEXTER, an end to end system to extract information from tables present in medical health documents, such as electronic health records (EHR) and explanation of benefits (EOB). DEXTER consists of four sub-system stages: i) table detection ii) table type classification iii) cell detection; and iv) cell content extraction. We propose a two-stage transfer learning-based approach using CDeC-Net architecture along with Non-Maximal suppression for table detection. We design a conventional computer vision-based approach for table type classification and cell detection using parameterized kernels based on image size for detecting rows and columns. Finally, we extract the text from the detected cells using pre-existing OCR engine Tessaract. To evaluate our system, we manually annotated a sample of the real-world medical dataset (referred to as Meddata) consisting of wide variations of documents (in terms of appearance) covering different table structures, such as bordered, partially bordered, borderless, or coloured tables. We experimentally show that DEXTER outperforms the commercially available Amazon Textract and Microsoft Azure Form Recognizer systems on the annotated real-world medical datase

Building realistic potential patient queries for medical information retrieval evaluation

To evaluate and improve medical information retrieval, benchmarking data sets need to be created. Few benchmarks have been focusing on patients’ information needs. There is a need for additional benchmarks to enable research into effective retrieval methods. In this paper we describe the manual creation of patient queries and investigate their automatic generation. This work is conducted in the framework of a medical evaluation campaign, which aims to evaluate and improve technologies to help patients and laypeople access eHealth data. To this end, the campaign is composed of different tasks, including a medical information retrieval (IR) task. Within this IR task, a web crawl of medically related documents, as well as patient queries are provided to participants. The queries are built to represent the potential information needs patients may have while reading their medical report. We start by describing typical types of patients’ information needs. We then describe how these queries have been manually generated from medical reports for the first two years of the eHealth campaign. We then explore techniques that would enable us to automate the query generation process. This process is particularly challenging, as it requires an understanding of the patients’ information needs, and of the electronic health records. We describe various approaches to automatically generate potential patient queries from medical reports and describe our future development and evaluation phase

Reduction of Unnecessary Scanning to Lower Costs While Preserving the Integrity of the Legal Health Record

The purpose of the paper is to explain the efforts taken at Rocky Mountain Clinic to reduce scanning of unnecessary documents into their Electronic Health Record (EHR). An EHR is a digital version of a patient’s medical paper chart. EHRs are real-time, patient-centered records that make information available instantly and securely to authorized users. (United States Department of Health and Human Services Office of the National Coordinator for Health Information Technology, 2017) The use of an EHR requires paper documents to be scanned into the system so they are available electronically within the patient’s EHR. Research was completed to determine documents required as part of the patient’s Legal Health Record (LHR). A LHR is any item, collection, or grouping of a patient’s individually identifiable health information that is created, received or maintained, in paper, or electronic form, by or for SCL Health in their ordinary course of business in any medium, collected and directly used in documenting health status. (SCL Health, 2016) The information obtained from the research was used as a guideline to determine documents unnecessarily scanned by the clinics. Analysis of unnecessary documents scanned during 2016 was completed and a review of the requirements of a LHR led to a plan to reduce documents unnecessarily scanned. The project focused on the reduction of three types of documents in the initial phase; extended care documents, other facility miscellaneous documents and consent forms. A time study was completed on the tasks associated with scanning documents, and a cost analysis was prepared to show the labor costs for scanning the unnecessary documents. The goal of the project was to improve efficiency and reduce costs associated with time spent on scanning unnecessary documents. Reducing time spent on scanning unnecessary documents allows associates to focus on scanning pertinent documents or allows time to complete other tasks. Appropriate documents should be scanned daily to ensure the documents are available in the patient’s record timely. The results of the study showed a slight reduction in unnecessary documents scanned in a short period of time. The results of the project were presented to the HIM Ambulatory team for use in working towards a system wide change. SCL Health plans on finalizing a system wide policy as well as a guideline for scanning appropriate documents. The policy and guideline will be rolled out to all clinics within the system. Follow up with Rocky Mountain Clinic will be provided to explain the progress made and they will be encouraged to continue making improvements

Characterization of patients with idiopathic normal pressure hydrocephalus using natural language processing within an electronic healthcare record system

OBJECTIVE: Idiopathic normal pressure hydrocephalus (iNPH) is an underdiagnosed, progressive, and disabling condition. Early treatment is associated with better outcomes and improved quality of life. In this paper, the authors aimed to identify features associated with patients with iNPH using natural language processing (NLP) to characterize this cohort, with the intention to later target the development of artificial intelligence–driven tools for early detection. / METHODS: The electronic health records of patients with shunt-responsive iNPH were retrospectively reviewed using an NLP algorithm. Participants were selected from a prospectively maintained single-center database of patients undergoing CSF diversion for probable iNPH (March 2008–July 2020). Analysis was conducted on preoperative health records including clinic letters, referrals, and radiology reports accessed through CogStack. Clinical features were extracted from these records as SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) concepts using a named entity recognition machine learning model. In the first phase, a base model was generated using unsupervised training on 1 million electronic health records and supervised training with 500 double-annotated documents. The model was fine-tuned to improve accuracy using 300 records from patients with iNPH double annotated by two blinded assessors. Thematic analysis of the concepts identified by the machine learning algorithm was performed, and the frequency and timing of terms were analyzed to describe this patient group. / RESULTS: In total, 293 eligible patients responsive to CSF diversion were identified. The median age at CSF diversion was 75 years, with a male predominance (69% male). The algorithm performed with a high degree of precision and recall (F1 score 0.92). Thematic analysis revealed the most frequently documented symptoms related to mobility, cognitive impairment, and falls or balance. The most frequent comorbidities were related to cardiovascular and hematological problems. / CONCLUSIONS: This model demonstrates accurate, automated recognition of iNPH features from medical records. Opportunities for translation include detecting patients with undiagnosed iNPH from primary care records, with the aim to ultimately improve outcomes for these patients through artificial intelligence–driven early detection of iNPH and prompt treatment

Electronic Health Records: Cure-all or Chronic Condition?

Computer-based information systems feature in almost every aspect of our lives, and yet most of us receive handwritten prescriptions when we visit our doctors and rely on paper-based medical records in our healthcare. Although electronic health record (EHR) systems have long been promoted as a cost-effective and efficient alternative to this situation, clear-cut evidence of their success has not been forthcoming. An examination of some of the underlying problems that prevent EHR systems from delivering the benefits that their proponents tout identifies four broad objectives - reducing cost, reducing errors, improving coordination and improving adherence to standards - and shows that they are not always met. The three possible causes for this failure to deliver involve problems with the codification of knowledge, group and tacit knowledge, and coordination and communication. There is, however, reason to be optimistic that EHR systems can fulfil a healthy part, if not all, of their potential