Journal articles: 'Patient data'

1

McCoy, Matthew S., Steven Joffe, and Ezekiel J. Emanuel. "Sharing Patient Data Without Exploiting Patients." JAMA 323, no. 6 (2020): 505. http://dx.doi.org/10.1001/jama.2019.22354.

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Jolly, Damian. "Recording patient data." Emergency Nurse 12, no. 8 (2004): 11. http://dx.doi.org/10.7748/en.12.8.11.s17.

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Goymer, Luke. "Recording patient data." Emergency Nurse 12, no. 9 (2005): 9. http://dx.doi.org/10.7748/en.12.9.9.s11.

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Straseski, Joely A., and Frederick G. Strathmann. "Patient Data Algorithms." Clinics in Laboratory Medicine 33, no. 1 (2013): 147–60. http://dx.doi.org/10.1016/j.cll.2012.11.009.

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Nieri, Michele, Carlo Clauser, Umberto Pagliaro, and Giovanpaolo PiniPrato. "Individual patient data." Journal of Evidence Based Dental Practice 3, no. 3 (2003): 122–26. http://dx.doi.org/10.1016/s1532-3382(03)00070-8.

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Contreras, Jorge L., John Rumbold, and Barbara Pierscionek. "Patient Data Ownership." JAMA 319, no. 9 (2018): 935. http://dx.doi.org/10.1001/jama.2017.21672.

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7

Sadan, Batami. "Patient data confidentiality and patient rights." International Journal of Medical Informatics 62, no. 1 (2001): 41–49. http://dx.doi.org/10.1016/s1386-5056(00)00135-0.

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Pearce, Christopher, Adam McCleod, and Jason Ferrigi. "From linked data to patient centred data: using health data to improve patient outcomes." International Journal of Integrated Care 20, no. 3 (2021): 39. http://dx.doi.org/10.5334/ijic.s4039.

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Menaka, Dr S. R., M. Gokul Raj, P. Elakiya Selvan, G. Tharani Kumar, and M. Yashika. "A Sensor based Data Analytics for Patient Monitoring Using Data Mining." International Academic Journal of Innovative Research 9, no. 1 (2022): 28–36. http://dx.doi.org/10.9756/iajir/v9i1/iajir0905.

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Remote sensor networks have been broadly utilized in medical care applications, like emergency clinic and home patient observing. A great deal of work has been done to get remote clinical sensor organizations. The current arrangements can safeguard the patient information during transmission, yet cant stop within assault where the manager of the patient data set uncovers the touchy patient information. In this paper, we propose a useful way to deal with forestall within assault by utilizing different information servers to store patient information. The fundamental commitment of this paper is safely disseminating the patient information in various information servers and utilizing the AES to perform measurement investigation on the patient information without compromising the patients security. Remote clinical sensor networks surely work on understandings nature of-care without upsetting their solace. Nonetheless, there exist numerous potential security dangers to the patient delicate physiological information communicated over the public diverts and put away in the back-end frameworks. Average security dangers to medical care applications with WSNs can be summed up as follows. Snoopping is a security danger to the patient information protection A busybody, having a strong beneficiary recieving wire, might have the option to catch the patient information from the clinical sensors and accordingly realizes the patients medical issue. He might even post the patients medical issue on interpersonal organization, which can represent a genuine danger to patient security. Pantomime is a security danger to the patient information credibility.

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AL-Mafrji, Ahmad Abdullah Mohammed, and Ahmed Burhan Mohammed. "Analysis of Patients Data Using Fuzzy Expert System." Webology 19, no. 1 (2022): 4027–34. http://dx.doi.org/10.14704/web/v19i1/web19265.

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Many problems are facing many developed and developing countries in the medical field, and the most important of these problems is the analysis and diagnosis of patient data for government and private hospitals. This is due to the lack of experience of medical staff, especially new ones, which affects the provision of correct medical services to patients. It is no secret that these countries are making great efforts to overcome these problems. The study focuses on the use of a fuzzy expert system to analyze patient data based on (age, type of review) to reach the result of the analysis (intensive care, medium care, no care) and this system helps to give advice and good analysis of patient data, which can increase the speed of gaining experience for new and inexperienced medical staff in this field.

