Relevant bibliographies by topics / Patient records

Academic literature on the topic 'Patient records'

Author: Grafiati
Published: 4 June 2021
Last updated: 28 January 2023

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Journal articles on the topic "Patient records"

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B N, Beena Ullala Mata, Rameshwara G N, Naveen Kumar G, and Raghavendra B. "Effects of Computerized Patient Records on Patients." Bonfring International Journal of Man Machine Interface 3, no. 3 (July 31, 2015): 25–29. http://dx.doi.org/10.9756/bijmmi.8068.

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Knaup, P. "Section 2: Patient Records: Electronic Patient Records and their Benefit for Patient Care." Yearbook of Medical Informatics 15, no. 01 (August 2006): 40–42. http://dx.doi.org/10.1055/s-0038-1638475.

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SummaryTo summarize current excellent research in the field of patient records.Synopsis of the articles selected for the IMIA Yearbook 2006.Current research in the field of patient records analyses users’ needs and attitudes as well as the potential and limitations of electronic patient record systems. Particular topics are the questions physicians have when assessing patients during ward rounds, the timeliness of results when ordered electronically, the quality of documenting haemophilia home therapy, attitudes towards patient access to health records and adequate strategies for record linkage in dependence on the intended purpose.The best paper selection of articles on patient records shows examples of excellent research on methods used for the management of patient records and for processing their content as well as assessing the potential, limitations of and user attitudes towards electronic patient record systems. Computerized patient records are mature, so that they can contribute to high quality patient care and efficient patient management.
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Cockerell, Tom. "PATIENT RECORDS." Journal of the American Dental Association 140, no. 4 (April 2009): 402–3. http://dx.doi.org/10.14219/jada.archive.2009.0181.

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Ashton, Norman. "Patient records." Lancet 345, no. 8953 (April 1995): 861. http://dx.doi.org/10.1016/s0140-6736(95)92997-5.

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Potter, H. Phelps. "Computerizing Patient Records." Annals of Internal Medicine 116, no. 11 (June 1, 1992): 959. http://dx.doi.org/10.7326/0003-4819-116-11-959_1.

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King, Alison B. "Safeguarding patient records." Nutrition 12, no. 10 (October 1996): 726–27. http://dx.doi.org/10.1016/s0899-9007(96)00219-5.

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Munday, Dave. "Electronic patient records." Nursing Management 16, no. 7 (October 26, 2009): 8. http://dx.doi.org/10.7748/nm.16.7.8.s12.

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Laugharne, Richard, and Claire Henderson. "Medical records: Patient-held records in mental health." Psychiatric Bulletin 28, no. 2 (February 2004): 51–52. http://dx.doi.org/10.1192/pb.28.2.51.

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‘But don't they get lost?’ This is usually the first comment made when the authors mention the use of patient-held records (PHRs) to colleagues. Nevertheless, PHRs have been used in mental health care as well as several other settings, including services for diabetes, cancer, maternity and child health. In some of these services, including mental health, PHRs have been an addition to clinician held standard notes, whereas in others the patient holds the only record for their care. The main purposes of introducing PHRs have been to empower patients with a sense of ownership of their care and to improve communication, between both patients and clinicians, as well as between different clinicians involved in that person's care (Laugharne & Stafford, 1996).
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Knaup, P., D. Schmidt, and M. W. M. Jaspers. "Section 2: Patient Records: The Computerized Patient Record: Where Do We Stand?" Yearbook of Medical Informatics 15, no. 01 (August 2006): 29–39. http://dx.doi.org/10.1055/s-0038-1638464.

