Переход на ведение электронных медицинских карт (ЭМК) является одним из базовых направлений цифровой трансформации здравоохранения. Одной из актуальных современных проблем ведения ЭМК является качество данных, которые накапливаются в современных медицинских информационных системах. Учитывая растущую роль ЭМК в качестве источника информации для систем поддержки принятия врачебных решений, внедрение элементов управления на основе первичных данных, а также развитие исследований в сфере данных реальной клинической практики (RWD), возрастает потребность в надежных и объективных методах оценки качества данных, накапливаемых в ЭМК. В этой связи разработка надежных методов и инструментов оценки качества данных (ОКД) в ЭМК является актуальной научной задачей.
Цель. Изучить и систематизировать предложенные в научной литературе подходы, методы и критерии ОКД ЭМК.
Материалы и методы. Были изучены обзоры и оригинальные работы по тематике ОКД ЭМК. Источники были выявлены в результате систематического поиска в четырех электронных библиографических базах данных: PubMed, Web of Science, Scopus и РИНЦ.
Результаты. В работе представлены основные подходы и критерии оценки качества данных ЭМК, проведена гармонизация терминов и определений ОКД, выделены ключевые компоненты, необходимые для внедрения системы ОКД ЭМК.
Заключение. Сформулированные в обзоре типовые критерии ОКД ЭМК могут быть использованы для дальнейших исследований и разработок инструментов ОКД, в том числе со стороны разработчиков медицинских информационных систем и организаторов здравоохранения, ответственных за цифровую трансформацию отрасли. Также данная работа поможет устранить путаницу в вопросах управления качеством данных ЭМК и предоставит руководство, необходимое для разработки эффективных программ для проведения ОКД.
Цель. Изучить и систематизировать предложенные в научной литературе подходы, методы и критерии ОКД ЭМК.
Материалы и методы. Были изучены обзоры и оригинальные работы по тематике ОКД ЭМК. Источники были выявлены в результате систематического поиска в четырех электронных библиографических базах данных: PubMed, Web of Science, Scopus и РИНЦ.
Результаты. В работе представлены основные подходы и критерии оценки качества данных ЭМК, проведена гармонизация терминов и определений ОКД, выделены ключевые компоненты, необходимые для внедрения системы ОКД ЭМК.
Заключение. Сформулированные в обзоре типовые критерии ОКД ЭМК могут быть использованы для дальнейших исследований и разработок инструментов ОКД, в том числе со стороны разработчиков медицинских информационных систем и организаторов здравоохранения, ответственных за цифровую трансформацию отрасли. Также данная работа поможет устранить путаницу в вопросах управления качеством данных ЭМК и предоставит руководство, необходимое для разработки эффективных программ для проведения ОКД.
Литература
1. Warren LR, Clarke J, Arora S, et al. Improving data sharing between acute hospitals in England: an overview of health record system distribution and retrospective observational analysis of inter-hospital transitions of care. BMJ Open 2019; 9: e031637. doi: 10.1136/ bmjopen-2019-031637.
2. Atasoy H, Greenwood BN, McCullough JS. The Digitization of Patient Care: A Review of the Effects of Electronic Health Records on Health Care Quality and Utilization. Annu Rev Public Health. 2019; 40: 487-500. doi: 10.1146/annurev-publhealth-040218-044206.
3. Häyrinen K, Saranto K, Nykänen P. Definition, structure, content, use and impacts of electronic health records: a review of the research literature. Int J Med Inform. 2008; 77(5): 291-304. doi: 10.1016/ j.ijmedinf.2007.09.001.
4. Meystre SM, Lovis C, Bürkle T et al. Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress. Yearb Med Inform. 2017; 26(1): 38-52. doi: 10.15265/IY-2017-007.
5. Goldstein BA, Navar AM, Pencina MJ, Ioannidis JP. Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review. J Am Med Inform Assoc. 2017; 24(1): 198-208. doi: 10.1093/jamia/ocw042.
6. Topol E. The Topol Review Preparing the Healthcare Workforce to Deliver the Digital Future. 2019: 1-48.
7. Vuokko R, Mäkelä-Bengs P, Hyppönen H, Doupi P. Secondary use of structured patient data: interim results of a systematic review. Stud Health Technol Inform. 2015; 210: 291-5.