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11

Ross, J., E. Holzbaur, M. Wade, and T. Rothrock. "Data Cleaning Paper Patient Reported Outcome (PRO) Data Versus Electronic Patient Reported Outcome (EPRO) Data." Value in Health 18, no. 3 (2015): A36—A37. http://dx.doi.org/10.1016/j.jval.2015.03.219.

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12

Doyal, L. "Data from patient records." British Dental Journal 178, no. 12 (1995): 448. http://dx.doi.org/10.1038/sj.bdj.4808798.

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Vulto, Arnold G. "Resolution in patient data." European Journal of Hospital Pharmacy 19, no. 3 (2012): 275–76. http://dx.doi.org/10.1136/ejhpharm-2012-000144.

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14

Friesdorf, W., and B. Schwilk. "Patient-related data management." Journal of Clinical Monitoring 8, no. 4 (1992): 308–14. http://dx.doi.org/10.1007/bf01617913.

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15

Mikk, Katherine A., Harry A. Sleeper, and Eric Topol. "Patient Data Ownership—Reply." JAMA 319, no. 9 (2018): 935. http://dx.doi.org/10.1001/jama.2017.21688.

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Chiruvella, Varsha, and Achuta Kumar Guddati. "Ethical Issues in Patient Data Ownership." Interactive Journal of Medical Research 10, no. 2 (2021): e22269. http://dx.doi.org/10.2196/22269.

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Patient data have conventionally been thought to be well protected by the privacy laws outlined in the United States. The increasing interest of for-profit companies in acquiring the databases of large health care systems poses new challenges to the protection of patients’ privacy. It also raises ethical concerns of sharing patient data with entities that may exploit it for commercial interests and even target vulnerable populations. Recognizing that every breach in the confidentiality of large databases exposes millions of patients to the potential of being exploited is important in framing new rules for governing the sharing of patient data. Similarly, the ethical aspects of data voluntarily and altruistically provided by patients for research, which may be exploited for commercial interests due to patient data sharing between health care entities and third-party companies, need to be addressed. The rise of technologies such as artificial intelligence and the availability of personal data gleaned by data vendor companies place American patients at risk of being exploited both intentionally and inadvertently because of the sharing of their data by their health care provider institutions and third-party entities.

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17

Langstrup, Henriette. "Patient-reported data and the politics of meaningful data work." Health Informatics Journal 25, no. 3 (2018): 567–76. http://dx.doi.org/10.1177/1460458218820188.

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Patient-reported outcome data have moved from the realm of research to center stage in efforts to provide patient-centered care. In a Danish context, health authorities are seeking to promote and standardize the use of patient-reported outcome data. This involves normative articulations of what counts as meaningful data work in a healthcare system characterized by intensified data-sourcing. Based on ethnographic material, I suggest that an assemblage of actors, both human and technological, has accomplished the articulation of meaningful data work, with patient-reported outcome as being dependent on the active application of data in clinical trajectories—in contrast to supplying data “passively” for secondary use for research or governance. This normative articulation of “Active patient-reported outcome” legitimizes the Danish patient-reported outcome assemblage by showing alignment of the concerns of patients, clinicians and health authorities. At the same time, “Active patient-reported outcome” foreshadows challenges in making data work meaningful in local practice.

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18

Lee, Jisan, Hun-Sung Kim, and Jeongeun Kim. "Out-of-Hospital Data: Patient Generated Health Data." Journal of Korean Diabetes 21, no. 3 (2020): 149–55. http://dx.doi.org/10.4093/jkd.2020.21.3.149.

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19

Munuera Gomez, Pilar, and Carmen Aleman Bracho. "BIG DATA OPPORTUNITY AND PRIVACY OF PATIENT DATA." International Journal of Advanced Research 8, no. 7 (2020): 1808–16. http://dx.doi.org/10.21474/ijar01/11448.

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20

Berglas, Sarah, Tamara Rader, and Helen Mai. "PD24 Data Collection By Patient Groups To Provide Patient Input." International Journal of Technology Assessment in Health Care 34, S1 (2018): 137–38. http://dx.doi.org/10.1017/s0266462318002994.