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SummaryTo provide an overview of trends in research, developments and implementations of the computerized patient record (CPR) of the last two years.We surveyed the medical informatics literature, spanning the years 2004-2005, focusing on publications on CPRs.The main trends revealed were: 1) the development of technologies to realize privacy and security goals or remote data entry and access to CPRs; 2) investigations into how to enhance the quality and reuse of CPR data; 3) the development and evaluation of decision support functions to be integrated with CPRs; 4) evaluations of the impact of CPRs on clinicians, patients, clinical work settings and patient outcomes; and 5) the further development and use of standards to move towards shared electronic health records (EHRs).The CPR is playing a growing part in medical informatics research and evaluation studies, but the goal of establishing a comprehensive lifelong EHR is still a long way off. In moving forward to EHRs, convergence of EHR standards seems required to realize true interoperability of health care applications. User acceptance of present-day CPRs (for all categories of users) and compatibility with work patterns has not been achieved yet, and can only be realized by giving these goals high priority. This will require substantial resources for in-depth work flow analysis, development and evaluation of CPRs. Besides this, the implementation of effective CPRs asks for health care organizations that are willing to invest in new developments and to contribute to evaluation studies, to further improve CPRs’ functionalities and enhance their use in practice.Haux R, Kulikowski C, editors. IMIA Yearbook of Medical Informatics 2006.
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Kilsdonk, Anton C. M., and Albert van der Werff. "Structuring the patient record: NUCLEUS (customisation environment for multimedia integrated patient records)." Computer Methods and Programs in Biomedicine 45, no. 1-2 (October 1994): 127–30. http://dx.doi.org/10.1016/0169-2607(94)90032-9.

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Dissertations / Theses on the topic "Patient records"

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Stallemo, Kjetil. "Patient friendly Presentation of Electronic Patient Records." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2008. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-8870.

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Reading an electronic patient record (EPR) is a very challenging task because of the medical jargons, which are almost impossible to understand for the layman. This becomes a highly relevant challenge because of the more extensive use of the internet to get medical information. Also the Norwegian laws state that the patient has the right to read his or her own EPR. A master thesis executed in 2006, and a specialization project in 2007 addressed this subject and developed a prototype for adapting EPRs to a patient presentation. This thesis continues this work and aims to extend the system with more functionality and improve the translation of the EPRs. The main issues discussed in the thesis are how disambiguating between Norwegian words and medical terms, provide summaries of EPRs, and supply the patient with external information about his or her health condition. In addition the refined user interface from the specialization project was implemented. The conclusion of this thesis is that the Support Vector Machine classifier with character bigrams provides good and accurate disambiguation between Norwegian words and medical terms. The external information functionality provides correct and quality assured information from the patient hand book. There are still some issues, and possible improvements on providing only precise and relevant articles. Summarizing of EPRs is achieved through named entity extraction of ICD codes, and then presenting the codes together with their corresponding descriptions. This implementation seems to be accurate, correct, and precise.

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Østerlund, Carsten Svarrer 1965. "Documenting dreams : patient-centered records versus practice-centered records." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/8005.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, February 2003.
Includes bibliographical references (leaves 261-268).
This thesis explores how doctors and nurses use documents to share their knowledge within and across healthcare settings. In addressing this question I draw on a 15-month, multi-sited ethnographic study in several pediatric health care settings, following patients from primary care clinics, to emergency rooms, and in-patient units. The analysis focuses on the practices that go into documenting patients' histories and care, which include recordings on various on-line systems, preprinted forms, and whiteboards. By combining the previously distinct lenses of 1) knowing in practice, 2) time-space analysis of social interaction, and 3) communicative genre and genre systems, I suggest that doctors and nurses employ various types of document genres to manage, not only their distributed knowing about patients' care, but also their own movements across time-space. I outline a perspective on documents and knowing which attempts to highlight the role of human practice in how people use documents to coordinate their activities, share their capabilities, and get things done in complex distributed organizational work. The data suggest that doctors and nurses use medical documents as maps and itineraries to organize their distributed work practices. Doctors and nurses record patients' histories many times in different documents, with each document serving as a map and itinerary for a different constituency of people. Each of these documents is rarely used in isolation from other documents. Doctors and nurses constantly recombine the documents they use, which allows them to both appropriate documents from other settings into their local organization of work and build unique local combinations of documents.
(cont.) I introduce the concept of "re-localizing" to describe how doctors and nurses use documents to share their knowing within and across healthcare settings. Re-localization involves many healthcare professionals' parallel rewriting of a patient's history based on a recombination of each other's maps and itineraries and the patient's own accounts. By integrating the concrete case and the maps and itineraries based on those cases the notion of relocalization overcomes the dichotomy between the abstract and the situated, the local and global. Documents are not seen as mere vessels for abstract representations, but integral parts of distributed knowing within and across settings.
by Carsten Svarrer Østerlund.
Ph.D.
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Al-Busaidi, Asma Ali S. "Personalising patient Internet searching using electronic patient records." Thesis, Cardiff University, 2008. http://orca.cf.ac.uk/54651/.