8. Collins SA, Bakken S, Vawdrey DK, et al. Clinician preferences for verbal communication compared to EHR documentation in the ICU. Appl Clin Inform. 2011; 2(2): 190-201. doi: 10.4338/ACI-2011-02-RA-0011.
9. Salomon RM, Blackford JU, Rosenbloom ST et al. Openness of patients' reporting with use of electronic records: psychiatric clinicians' views. J Am Med Inform Assoc. 2010; 17(1): 54-60. doi: 10.1197/jamia.M3341.
10. Peivandi S, Ahmadian L, Farokhzadian J, Jahani Y. Evaluation and comparison of errors on nursing notes created by online and offline speech recognition technology and handwritten: an interventional study. BMC Med Inform Decis Mak. 2022; 22(1): 96. doi: 10.1186/s12911-022-01835-4.
11. Colin NV, Cholan RA, Sachdeva B et al. Understanding the Impact of Variations in Measurement Period Reporting for Electronic Clinical Quality Measures. EGEMS (Wash DC). 2018; 6(1): 17. doi: 10.5334/egems.235.
12. Bowman S. Impact of electronic health record systems on information integrity: quality and safety implications. Perspect Health Inf Manag. 2013; 10(Fall): 1c.
13. O'Donnell HC, Kaushal R, Barrón Y, et al. Physicians' attitudes towards copy and pasting in electronic note writing. J Gen Intern Med. 2009; 24(1): 63-8. doi: 10.1007/s11606-008-0843-2.
14. Coleman N, Halas G, Peeler W, et al. From patient care to research: a validation study examining the factors contributing to data quality in a primary care electronic medical record database. BMC Fam Pract. 2015; 16: 11. doi: 10.1186/s12875-015-0223-z.
15. DAMA-DMBOK: Свод знаний по управлению данными. 2-е изд. 2020. Dama International [пер. с англ. Г. Агафонова]. М.: Олимп-Бизнес, 2020. 828 с.: ил.
16. Любицын В.Н. Повышение качества данных в контексте современных аналитических технологий // Вестник ЮУрГУ. Серия: Компьютерные технологии, управление, радиоэлектроника. – 2012. – №23.
17. Килимова А.Д. Потоки данных в легкой промышленности // Компетентность. – 2022. – №3.
18. Афанасьев А.А., Кудинов В.А. Использование онтологического подхода для извлечения ожиданий к качеству данных корпоративных хранилищ // Экономика. Информатика. – 2022. – №49(3). – С.566-574. doi: 10.52575/2687-0932-2022-49-3-566-574.
19. Elliott RA, Camacho E, Jankovic D, et al. Economic analysis of the prevalence and clinical and economic burden of medication error in England. BMJ Qual Saf. 2021; 30(2): 96-105. doi: 10.1136/bmjqs-2019-010206.
20. Zozus MN, Penning M, Hammond WE. Factors impacting physician use of information charted by others. JAMIA Open. 2019; 2(1): 107-114. doi: 10.1093/jamiaopen/ooy041.
21. Munyisia EN, Reid D, Yu P. Accuracy of outpatient service data for activity-based funding in New South Wales, Australia. Health Inf Manag. 2017; 46(2): 78-86. doi: 10.1177/1833358316678957.
22. Kaplan B. How Should Health Data Be Used? Camb Q Healthc Ethics. 2016; 25(2): 312-29. doi: 10.1017/S0963180115000614.
23. Nouraei SA, Virk JS, Hudovsky A, et al. Accuracy of clinician-clinical coder information handover following acute medical admissions: implication for using administrative datasets in clinical outcomes management. J Public Health (Oxf). 2016; 38(2): 352-62. doi: 10.1093/pubmed/fdv041.
24. Feldman K, Faust L, Wuet X, et al. Beyond volume: The impact of complex healthcare data on the machine learning pipeline Lecture Notes in Computer Science (including subseries Lecture Notes in Arti- ficial Intelligence and Lecture Notes in Bioinformatics). 2017; 10344 LNAI: 150-169.