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Introduction:The Canadian Agency for Drugs and Technologies in Health (CADTH) Common Drug Review and pan-Canadian Oncology Drug Review programs incorporate perspectives and experiences from patients and family members who might be affected by the resulting funding recommendation. Perspectives are provided by patient groups who use different approaches to gather patient input.Methods:We analyzed a random sample of ninety-three patient input submissions, drawn from a sampling frame of 532 submissions given to CADTH between June 2010 and June 2016. We looked at how groups described their information gathering methods in the original submissions or the published Clinical Guidance Reports.Results:Approaches were categorized according to whether they involved primary (n = 86) or secondary data collection (n = 130) and further sub categorized according to how data was collected. Primary data included: personal experiences, as described by the submission's author (n = 16); surveys conducted specifically for the submission (n=34); and new interviews of patients and family members on disease and drug experiences (n = 36). Half (forty-seven of ninety-three) of the patient input submissions included experiences of one or more patients who had received the drug under review. Secondary data included: published literature (n = 31); existing surveys (n = 27); past conversations with patients and family members (n = 36); experiences of patient group staff interacting with patients and family members (n = 19); and advice from clinical experts (n = 17). Many patient input submissions (sixty-eight out of ninety-three) reported multiple approaches to collect data. Use of two approaches was most common (thirty-seven out of ninety-three) with five or six approaches used in three of ninety-three submissions.Conclusions:Despite resource and timing challenges, many patient groups gather primary data to share with CADTH and find individuals with experience of the drug under review.

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21

Király, Z., I. Boncz, D. Endrei, and Z. Kívés. "HPR23 Analysis of Patient Turnover Data and Patient and Medical Delay Data of Lung Cancer Patients between 2016-2020." Value in Health 25, no. 7 (2022): S471. http://dx.doi.org/10.1016/j.jval.2022.04.949.

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22

Islind, Anna Sigridur, Tomas Lindroth, Johan Lundin, and Gunnar Steineck. "Shift in translations: Data work with patient-generated health data in clinical practice." Health Informatics Journal 25, no. 3 (2019): 577–86. http://dx.doi.org/10.1177/1460458219833097.

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This article reports on how the introduction of patient-generated health data affects the nurses’ and patients’ data work and unpacks how new forms of data collection trigger shifts in the work with data through translation work. The article is based on a 2.5-year case study examining data work of nurses and patients at a cancer rehabilitation clinic at a Swedish Hospital in which patient-generated health data are gathered by patients and then used outside and within clinical practice for decision-making. The article reports on how data are prepared and translated, that is, made useful by the nurses and patients. Using patient-generated health data alters the data work and how the translation of data is performed. The shift in work has three components: (1) a shift in question tactics, (2) a shift in decision-making, and (3) a shift in distribution. The data become mobile, and the data work becomes distributed when using patient-generated health data as an active part of care.

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23

Pilipczuk, Olga, Dmitri Eidenzon, and Olena Kosenko. "Patient Postoperative Care Data Visualization." International Journal of Computer Applications 156, no. 7 (2016): 27–33. http://dx.doi.org/10.5120/ijca2016912469.

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24

Amos, DavidCharles. "Cloud computing – Securing patient data." Digital Medicine 5, no. 3 (2019): 96. http://dx.doi.org/10.4103/digm.digm_20_19.

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25

H, Bharath, Rahul N, Shylash S, and Vinny Pious. "Patient Data Management Using Blockchain." International Journal of Scientific and Research Publications (IJSRP) 10, no. 7 (2020): 310–16. http://dx.doi.org/10.29322/ijsrp.10.07.2020.p10339.

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26

Hayhurst, Chris. "Is Your Patient Data Secure?" Biomedical Instrumentation & Technology 48, no. 3 (2014): 166–73. http://dx.doi.org/10.2345/0899-8205-48.3.166.

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27

Taylor, P. "Caldicott 2 and patient data." BMJ 346, apr24 11 (2013): f2260. http://dx.doi.org/10.1136/bmj.f2260.

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28

&NA;. "Clinical Patient Data Management System." Dimensions of Critical Care Nursing 5, no. 3 (1986): 191. http://dx.doi.org/10.1097/00003465-198605000-00015.

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29

Butler, Declan. "'Data network threatens patient privacy'." Nature 386, no. 6620 (1997): 6. http://dx.doi.org/10.1038/386006b0.