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The research reported in this thesis addresses a patient's information requirements when searching the Internet for health information. A patient's lack of information about his/her health condition and its care is officially acknowledged and traditional patient information sources do not address today's patient information needs. Internet health information resources have become the foremost health information platform. However, patient Internet searching is currently manual, uncustomised and hindered by health information vocabulary and quality challenges. Patient access to quality Internet health information is currently ensured through national health gateways, medical search engines, third-party accredited search engines and charity health websites. However, such resources are generic, i.e. do not cater for a patient particular information needs. In this study, we propose personalising patient Internet searching by enabling a patient's access to their Electronic Patient Records (EPRs) and using this EPR data in Internet information searching. The feasibility of patient access to EPRs has recently been promoted by national health information programmes. Very recently, in the literature, there are reports about pilot studies on personal Health Record (PHR) systems that offer a patient online access to their medical records and related health information. However, the extensive literature searching shows no reports about patient-personalised search engines, within the reported PHR prototypes, that utilise a patient's own data to personalise the search features for a patient especially with regard to health information vocabulary needs. The thesis presents a novel approach to personalising patient information searching based on linking EPR data with relevant Internet Information resources, integrating medical and lay perspectives in a diagnosis vocabulary that distinguishes between medical and lay information needs, and accommodating a variable perspective on online information quality. To demonstrate our research work, we have implemented a prototype online patient personal health information system, known as the Patient Health Base (PHB) that offers a patient a Summary Medical Record (SMR) and a Personal Internet Search (PerlS) service. PerlS addresses patient Internet search challenges identified in the project. Evaluation of PerlS's approach to improving a patient's medical Internet searching demonstrated improvements in terms of search capabilities, focusing techniques and results. This research explored a new direction for patient Internet searching and foresees a great potential for further customising Internet information searching for patients, families and the public as a whole.
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Levine, Jason M. (Jason Michael) 1981. "De-identification of ICU patient records." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/28460.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.
Includes bibliographical references (leaf 34).
The creation of systems for assembling and analyzing medical data is currently one of the major factors in advancing the speed of medical research. To ensure patient privacy, legal limitations have been placed on these systems. The Health Insurance Portability and Accountability Act requires that certain types potential identifiers be removed from the data before it can be shared freely. The process of removing the identifiers is called de-identification. The purpose of this project is to create a de-identification filter for the MIMIC database, a system that retrieves and organizes data from the intensive care unit at the Beth Israel Deaconess Medical Center.
by Jason M. Levine.
M.Eng.
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Rogers, Philip John. "Patient medication records by community pharmacy." Thesis, University of Bath, 1993. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.357290.

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Gregory, Judith. "Sorcerer's apprentice : creating the electronic health record, re-inventing medical records and patient care /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2000. http://wwwlib.umi.com/cr/ucsd/fullcit?p9992380.

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Shen, Shijun. "Approaches to creating anonymous patient database." Morgantown, W. Va. : [West Virginia University Libraries], 2000. http://etd.wvu.edu/templates/showETD.cfm?recnum=1693.