25. Hanauer DA, Mei Q, Vydiswaran VGV, et al. Complexities, variations, and errors of numbering within clinical notes: the potential impact on information extraction and cohort-identification. BMC Med Inform Decis Mak. 2019; 19(3): 75. doi: 10.1186/s12911-019-0784-1.
26. Batini C, Francalanci C, Cappiello C, Maurino A. Methodologies for data quality assessment and improvement. ACM computing surveys (CSUR). 2009; 41(3): 16.
27. Wang RY. A product perspective on total data quality management. Communications of the ACM. 1998; 41(2): 58-66. doi: 10.1145/269012.269022.
28. Veiga AK, Saraiva AM, Chapman AD, et al. A conceptual framework for quality assessment and management of biodiversity data. PLoS One. 2017; 12(6): e0178731. doi: 10.1371/journal.pone.0178731.
29. WHO, Data Quality Assessment of National and Partner Hiv Treatment and Patient Monitoring Systems. 2018. August: 1-68.
30. Weiskopf NG, Weng C. Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. J Am Med Inform Assoc. 2013; 20(1): 144-51. doi: 10.1136/amiajnl-2011-000681.
31. Feder SL. Data Quality in Electronic Health Records Research: Quality Domains and Assessment Methods. West J Nurs Res. 2018; 40(5): 753-766. doi: 10.1177/0193945916689084.
32. Reimer AP, Milinovich A, Madigan EA. Data quality assessment framework to assess electronic medical record data for use in research. Int J Med Inform. 2016; 90: 40-7. doi: 10.1016/j.ijmedinf.2016.03.006.
33. Kahn MG, Raebel MA, Glanz JM, et al. A pragmatic framework for single-site and multisite data quality assessment in electronic health record-based clinical research. Med Care. 2012; 50(0): S21-9. doi: 10.1097/MLR.0b013e318257dd67.
34. Muthee V, Bochner AF, Osterman A, et al. The impact of routine data quality assessments on electronic medical record data quality in Kenya. PLoS One. 2018; 13(4): e0195362. doi: 10.1371/journal.pone.0195362.
35. Yadav S, Kazanji N, K C N, Paudel S, et al. Comparison of accuracy of physical examination findings in initial progress notes between paper charts and a newly implemented electronic health record. J Am Med Inform Assoc. 2017; 24(1): 140-144. doi: 10.1093/jamia/ocw067.
36. Abiy R, Gashu K, Asemaw T, et al. A Comparison of Electronic Medical Record Data to Paper Records in Antiretroviral Therapy Clinic in Ethiopia: What is affecting the Quality of the Data? Online J Public Health Inform. 2018; 10(2): e212. doi: 10.5210/ojphi.v10i2.8309.
37. Maletic JI, Marcus A, Data Cleansing: Beyond Integrity Analysis Iq, 2000: 1-10.
38. Daymont C, Ross ME, Russell Localio A, et al. Automated identification of implausible values in growth data from pediatric electronic health records. J Am Med Inform Assoc. 2017; 24(6): 1080-1087. doi: 10.1093/jamia/ocx037.
39. Brown JS, Kahn M, Toh S. Data quality assessment for comparative effectiveness research in distributed data networks. Med Care. 2013; 51(8S3): S22-9. doi: 10.1097/MLR.0b013e31829b1e2c.
40. Lewis AE, Weiskopf N, Abrams ZB, et al. Electronic health record data quality assessment and tools: a systematic review. J Am Med Inform Assoc. 2023; 30(10): 1730-1740. doi: 10.1093/jamia/ocad120.
41. Kahn MG, Callahan TJ, Barnard J, et al. A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data. EGEMS (Wash DC). 2016; 4(1): 1244. doi: 10.13063/2327-9214.1244.
42. Ozonze O, Scott PJ, Hopgood AA. Automating Electronic Health Record Data Quality Assessment. J Med Syst. 2023; 47(1): 23. doi: 10.1007/s10916-022-01892-2.
43. Pageler NM, Grazier G'Sell MJ, Chandler W, et al. A rational approach to legacy data validation when transitioning between electronic health record systems. J Am Med Inform Assoc. 2016; 23(5): 991-4. doi: 10.1093/jamia/ocv173.
