Information quality management of ambulatory outcome in patients with coronary artery disease

Highlights. Using the big data databases of provided medical services belonging to the Territorial Compulsory Medical Insurance Fund and the Unified State Healthcare Information System (USHIS), clinical and statistical analysis of ambulatory follow-up of patients with coronary artery disease was carried out, an algorithm was developed and the effectiveness of the presented information management technology was determined.

Aim. To assess and justify the developed information management technology for the quality management of ambulatory follow-up of patients with coronary artery disease (CAD) based on monitoring and analyzing electronic personified data on medical services.

Methods. In 2018 the Territorial Compulsory Medical Insurance Fund (TCMIF), the Medical Information and Analytical Center (MIAC) and healthcare organizations of the Orenburg Region implemented information management technology to assess the quality of ambulatory follow-up in patients with CAD, which is based on big data analysis of medical services and information integration “emergency medical services (EMS), hospital-TCMIF + MIAC-polyclinic”. To assess the effectiveness of the technology, the proportion of patients with myocardial infarction (MI), death from coronary artery disease, emergency hospitalizations and emergency calls for angina pectoris were compared. The study involved   two groups of patients: 13 208 ambulatory patients with angina pectoris and 4017 patients without follow-up, according to  the  TCMIF  reports  in  2017.  Out of the 13,208 patients with angina pectoris in 2019, 10,205 patients with continued follow-up presented with the same clinical endpoints. Patients with these unfavorable outcomes in proportion to the total number of patients in each group in 2017 and 2019 were compared as well.

Results. The developed and implemented information management technology led to an increase in the number of ambulatory patients with angina pectoris from 63 to 69%, with MI from 78 to 87%, and a reduction of emergency calls and emergency hospitalizations of patients with angina pectoris in the region in 2017 and 2019.

Conclusion. Information quality management of ambulatory follow-up of patients with coronary artery disease using electronic personalized data on medical services increases the number of ambulatory patients with angina pectoris and myocardial infarction, and decreases the number of unfavorable outcomes of patients with angina pectoris.

1. Garganeeva A.A., Zyablov Yu.I., Okrugin S.A. Programma VOZ «Registr ostrogo infarkta miokarda»: 25-letnee epidemiologicheskoe izuchenie infarkta miokarda v sredne urbanizirovannom gorode Zapadnoi Sibiri. Sibirskii meditsinskii zhurnal. 2010; 25(2-1): 44-48. (In Russian)

2. Chazova I.E., Oschepkova E.V. The fight against cardiovascular diseases: problems and solutions at the present stage. Vestnik Roszdravnadzora. 2015; 5: 7–10. (In Russian)

3. Federal State Statistics Serviceю Svedeniya o smertnosti naseleniya po prichinam smerti po Rossiiskoi Federatsii za yanvar' - dekabr' 2017 g. Available at: https://www.gks.ru/free_doc/2017/demo/t3_3.xls. (accessed 01.07.2021) (In Russian)

4. Benjamin E.J., Virani S.S., Callaway C.W., Chamberlain A.M., Chang A.R., Cheng S., Chiuve S.E., Cushman M., Delling F.N., Deo R., de Ferranti S.D., Ferguson J.F., Fornage M., Gillespie C., Isasi C.R., Jiménez M.C., Jordan L.C., Judd S.E., Lackland D., Lichtman J.H., Lisabeth L., Liu S., Longenecker C.T., Lutsey P.L., Mackey J.S., Matchar D.B., Matsushita K., Mussolino M.E., Nasir K., O'Flaherty M., Palaniappan L.P., Pandey A., Pandey D.K., Reeves M.J., Ritchey M.D., Rodriguez C.J., Roth G.A., Rosamond W.D., Sampson U.K.A., Satou G.M., Shah S.H., Spartano N.L., Tirschwell D.L., Tsao C.W., Voeks J.H., Willey J.Z., Wilkins J.T., Wu J.H., Alger H.M., Wong S.S., Muntner P.; American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee. Heart Disease and Stroke Statistics-2018 Update: A Report From the American Heart Association. Circulation. 2018;137(12):e67-e492. doi: 10.1161/CIR.0000000000000558.

5. Kontsevaya A.V., Kalinina A.M., Koltunov I.E., Oganov R.G. Socio-Economic Damage by Acute Coronary Syndrome in Russian Federation. Rational Pharmacotherapy in Cardiology. 2011; 7(2): 158-166. (In Russian) doi: 10.20996/1819-6446-2011-7-2-158-166.

6. Avxentyeva M.V., Omelyanovsky V.V. Perspectives of Health Technology Assessment in the Russian Federation. Internal Medicine. Clinical Sleep Medicine. 2015; 104(3) - 105(4): 12-16. (In Russian)

7. Vandenheede H., Vikhireva O., Pikhart H., Kubinova R., Malyutina S., Pajak A., Tamosiunas A., Peasey A., Simonova G., Topor-Madry R., Marmot M., Bobak M. Socioeconomic inequalities in all-cause mortality in the Czech Republic, Russia, Poland and Lithuania in the 2000s: findings from the HAPIEE Study. J Epidemiol Community Health. 2014; 68: 297-303. doi: 10.1136/jech-2013-203057.

8. Boitsov S.A., Kalinina A.M., Ipatov P.V. Health assessment of the adult population as a mechanism to detect cardiovascular diseases and implementation of follow-up care. Vestnik Roszdravnadzora. 2015; 5: 11-18. (In Russian)

9. IT glossary: big data [webpage on the Internet]. Stamford, CT: Gartner. 2012. Available at: http://www.gartner.com/it-glossary/big-data/ (accessed June 17, 2014)

10. White S. A review of big data in health care: challenges and opportunities. Open Access Bioinformatics. 2014; 6: 13-18. doi: 10.2147/OAB.S50519.

11. Hintz J.E., O’Connor M.C. First steps in the era of value-based health care purchasing. Western Springs: National Law Review. 2012. Available at: http://www.natlawreview.com/article/first-steps-era-value-based-health-care-purchasing. (accessed 18.06.2014).

12. Nazarov A. M., Tolpygina S. N., Bolodurina I.P. Analysis and evaluation of healthcare delivery to patients with coronary artery disease using PC-based medical data. Cardiovascular Therapy and Prevention. 2020;19(6): 18 – 23. (In Russian) doi:10.15829/1728-8800-2020-19-6-18-23.

13. Dispansernoe nablyudenie bol'nykh khronicheskimi neinfektsionnymi zabolevaniyami i patsientov s vysokim riskom ikh razvitiya: metodicheskie rekomendatsii. Moscow; 2014. (In Russian)

14. Gubler E.V. Vychislitel'nye metody analiza i raspoznavaniya patologicheskikh protsessov. Leningrad: Medizina; 1978. (In Russian)

15. Borovikov V.P. STATISTICA. Iskusstvo analiza dannykh na komp'yutere: Dlya professionalov. Saint Petersburg:Piter; 2003. (In Russian)