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Thesis (M.S.)--West Virginia University, 2000.
Title from document title page. Document formatted into pages; contains v, 68 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 67-68).
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Kindberg, Erik. "Word embeddings and Patient records : The identification of MRI risk patients." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157467.

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Identification of risks ahead of MRI examinations is identified as a cumbersome and time-consuming process at the Linköping University Hospital radiology clinic. The hospital staff often have to search through large amounts of unstructured patient data to find information about implants. Word embeddings has been identified as a possible tool to speed up this process. The purpose of this thesis is to evaluate this method, and that is done by training a Word2Vec model on patient journal data and analyzing the close neighbours of key search words by calculating cosine similarity. The 50 closest neighbours of each search words are categorized and annotated as relevant to the task of identifying risk patients ahead of MRI examinations or not. 10 search words were explored, leading to a total of 500 terms being annotated. In total, 14 different categories were observed in the result and out of these 8 were considered relevant. Out of the 500 terms, 340 (68%) were considered relevant. In addition, 48 implant models could be observed which are particularly interesting because if a patient have an implant, hospital staff needs to determine it’s exact model and the MRI conditions of that model. Overall these findings points towards a positive answer for the aim of the thesis, although further developments are needed.
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Kirkham, David Andrew. "Patient-held medical records : a thermodynamic perspective." Thesis, University of Cambridge, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296769.

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Sibanda, Tawanda Carleton. "Was the patient cured? : understanding semantic categories and their relationship in patient records." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/37097.

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Includes bibliographical references (leaves 103-107).
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.
In this thesis, we detail an approach to extracting key information in medical discharge summaries. Starting with a narrative patient report, we first identify and remove information that compromises privacy (de-identification); next we recognize words and phrases in the text belonging to semantic categories of interest to doctors (semantic category recognition). For disease and symptoms, we determine whether the problem is present, absent, uncertain, or associated with somebody else (assertion classification). Finally, we classify the semantic relationships existing between our categories (semantic relationship classification). Our approach utilizes a series of statistical models that rely heavily on local lexical and syntactic context, and achieve competitive results compared to more complex NLP solutions. We conclude the thesis by presenting the design for the Category and Relationship Extractor (CaRE). CaRE combines our solutions to de-identification, semantic category recognition, assertion classification, and semantic relationship classification into a single application that facilitates the easy extraction of semantic information from medical text.
by Tawanda Carleton Sibanda.
M.Eng.
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Books on the topic "Patient records"

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Driedger, Myrna. Patient focused charting. 4th ed. St. Boniface, Man: St. Boniface General Hospitas, 1992.

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L, Pagano Canera, ed. Authoring patient records: An interactive guide. Sudbury, Mass: Jones and Bartlett, 2011.

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Lampe, Susan. Focus charting: Documentation for patient-centred care. 6th ed. Minnesota: Creative NursingManagement, 1994.

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Lampe, Susan. Focus charting: Documentation for patient-centered care. 7th ed. Minneapolis, MN: Creative HealthCare Management, 1997.

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Lampe, Susan. Focus charting: Documentation for patient-centered care. 6th ed. Minneapolis, MN: Creative Nursing, 1994.

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Saunders, Janet M. McGee. Patient confidentiality. 3rd ed. Salt Lake City, UT: Medicode, Inc., 1996.

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Johnson, Diann. Medical records: Getting yours : a consumer's guide to obtaining and understanding medical records. Washington, D.C: Public Citizen's Health Research Group, 1995.

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Glondys, Barbara A. Documentation requirements for the acute care patient record. 4th ed. Chicago, Ill: American Health Information Management Association, 1996.

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Julia, Sheppard, Yeo Geoffrey, and Health Archives Group, eds. Hospital patient case records: A guide to their retention and disposal. London: Health Archives Group, 1996.

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Legal aspects of documenting patient care. 2nd ed. Gaithersburg, Md: Aspen Publishers, 2000.