44. Ferrão JC, Oliveira MD, Janela F, Martins HM. Preprocessing structured clinical data for predictive modeling and decision support. A roadmap to tackle the challenges. Appl Clin Inform. 2016; 7(4): 1135-1153. doi: 10.4338/ACI-2016-03-SOA-0035.
45. Safran C. Update on Data Reuse in Health Care. Yearb Med Inform. 2017; 26(1): 24-27. doi: 10.15265/IY-2017-013.
46. Jensen PB, Jensen LJ, Brunak S. Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet. 2012; 13(6): 395-405. doi: 10.1038/nrg3208.
47. Estiri H, Klann JG, Weiler SR, et al. A federated EHR network data completeness tracking system. J Am Med Inform Assoc. 2019; 26(7): 637-645. doi: 10.1093/jamia/ocz014.
48. Huser V, DeFalco FJ, Schuemie M, et al. Multisite Evaluation of a Data Quality Tool for Patient-Level Clinical Data Sets. EGEMS (Wash DC). 2016; 4(1): 1239. doi: 10.13063/2327-9214.1239.
49. Pipino LL, Lee YW, Wang RY. Data Quality Assessment Communications of the ACM. 2002; 45(4): 211. doi: 10.1145/505248.506010.
50. Naumann F, Rolker C. Assessment Methods for Information Quality Criteria Information Systems. 2000: 148-162. doi: 10.18452/9207.
51. Woodall P, Oberhofer M, Borek А. A classification of data quality assessment and improvement methods. International Journal of Information Quality, 2014. 3(4): 298-321. doi: 10.1504/IJIQ.2014.068656.
52. DAMA UK Working Group. The six primary dimensions for data quality assessment: defining data quality dimensions. 2013.
53. Weiskopf NG, Bakken S, Hripcsak G, Weng C. A Data Quality Assessment Guideline for Electronic Health Record Data Reuse. EGEMS (Wash DC). 2017; 5(1): 14. doi: 10.5334/egems.218.
54. Johnson SG, Speedie S, Simon G, et al. A Data Quality Ontology for the Secondary Use of EHR Data. AMIA Annu Symp Proc. 2015; 2015: 1937-46.
55. Hartzema AG, Reich CG, Ryan PB, et al. Managing data quality for a drug safety surveillance system. Drug Saf. 2013; 36(1): S49-58. doi: 10.1007/s40264-013-0098-7.
56. Kahn MG, Brown JS, Chun AT, et al. Transparent reporting of data quality in distributed data networks. EGEMS (Wash DC). 2015; 3(1): 1052. doi: 10.13063/2327-9214.1052.
57. Callahan T, Barnard J, Helmkamp L, et al. Reporting Data Quality Assessment Results: Identifying Individual and Organizational Barriers and Solutions. EGEMS (Wash DC). 2017; 5(1): 16. doi: 10.5334/egems.214.
58. Roomaney RA, Pillay-van Wyk V, Awotiwon OF, et al. Availability and quality of routine morbidity data: review of studies in South Africa. J Am Med Inform Assoc. 2017; 24(e1): e194-e206. doi: 10.1093/jamia/ocw075.
59. Estiri H, Stephens KA, Klann JG, Murphy SN. Exploring completeness in clinical data research networks with DQe-c. J Am Med Inform Assoc. 2018; 25(1): 17-24. doi: 10.1093/jamia/ocx109.
2. Atasoy H, Greenwood BN, McCullough JS. The Digitization of Patient Care: A Review of the Effects of Electronic Health Records on Health Care Quality and Utilization. Annu Rev Public Health. 2019; 40: 487-500. doi: 10.1146/annurev-publhealth-040218-044206.
3. Häyrinen K, Saranto K, Nykänen P. Definition, structure, content, use and impacts of electronic health records: a review of the research literature. Int J Med Inform. 2008; 77(5): 291-304. doi: 10.1016/ j.ijmedinf.2007.09.001.
4. Meystre SM, Lovis C, Bürkle T et al. Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress. Yearb Med Inform. 2017; 26(1): 38-52. doi: 10.15265/IY-2017-007.