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30

Rayner, Moira. "Confidentiality of Patient Identifiable Data." Australian Medical Record Journal 20, no. 1 (1990): 12–17. http://dx.doi.org/10.1177/183335839002000104.

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31

Kelly, G. "Patient data, confidentiality, and electronics." BMJ 316, no. 7133 (1998): 718–19. http://dx.doi.org/10.1136/bmj.316.7133.718.

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32

Ullah, Dayem, Hemant Kocher, and Claude Chelala. "Data integration for patient benefit." Pancreatology 20, no. 8 (2020): e2. http://dx.doi.org/10.1016/j.pan.2018.10.014.

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33

AULT, ALICIA. "Patient Satisfaction Data Gaining Clout." Hospitalist News 3, no. 6 (2010): 1–16. http://dx.doi.org/10.1016/s1875-9122(10)70132-2.

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34

Webster, Paul. "Patient data in the cloud." Lancet Digital Health 1, no. 8 (2019): e391-e392. http://dx.doi.org/10.1016/s2589-7500(19)30202-x.

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35

Boyce, Robert W., and Richard N. Herrier. "Obtaining and Using Patient Data." American Pharmacy 31, no. 7 (1991): 65–71. http://dx.doi.org/10.1016/s0160-3450(16)33787-4.

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36

Whatling, J. "Big Data and Patient Care." ITNOW 55, no. 3 (2013): 16–17. http://dx.doi.org/10.1093/itnow/bwt039.

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37

Selby, Joe V. "US patient network safeguards data." Nature 509, no. 7502 (2014): 563. http://dx.doi.org/10.1038/509563b.

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38

Byrne, Patrick J. "Patient data in child psychiatry." British Journal of Psychiatry 158, no. 4 (1991): 572. http://dx.doi.org/10.1192/bjp.158.4.572.

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39

Cantor, Michael N., and Lorna Thorpe. "Patient Data: The Authors Reply." Health Affairs 37, no. 8 (2018): 1341. http://dx.doi.org/10.1377/hlthaff.2018.0673.

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40

Edwards, Marilyn. "Patient confidentiality and data protection." Practice Nursing 20, no. 8 (2009): 411–13. http://dx.doi.org/10.12968/pnur.2009.20.8.43665.

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41

Clarke, Michael J. "Individual patient data meta-analyses." Best Practice & Research Clinical Obstetrics & Gynaecology 19, no. 1 (2005): 47–55. http://dx.doi.org/10.1016/j.bpobgyn.2004.10.011.

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42

Lobo-Stratton, Gloria, Tyran Mercer, and Raimund Polman. "Patient Exam Data Reconciliation Tool." Journal of Digital Imaging 19, S1 (2006): 60–65. http://dx.doi.org/10.1007/s10278-006-0926-8.

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43

Jelonek, Dorota, and Andrzej Chluski. "The use of Big Data resources in patient experience management." Scientific Papers of Silesian University of Technology. Organization and Management Series 2018, no. 120 (2018): 117–29. http://dx.doi.org/10.29119/1641-3466.2018.120.9.

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44

Jonsson, Pia Maria, and John Øvretveit. "Patient claims and complaints data for improving patient safety." International Journal of Health Care Quality Assurance 21, no. 1 (2008): 60–74. http://dx.doi.org/10.1108/09526860810841165.

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45

Carine, Fiona, and Nicolle Parrent. "Improving Patient Identification Data on the Patient Master Index." Health Information Management 29, no. 1 (1999): 14–17. http://dx.doi.org/10.1177/183335839902900107.

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46

Vollmer, Jochen, Stefan Mönk, Wolfgang Heinrichs, and Thomas Uthmann. "Real Patient Intensive Care Data On A Patient Simulator." Simulation in Healthcare: The Journal of the Society for Simulation in Healthcare 1, no. 2 (2006): 101. http://dx.doi.org/10.1097/01266021-200600120-00025.

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47

Torenholt, Rikke, Lena Saltbæk, and Henriette Langstrup. "Patient data work: filtering and sensing patient‐reported outcomes." Sociology of Health & Illness 42, no. 6 (2020): 1379–93. http://dx.doi.org/10.1111/1467-9566.13114.