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Book chapters on the topic "Patient records"

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Overton, Barbara Cook. "Patient Records." In Encyclopedia of Big Data, 727–31. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-32010-6_160.

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Overton, Barbara Cook. "Patient Records." In Encyclopedia of Big Data, 1–5. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-32001-4_160-1.

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Overton, Barbara Cook. "Patient Records." In Encyclopedia of Big Data, 1–5. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-32001-4_160-2.

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Degoulet, Patrice, and Marius Fieschi. "Managing Patient Records." In Introduction to Clinical Informatics, 117–30. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-0675-0_9.

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Kluge, Eike-Henner W. "Electronic Patient Records." In Encyclopedia of Global Bioethics, 1–10. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-05544-2_168-1.

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Goundrey-Smith, Stephen. "Electronic Patient Records." In Health Informatics, 27–58. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2780-2_2.

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Kluge, Eike-Henner W. "Electronic Patient Records." In Encyclopedia of Global Bioethics, 1080–88. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-09483-0_168.

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ten Have, Henk, and Maria do Céu Patrão Neves. "Electronic Patient Records." In Dictionary of Global Bioethics, 453. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-54161-3_222.

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Buckley, William H. "Patients’ Needs for Computer-based Patient Records." In Aspects of the Computer-based Patient Record, 36–39. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4757-3873-5_5.

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Amatayakul, Margret K., and Mary Joan Wogan. "Record Administrators’ Needs for Computer-based Patient Records." In Aspects of the Computer-based Patient Record, 57–64. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4757-3873-5_8.

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Conference papers on the topic "Patient records"

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Müller, Heimo, Stefan Sauer, Kurt Zatloukal, and Thomas Bauernhofer. "Interactive Patient Records." In 2010 14th International Conference Information Visualisation (IV). IEEE, 2010. http://dx.doi.org/10.1109/iv.2010.45.

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Dube, Sibusisiwe, Siqabukile Sihwa, Thambo Nyathi, and Khulekani Sibanda. "QR Code Based Patient Medical Health Records Transmission: Zimbabwean Case." In InSITE 2015: Informing Science + IT Education Conferences: USA. Informing Science Institute, 2015. http://dx.doi.org/10.28945/2233.

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In Zimbabwe the health care delivery system is hierarchical and patient transfer from the lower level to the next higher level health care facility involves patients carrying their physical medical record card. A medical record card holds information pertaining to the patient’s medical history, pre-existing allergies, medical health conditions, prescribed medication the patient is currently taking among other details. Recording such patient information on a medical health card renders it susceptible to tempering, loss, and misinterpretation as well as susceptible to breaches in confidentiality. In this paper, we propose the application of Quick Response (QR) codes to secure and transmit this sensitive patient information from one level of the health care delivery system to another. Other security methods such as steganography could be used, but in this paper we propose the use of QR codes owing to the high proliferation of mobile phones in the country, high storage capacity, flexibility, ease of use and their capability to maintain data integrity as well as storage of data in any format.
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Cvejic, Ivko, Jun Zhang, James Marx, and Judy Tjoe. "Automated search for patient records." In the 2nd ACM SIGHIT symposium. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2110363.2110442.

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Sibona, Christopher, Jon Brickey, Steven Walczak, and Madhavan Parthasarathy. "Patient Perceptions of Electronic Medical Records." In 2010 43rd Hawaii International Conference on System Sciences. IEEE, 2010. http://dx.doi.org/10.1109/hicss.2010.302.

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Anwar, Muhammad Naveed, and Michael Philip Oakes. "Data mining of audiology patient records." In the ACM fifth international workshop. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2064696.2064701.

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Tan, Christopher, Huawei Jian, and Ben Margetts. "64 Generating synthetic electronic patient records." In GOSH Conference 2019, Care of the Complex Child. BMJ Publishing Group Ltd and Royal College of Paediatrics and Child Health, 2019. http://dx.doi.org/10.1136/archdischild-2019-gosh.64.