5. Goldstein BA, Navar AM, Pencina MJ, Ioannidis JP. Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review. J Am Med Inform Assoc. 2017; 24(1): 198-208. doi: 10.1093/jamia/ocw042.
6. Topol E. The Topol Review Preparing the Healthcare Workforce to Deliver the Digital Future. 2019: 1-48.
7. Vuokko R, Mäkelä-Bengs P, Hyppönen H, Doupi P. Secondary use of structured patient data: interim results of a systematic review. Stud Health Technol Inform. 2015; 210: 291-5.
8. Collins SA, Bakken S, Vawdrey DK, et al. Clinician preferences for verbal communication compared to EHR documentation in the ICU. Appl Clin Inform. 2011; 2(2): 190-201. doi: 10.4338/ACI-2011-02-RA-0011.
9. Salomon RM, Blackford JU, Rosenbloom ST et al. Openness of patients' reporting with use of electronic records: psychiatric clinicians' views. J Am Med Inform Assoc. 2010; 17(1): 54-60. doi: 10.1197/jamia.M3341.
10. Peivandi S, Ahmadian L, Farokhzadian J, Jahani Y. Evaluation and comparison of errors on nursing notes created by online and offline speech recognition technology and handwritten: an interventional study. BMC Med Inform Decis Mak. 2022; 22(1): 96. doi: 10.1186/s12911-022-01835-4.
11. Colin NV, Cholan RA, Sachdeva B et al. Understanding the Impact of Variations in Measurement Period Reporting for Electronic Clinical Quality Measures. EGEMS (Wash DC). 2018; 6(1): 17. doi: 10.5334/egems.235.
12. Bowman S. Impact of electronic health record systems on information integrity: quality and safety implications. Perspect Health Inf Manag. 2013; 10(Fall): 1c.
13. O'Donnell HC, Kaushal R, Barrón Y, et al. Physicians' attitudes towards copy and pasting in electronic note writing. J Gen Intern Med. 2009; 24(1): 63-8. doi: 10.1007/s11606-008-0843-2.
14. Coleman N, Halas G, Peeler W, et al. From patient care to research: a validation study examining the factors contributing to data quality in a primary care electronic medical record database. BMC Fam Pract. 2015; 16: 11. doi: 10.1186/s12875-015-0223-z.
15. DAMA-DMBOK: Свод знаний по управлению данными. 2-е изд. 2020. Dama International [пер. с англ. Г. Агафонова]. М.: Олимп-Бизнес, 2020. 828 с.: ил.
16. Любицын В.Н. Повышение качества данных в контексте современных аналитических технологий // Вестник ЮУрГУ. Серия: Компьютерные технологии, управление, радиоэлектроника. – 2012. – №23.
17. Килимова А.Д. Потоки данных в легкой промышленности // Компетентность. – 2022. – №3.
18. Афанасьев А.А., Кудинов В.А. Использование онтологического подхода для извлечения ожиданий к качеству данных корпоративных хранилищ // Экономика. Информатика. – 2022. – №49(3). – С.566-574. doi: 10.52575/2687-0932-2022-49-3-566-574.
19. Elliott RA, Camacho E, Jankovic D, et al. Economic analysis of the prevalence and clinical and economic burden of medication error in England. BMJ Qual Saf. 2021; 30(2): 96-105. doi: 10.1136/bmjqs-2019-010206.
20. Zozus MN, Penning M, Hammond WE. Factors impacting physician use of information charted by others. JAMIA Open. 2019; 2(1): 107-114. doi: 10.1093/jamiaopen/ooy041.
21. Munyisia EN, Reid D, Yu P. Accuracy of outpatient service data for activity-based funding in New South Wales, Australia. Health Inf Manag. 2017; 46(2): 78-86. doi: 10.1177/1833358316678957.
22. Kaplan B. How Should Health Data Be Used? Camb Q Healthc Ethics. 2016; 25(2): 312-29. doi: 10.1017/S0963180115000614.