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48

Dorda, W. G. "Data-Screening and Retrieval of Medical Data by the System WAREL." Methods of Information in Medicine 29, no. 01 (1990): 3–11. http://dx.doi.org/10.1055/s-0038-1634760.

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AbstractThe paper presented here deals with the computer system WAREL, a system for analyzing medical patient data. It is a patientoriented medical data-screening system which automatically points out medical risk factors. Referring to all patients, it is a retrieval system for selecting groups of patients from the clinical data base; the explorative statistical analysis concerning medical data of these selected groups can give an essential impulse to clinical research. The most important components of the system such as the relational data base and the system for defining and activating logical conditions are discussed. These conditions (IF -THEN rules) are the basis of both the patient oriented data-screening and the retrieval of patient groups. They may refer to data in formatted or in natural language form; furthermore conditions of the course of illness can also be formulated. Thus, the data-screeni ng system may also indicate risk situations due to the course of an illness.

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49

Bobroske, Katherine, Christine Larish, Anita Cattrell, Margrét V. Bjarnadóttir, and Lawrence Huan. "The bird’s-eye view: A data-driven approach to understanding patient journeys from claims data." Journal of the American Medical Informatics Association 27, no. 7 (2020): 1037–45. http://dx.doi.org/10.1093/jamia/ocaa052.

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Abstract Objective In preference-sensitive conditions such as back pain, there can be high levels of variability in the trajectory of patient care. We sought to develop a methodology that extracts a realistic and comprehensive understanding of the patient journey using medical and pharmaceutical insurance claims data. Materials and Methods We processed a sample of 10 000 patient episodes (comprised of 113 215 back pain–related claims) into strings of characters, where each letter corresponds to a distinct encounter with the healthcare system. We customized the Levenshtein edit distance algorithm to evaluate the level of similarity between each pair of episodes based on both their content (types of events) and ordering (sequence of events). We then used clustering to extract the main variations of the patient journey. Results The algorithm resulted in 12 comprehensive and clinically distinct patterns (clusters) of patient journeys that represent the main ways patients are diagnosed and treated for back pain. We further characterized demographic and utilization metrics for each cluster and observed clear differentiation between the clusters in terms of both clinical content and patient characteristics. Discussion Despite being a complex and often noisy data source, administrative claims provide a unique longitudinal overview of patient care across multiple service providers and locations. This methodology leverages claims to capture a data-driven understanding of how patients traverse the healthcare system. Conclusions When tailored to various conditions and patient settings, this methodology can provide accurate overviews of patient journeys and facilitate a shift toward high-quality practice patterns.

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Kukielka, Elizabeth. "Challenges with Measurement and Transcription of Patient Height: An Analysis of Patient Safety Events in Pennsylvania Related to Inaccurate Patient Height." Patient Safety, March 17, 2021, 48–57. http://dx.doi.org/10.33940/data/2021.3.5.

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An accurate patient height is necessary to calculate certain measurements (e.g., body surface area [BSA]) and lab values (e.g., creatinine clearance [CrCl]), which may be needed to assess renal, cardiac, and lung function and to calculate accurate medication doses. We queried the Pennsylvania Patient Safety Reporting System (PA-PSRS) and identified 679 event reports related to an inaccurate patient height. All events were classified by the reporting facility as incidents, meaning that the patient did not sustain an unanticipated injury or require the delivery of additional healthcare services. The most common care area group where an event occurred was outpatient/clinic (35.8%; 243 of 679). Events were categorized as being related to an error in transcription (72.5%; 492 of 679) or measurement (7.4%; 50 of 679), and the remainder were categorized as etiology of error unclear (20.2%; 137 of 679). The most common transcription errors were the use of the wrong unit of measurement, the transposition of another measurement with height, and typographical errors. Inaccurate patient heights most often led to errors in calculation of medication doses or laboratory values. The most common medication class involved in a dosing error was cancer chemotherapy. In order to ensure accuracy of patient height measurements, patients should be measured at the beginning of every healthcare encounter, units of measurement should be consistent from measurement to transcription into the electronic medical record, and estimated patient height should never be relied upon or recorded.

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Journal articles on the topic 'Patient data'

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