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"WHO SHOULD ACCESS ELECTRONIC PATIENT RECORDS." In International Conference on Health Informatics. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0001039401820185.

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Jalali, Niloofar, Stephen Agboola, Kamal Jethwani, Ibrahim Zeid, and Sagar Kamarthi. "Temporal Case-Based Reasoning for Personalized Hypertensive Treatment." In ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-67066.

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Most of the current problems can be solved by referring to the solutions of the previous problems. Case Based reasoning (CBR) is one of the methods that solves a problem by retrieving the similar problems from the past and adapting the solutions of the past problems to solve the new problem. Recent studies that apply CBR include time as a parameter to retrieve most effective solutions that vary with time. This approach is more helpful in healthcare area in which one needs to look at historical evidence to find an accurate diagnostic or treatment regime. Hence, in this study, a time-based CBR is applied to track the outcomes of the drug therapy on hypertensive patients and find the most effective drug as a prescription. Initially, episodes in each patient’s medical records are chronologically ordered such that the oldest episode is placed first in the episode sequence and the latest episode is placed the last. It is assumed that the first episode of each patient is the first instance of diagnose; so when a new patient comes for checkup, his/her state (health condition) is compared with the initial state of the past patients. Therefore, the retrieval process calculates the similarity between the new patient’s current state and the most similar patients at their first episodes in the patient records. Due to the diversity of therapies for matching patients, the best treatment couldn’t be determined without knowing the efficacy of the different treatments. Therefore, the subsequent episodes of matching patients are examined to find the best treatment for the new patient. This might even require using a combination of treatments from all matching patients to find a good treatment for the new patient. After the treatment is defined for the first visit, the record of the new patient is stored in the library for future case retrieval. This method is a novel approach to personalized treatment of patients having chronic disease by tracking the medical records past patients over a long period of time. The current approach for treating the hypertensive patients uses evidence-based guidelines for managing the disease. However, this approach is more general and doesn’t take into account all the patient characteristics such as lab results and physical examination parameters. In the current approach the similarity between patients can’t be leveraged; the change of the treatment regime is based only on the risk parameter. However, in this method several parameters are being checked for efficiency of the medication. In contrast, the proposed CBR-based method personalizes the treatment based on what worked well for similar patients. In this paper, the clinical records of hypertensive patients are provided by a Boston based hospital. The preliminary results confirm that the proposed approach will give good recommendation for hypertension treatment.
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Brinck, Tom, and Gary York. "User interfaces for computer-based patient records." In CHI98: ACM Conference on Human Factors and Computing Systems. New York, NY, USA: ACM, 1998. http://dx.doi.org/10.1145/286498.286701.

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SIVAGURUNATHAN, KAMI, PANAGIOTIS CHOUNTAS, and ELIA EL-DARZI. "REPRESENTATION & MODELLING OF ELECTRONIC PATIENT RECORDS." In Proceedings of the International Conference (ICCMSE 2003). WORLD SCIENTIFIC, 2003. http://dx.doi.org/10.1142/9789812704658_0131.

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Reports on the topic "Patient records"

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Haynes, Kevin, Abiy Agiro, Xiaoxue Chen, Judith Stephenson, Biruk Eshete, Rebecca Sutphen, Elizabeth Bourquardez Clark, et al. Developing Methods to Link Patient Records across Data Sets that Preserve Patient Privacy. Patient-Centered Outcomes Research Institute (PCORI), June 2020. http://dx.doi.org/10.25302/06.2020.me.150328785.

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Gallegos, J., V. Hamilton, T. Gaylor, K. McCurley, and T. Meeks. Information integrity and privacy for computerized medical patient records. Office of Scientific and Technical Information (OSTI), September 1996. http://dx.doi.org/10.2172/392809.

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Stier, Jeffrey. Health Technology Integration for Clinical, Patient Records and Financial Management Related to the Military. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada566643.