23. Nouraei SA, Virk JS, Hudovsky A, et al. Accuracy of clinician-clinical coder information handover following acute medical admissions: implication for using administrative datasets in clinical outcomes management. J Public Health (Oxf). 2016; 38(2): 352-62. doi: 10.1093/pubmed/fdv041.
24. Feldman K, Faust L, Wuet X, et al. Beyond volume: The impact of complex healthcare data on the machine learning pipeline Lecture Notes in Computer Science (including subseries Lecture Notes in Arti- ficial Intelligence and Lecture Notes in Bioinformatics). 2017; 10344 LNAI: 150-169.
25. Hanauer DA, Mei Q, Vydiswaran VGV, et al. Complexities, variations, and errors of numbering within clinical notes: the potential impact on information extraction and cohort-identification. BMC Med Inform Decis Mak. 2019; 19(3): 75. doi: 10.1186/s12911-019-0784-1.
26. Batini C, Francalanci C, Cappiello C, Maurino A. Methodologies for data quality assessment and improvement. ACM computing surveys (CSUR). 2009; 41(3): 16.
27. Wang RY. A product perspective on total data quality management. Communications of the ACM. 1998; 41(2): 58-66. doi: 10.1145/269012.269022.
28. Veiga AK, Saraiva AM, Chapman AD, et al. A conceptual framework for quality assessment and management of biodiversity data. PLoS One. 2017; 12(6): e0178731. doi: 10.1371/journal.pone.0178731.
29. WHO, Data Quality Assessment of National and Partner Hiv Treatment and Patient Monitoring Systems. 2018. August: 1-68.
30. Weiskopf NG, Weng C. Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. J Am Med Inform Assoc. 2013; 20(1): 144-51. doi: 10.1136/amiajnl-2011-000681.
31. Feder SL. Data Quality in Electronic Health Records Research: Quality Domains and Assessment Methods. West J Nurs Res. 2018; 40(5): 753-766. doi: 10.1177/0193945916689084.
32. Reimer AP, Milinovich A, Madigan EA. Data quality assessment framework to assess electronic medical record data for use in research. Int J Med Inform. 2016; 90: 40-7. doi: 10.1016/j.ijmedinf.2016.03.006.
33. Kahn MG, Raebel MA, Glanz JM, et al. A pragmatic framework for single-site and multisite data quality assessment in electronic health record-based clinical research. Med Care. 2012; 50(0): S21-9. doi: 10.1097/MLR.0b013e318257dd67.
34. Muthee V, Bochner AF, Osterman A, et al. The impact of routine data quality assessments on electronic medical record data quality in Kenya. PLoS One. 2018; 13(4): e0195362. doi: 10.1371/journal.pone.0195362.
35. Yadav S, Kazanji N, K C N, Paudel S, et al. Comparison of accuracy of physical examination findings in initial progress notes between paper charts and a newly implemented electronic health record. J Am Med Inform Assoc. 2017; 24(1): 140-144. doi: 10.1093/jamia/ocw067.
36. Abiy R, Gashu K, Asemaw T, et al. A Comparison of Electronic Medical Record Data to Paper Records in Antiretroviral Therapy Clinic in Ethiopia: What is affecting the Quality of the Data? Online J Public Health Inform. 2018; 10(2): e212. doi: 10.5210/ojphi.v10i2.8309.
37. Maletic JI, Marcus A, Data Cleansing: Beyond Integrity Analysis Iq, 2000: 1-10.
38. Daymont C, Ross ME, Russell Localio A, et al. Automated identification of implausible values in growth data from pediatric electronic health records. J Am Med Inform Assoc. 2017; 24(6): 1080-1087. doi: 10.1093/jamia/ocx037.
39. Brown JS, Kahn M, Toh S. Data quality assessment for comparative effectiveness research in distributed data networks. Med Care. 2013; 51(8S3): S22-9. doi: 10.1097/MLR.0b013e31829b1e2c.
40. Lewis AE, Weiskopf N, Abrams ZB, et al. Electronic health record data quality assessment and tools: a systematic review. J Am Med Inform Assoc. 2023; 30(10): 1730-1740. doi: 10.1093/jamia/ocad120.
41. Kahn MG, Callahan TJ, Barnard J, et al. A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data. EGEMS (Wash DC). 2016; 4(1): 1244. doi: 10.13063/2327-9214.1244.
