Evaluating quality of care for patients with type 2 diabetes using electronic health record information in Mexico

被引:32
|
作者
Perez-Cuevas, Ricardo [1 ]
Doubova, Svetlana V. [2 ]
Suarez-Ortega, Magdalena [2 ]
Law, Michael [3 ]
Pande, Aakanksha H. [4 ,5 ,6 ]
Escobedo, Jorge [7 ]
Espinosa-Larranaga, Francisco [8 ]
Ross-Degnan, Dennis [4 ,5 ,6 ]
Wagner, Anita K. [4 ,5 ,6 ]
机构
[1] Interamer Dev Bank, Div Social Protect & Hlth, Washington, DC USA
[2] Mexican Inst Social Secur, Epidemiol & Hlth Serv Res Unit CMN Siglo XXI, Mexico City, DF, Mexico
[3] Univ British Columbia, Sch Populat & Publ Hlth, Ctr Hlth Serv & Policy Res, Vancouver, BC V5Z 1M9, Canada
[4] Harvard Univ, Sch Med, Dept Populat Med, Boston, MA USA
[5] Harvard Univ, Sch Med, WHO Collaborating Ctr Pharmaceut Policy, Boston, MA USA
[6] Harvard Pilgrim Hlth Care Inst, Boston, MA USA
[7] IMSS, Hosp Reg Carlos MacGregor Sanchez Navarro 1, Unidad Invest Epidemiol Clin, Mexico City, DF, Mexico
[8] IMSS, Div Innovac Coordinac Educ Salud, Mexico City, DF, Mexico
关键词
CARDIOVASCULAR RISK-FACTORS; MIDDLE-INCOME; COUNTRIES; ADULTS;
D O I
10.1186/1472-6947-12-50
中图分类号
R-058 [];
学科分类号
摘要
Background: Several low and middle-income countries are implementing electronic health records (EHR). In the near future, EHRs could become an efficient tool to evaluate healthcare performance if appropriate indicators are developed. The aims of this study are: a) to develop quality of care indicators (QCIs) for type 2 diabetes (T2DM) in the Mexican Institute of Social Security (IMSS) health system; b) to determine the feasibility of constructing QCIs using the IMSS EHR data; and c) to evaluate the quality of care (QC) provided to IMSS patients with T2DM. Methods: We used a three-stage mixed methods approach: a) development of QCIs following the RAND-UCLA method; b) EHR data extraction and construction of indicators; c) QC evaluation using EHR data from 25, 130 T2DM patients who received care in 2009. Results: We developed 18 QCIs, of which 14 were possible to construct using available EHR data. QCIs comprised both process of care and health outcomes. Several flaws in the EHR design and quality of data were identified. The indicators of process and outcomes of care suggested areas for improvement. For example, only 13.0% of patients were referred to an ophthalmologist; 3.9% received nutritional counseling; 63.2% of overweight/obese patients were prescribed metformin, and only 23% had HbA1c < 7% (or plasma glucose <= 130 mg/dl). Conclusions: EHR data can be used to evaluate QC. The results identified both strengths and weaknesses in the electronic information system as well as in the process and outcomes of T2DM care at IMSS. This information can be used to guide targeted interventions to improve QC.
引用
收藏
页数:10
相关论文
共 50 条
  • [21] Diabetes Phenotyping Using the Electronic Health Record
    Weerahandi, Himali M.
    Horwitz, Leora I.
    Blecker, Saul B.
    JOURNAL OF GENERAL INTERNAL MEDICINE, 2020, 35 (12) : 3716 - 3718
  • [22] Automated Detection and Classification of Type 1 Versus Type 2 Diabetes Using Electronic Health Record Data
    Klompas, Michael
    Eggleston, Emma
    McVetta, Jason
    Lazarus, Ross
    Li, Lingling
    Platt, Richard
    DIABETES CARE, 2013, 36 (04) : 914 - 921
  • [23] Clinical effectiveness of exenatide in patients with type 2 diabetes in a primary care electronic medical record database
    Brixner, Diana
    Oderda, Gary
    Ye, Xiangyang
    Boye, Kristina Secnik
    Wintle, Matthew
    Fabunmi, Rosalind
    DIABETES, 2008, 57 : A135 - A136
  • [24] Evaluating the quality of Spanish-language information for patients with type 2 diabetes on YouTube and Facebook
    Soto-Chavez, Maria Juliana
    Diaz-Brochero, Candida
    Gomez-Medina, Ana Maria
    Henao, Diana Cristina
    Munoz, Oscar Mauricio
    HEALTH INFORMATICS JOURNAL, 2025, 31 (01)
  • [25] USING ELECTRONIC HEALTH RECORD CLINICAL DECISION SUPPORT IMPROVES QUALITY OF CARE
    Bates, David W.
    Bitton, Asaf
    JOURNAL OF GENERAL INTERNAL MEDICINE, 2013, 28 : S234 - S234
  • [26] Predictive and Preventive Models for Diabetes Prevention using Clinical Information in Electronic Health Record
    Cao, Ni
    Zeng, Sisi
    Shen, Feixia
    Pan, Chuandi
    Chen, Chengshui
    Thanh Nguyen
    Chen, Jake
    PROCEEDINGS 2015 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE, 2015, : 867 - 874
  • [27] Electronic Health Record Nudges and Health Care Quality and Outcomes in Primary Care
    Nguyen, Oliver T.
    Kunta, Avaneesh R.
    Katoju, Srivarsha
    Gheytasvand, Sara
    Masoumi, Niloofar
    Tavasolian, Ronia
    Tabriz, Amir Alishahi
    Hong, Young-Rock
    Hanna, Karim
    Perkins, Randa
    Parekh, Arpan
    Turner, Kea
    JAMA NETWORK OPEN, 2024, 7 (09)
  • [28] Targeting Deprescribing in Type 2 Diabetes Mellitus Using a Pragmatic Electronic Health Record Measure of Frailty
    Usoh, Chinenye
    Lenoir, Kristin M.
    Pajewski, Nicholas M.
    Callahan, Kathryn E.
    DIABETES, 2021, 70
  • [29] How Accurate is the Electronic Health Record? - A Pilot Study Evaluating Information Accuracy in a Primary Care Setting
    Tse, J.
    You, W.
    HEALTH INFORMATICS: THE TRANSFORMATIVE POWER OF INNOVATION, 2011, 168 : 158 - 164
  • [30] MONITORING THE PREVALENCE OF DIABETES AND THE QUALITY OF CARE USING ELECTRONIC HEALTH DATA
    Gnavi, R.
    Picariello, R.
    Bruno, G.
    Giorda, C.
    Costa, G.
    EPIDEMIOLOGIA & PREVENZIONE, 2010, 34 (5-6): : 85 - 85