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Lowry, Svetlana Z., Mala Ramaiah, A. Ant Ozok, Ayse P. Gurses, Michael C. Gibbons, David Brick, Emily S. Patterson, Vicki R. Lewis, and Debora Simmons. Toward a shared approach for ensuring patient safety with enhanced workflow design for electronic health records. National Institute of Standards and Technology, August 2013. http://dx.doi.org/10.6028/nist.ir.7952.

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Walker, Alex, Brian MacKenna, Peter Inglesby, Christopher Rentsch, Helen Curtis, Caroline Morton, Jessica Morley, et al. Clinical coding of long COVID in English primary care: a federated analysis of 58 million patient records in situ using OpenSAFELY. OpenSAFELY, 2021. http://dx.doi.org/10.53764/rpt.3917ab5ac5.

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This OpenSAFELY report is a routine update of our peer-review paper published in the British Journal of General Practice on the Clinical coding of long COVID in English primary care: a federated analysis of 58 million patient records in situ using OpenSAFELY. It is a routine update of the analysis described in the paper. The data requires careful interpretation and there are a number of caveats. Please read the full detail about our methods and discussionis and the full analytical methods on this routine report are available on GitHub. OpenSAFELY is a new secure analytics platform for electronic patient records built on behalf of NHS England to deliver urgent academic and operational research during the pandemic. You can read more about OpenSAFELY on our website.
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Baker, Laurence, Kate Bundorf, and Daniel Kessler. Expanding Patients' Property Rights In Their Medical Records. Cambridge, MA: National Bureau of Economic Research, October 2014. http://dx.doi.org/10.3386/w20565.

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7

Rudd, Ian. Leveraging Artificial Intelligence and Robotics to Improve Mental Health. Intellectual Archive, July 2022. http://dx.doi.org/10.32370/iaj.2710.

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Artificial Intelligence (AI) is one of the oldest fields of computer science used in building structures that look like human beings in terms of thinking, learning, solving problems, and decision making (Jovanovic et al., 2021). AI technologies and techniques have been in application in various aspects to aid in solving problems and performing tasks more reliably, efficiently, and effectively than what would happen without their use. These technologies have also been reshaping the health sector's field, particularly digital tools and medical robotics (Dantas & Nogaroli, 2021). The new reality has been feasible since there has been exponential growth in the patient health data collected globally. The different technological approaches are revolutionizing medical sciences into dataintensive sciences (Dantas & Nogaroli, 2021). Notably, with digitizing medical records supported the increasing cloud storage, the health sector created a vast and potentially immeasurable volume of biomedical data necessary for implementing robotics and AI. Despite the notable use of AI in healthcare sectors such as dermatology and radiology, its use in psychological healthcare has neem models. Considering the increased mortality and morbidity levels among patients with psychiatric illnesses and the debilitating shortage of psychological healthcare workers, there is a vital requirement for AI and robotics to help in identifying high-risk persons and providing measures that avert and treat mental disorders (Lee et al., 2021). This discussion is focused on understanding how AI and robotics could be employed in improving mental health in the human community. The continued success of this technology in other healthcare fields demonstrates that it could also be used in redefining mental sicknesses objectively, identifying them at a prodromal phase, personalizing the treatments, and empowering patients in their care programs.
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Flori, Heidi R. Children's Hospital Integrated Patient Electronic Record System (CHIPERS) Continuation. Fort Belvoir, VA: Defense Technical Information Center, October 2012. http://dx.doi.org/10.21236/ada615423.

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Hamlin, Alexandra, Erik Kobylarz, James Lever, Susan Taylor, and Laura Ray. Assessing the feasibility of detecting epileptic seizures using non-cerebral sensor. Engineer Research and Development Center (U.S.), December 2021. http://dx.doi.org/10.21079/11681/42562.