42. Ozonze O, Scott PJ, Hopgood AA. Automating Electronic Health Record Data Quality Assessment. J Med Syst. 2023; 47(1): 23. doi: 10.1007/s10916-022-01892-2.
43. Pageler NM, Grazier G'Sell MJ, Chandler W, et al. A rational approach to legacy data validation when transitioning between electronic health record systems. J Am Med Inform Assoc. 2016; 23(5): 991-4. doi: 10.1093/jamia/ocv173.
44. Ferrão JC, Oliveira MD, Janela F, Martins HM. Preprocessing structured clinical data for predictive modeling and decision support. A roadmap to tackle the challenges. Appl Clin Inform. 2016; 7(4): 1135-1153. doi: 10.4338/ACI-2016-03-SOA-0035.
45. Safran C. Update on Data Reuse in Health Care. Yearb Med Inform. 2017; 26(1): 24-27. doi: 10.15265/IY-2017-013.
46. Jensen PB, Jensen LJ, Brunak S. Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet. 2012; 13(6): 395-405. doi: 10.1038/nrg3208.
47. Estiri H, Klann JG, Weiler SR, et al. A federated EHR network data completeness tracking system. J Am Med Inform Assoc. 2019; 26(7): 637-645. doi: 10.1093/jamia/ocz014.
48. Huser V, DeFalco FJ, Schuemie M, et al. Multisite Evaluation of a Data Quality Tool for Patient-Level Clinical Data Sets. EGEMS (Wash DC). 2016; 4(1): 1239. doi: 10.13063/2327-9214.1239.
49. Pipino LL, Lee YW, Wang RY. Data Quality Assessment Communications of the ACM. 2002; 45(4): 211. doi: 10.1145/505248.506010.
50. Naumann F, Rolker C. Assessment Methods for Information Quality Criteria Information Systems. 2000: 148-162. doi: 10.18452/9207.
51. Woodall P, Oberhofer M, Borek А. A classification of data quality assessment and improvement methods. International Journal of Information Quality, 2014. 3(4): 298-321. doi: 10.1504/IJIQ.2014.068656.
52. DAMA UK Working Group. The six primary dimensions for data quality assessment: defining data quality dimensions. 2013.
53. Weiskopf NG, Bakken S, Hripcsak G, Weng C. A Data Quality Assessment Guideline for Electronic Health Record Data Reuse. EGEMS (Wash DC). 2017; 5(1): 14. doi: 10.5334/egems.218.
54. Johnson SG, Speedie S, Simon G, et al. A Data Quality Ontology for the Secondary Use of EHR Data. AMIA Annu Symp Proc. 2015; 2015: 1937-46.
55. Hartzema AG, Reich CG, Ryan PB, et al. Managing data quality for a drug safety surveillance system. Drug Saf. 2013; 36(1): S49-58. doi: 10.1007/s40264-013-0098-7.
56. Kahn MG, Brown JS, Chun AT, et al. Transparent reporting of data quality in distributed data networks. EGEMS (Wash DC). 2015; 3(1): 1052. doi: 10.13063/2327-9214.1052.
57. Callahan T, Barnard J, Helmkamp L, et al. Reporting Data Quality Assessment Results: Identifying Individual and Organizational Barriers and Solutions. EGEMS (Wash DC). 2017; 5(1): 16. doi: 10.5334/egems.214.
58. Roomaney RA, Pillay-van Wyk V, Awotiwon OF, et al. Availability and quality of routine morbidity data: review of studies in South Africa. J Am Med Inform Assoc. 2017; 24(e1): e194-e206. doi: 10.1093/jamia/ocw075.
59. Estiri H, Stephens KA, Klann JG, Murphy SN. Exploring completeness in clinical data research networks with DQe-c. J Am Med Inform Assoc. 2018; 25(1): 17-24. doi: 10.1093/jamia/ocx109.
Для цитирования
Кафтанов А.Н., Андрейченко А.Е., Гусев А.В. Обзор методических подходов к оценке качества ведения электронных медицинских карт. Врач и информационные технологии. 2024; 3: 6-19.
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