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This paper investigates the feasibility of using non-cerebral, time-series data to detect epileptic seizures. Data were recorded from fifteen patients (7 male, 5 female, 3 not noted, mean age 36.17 yrs), five of whom had a total of seven seizures. Patients were monitored in an inpatient setting using standard video electroencephalography (vEEG), while also wearing sensors monitoring electrocardiography, electrodermal activity, electromyography, accelerometry, and audio signals (vocalizations). A systematic and detailed study was conducted to identify the sensors and the features derived from the non-cerebral sensors that contribute most significantly to separability of data acquired during seizures from non-seizure data. Post-processing of the data using linear discriminant analysis (LDA) shows that seizure data are strongly separable from non-seizure data based on features derived from the signals recorded. The mean area under the receiver operator characteristic (ROC) curve for each individual patient that experienced a seizure during data collection, calculated using LDA, was 0.9682. The features that contribute most significantly to seizure detection differ for each patient. The results show that a multimodal approach to seizure detection using the specified sensor suite is promising in detecting seizures with both sensitivity and specificity. Moreover, the study provides a means to quantify the contribution of each sensor and feature to separability. Development of a non-electroencephalography (EEG) based seizure detection device would give doctors a more accurate seizure count outside of the clinical setting, improving treatment and the quality of life of epilepsy patients.
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Brown, Yolanda, Twonia Goyer, and Maragaret Harvey. Heart Failure 30-Day Readmission Frequency, Rates, and HF Classification. University of Tennessee Health Science Center, December 2020. http://dx.doi.org/10.21007/con.dnp.2020.0002.

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30 Day Hospital Readmission Rates, Frequencies, and Heart Failure Classification for Patients with Heart Failure Background Congestive heart failure (CHF) is the leading cause of mortality, morbidity, and disability worldwide among patients. Both the incidence and the prevalence of heart failure are age dependent and are relatively common in individuals 40 years of age and older. CHF is one of the leading causes of inpatient hospitalization readmission in the United States, with readmission rates remaining above the 20% goal within 30 days. The Center for Medicare and Medicaid Services imposes a 3% reimbursement penalty for excessive readmissions including those who are readmitted within 30 days from prior hospitalization for heart failure. Hospitals risk losing millions of dollars due to poor performance. A reduction in CHF readmission rates not only improves healthcare system expenditures, but also patients’ mortality, morbidity, and quality of life. Purpose The purpose of this DNP project is to determine the 30-day hospital readmission rates, frequencies, and heart failure classification for patients with heart failure. Specific aims include comparing computed annual re-admission rates with national average, determine the number of multiple 30-day re-admissions, provide descriptive data for demographic variables, and correlate age and heart failure classification with the number of multiple re-admissions. Methods A retrospective chart review was used to collect hospital admission and study data. The setting occurred in an urban hospital in Memphis, TN. The study was reviewed by the UTHSC Internal Review Board and deemed exempt. The electronic medical records were queried from July 1, 2019 through December 31, 2019 for heart failure ICD-10 codes beginning with the prefix 150 and a report was generated. Data was cleaned such that each patient admitted had only one heart failure ICD-10 code. The total number of heart failure admissions was computed and compared to national average. Using age ranges 40-80, the number of patients re-admitted withing 30 days was computed and descriptive and inferential statistics were computed using Microsoft Excel and R. Results A total of 3524 patients were admitted for heart failure within the six-month time frame. Of those, 297 were re-admitted within 30 days for heart failure exacerbation (8.39%). An annual estimate was computed (16.86%), well below the national average (21%). Of those re-admitted within 30 days, 50 were re-admitted on multiple occasions sequentially, ranging from 2-8 re-admissions. The median age was 60 and 60% male. Due to the skewed distribution (most re-admitted twice), nonparametric statistics were used for correlation. While graphic display of charts suggested a trend for most multiple re-admissions due to diastolic dysfunction and least number due to systolic heart failure, there was no statistically significant correlation between age and number or multiple re-admissions (Spearman rank, p = 0.6208) or number of multiple re-admissions and heart failure classification (Kruskal Wallis, p =0.2553).
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