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9 "Quality of health care"
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Original Article
Changes in the Hospital Standardized Mortality Ratio Before and During the COVID-19 Pandemic: A Disaggregated Analysis by Region and Hospital Type in Korea
EunKyo Kang, Won Mo Jang, Min Sun Shin, Hyejin Lee, Jin Yong Lee
J Prev Med Public Health. 2023;56(2):180-189.   Published online March 20, 2023
DOI: https://doi.org/10.3961/jpmph.22.479
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AbstractAbstract AbstractSummary PDF
Objectives
The coronavirus disease 2019 (COVID-19) pandemic has led to a global shortage of medical resources; therefore, we investigated whether COVID-19 impacted the quality of non-COVID-19 hospital care in Korea by comparing hospital standardized mortality rates (HSMRs) before and during the pandemic.
Methods
This retrospective cohort study analyzed Korean National Health Insurance discharge claim data obtained from January to June in 2017, 2018, 2019, and 2020. Patients’ in-hospital deaths were classified according to the most responsible diagnosis categories. The HSMR is calculated as the ratio of expected deaths to actual deaths. The time trend in the overall HSMR was analyzed by region and hospital type.
Results
The final analysis included 2 252 824 patients. In 2020, the HSMR increased nationwide (HSMR, 99.3; 95% confidence interval [CI], 97.7 to 101.0) in comparison to 2019 (HSMR, 97.3; 95% CI, 95.8 to 98.8). In the COVID-19 pandemic zone, the HSMR increased significantly in 2020 (HSMR, 112.7; 95% CI, 107.0 to 118.7) compared to 2019 (HSMR, 101.7; 95% CI, 96.9 to 106.6). The HSMR in all general hospitals increased significantly in 2020 (HSMR, 106.4; 95% CI, 104.3 to 108.5) compared to 2019 (HSMR, 100.3; 95% CI, 98.4 to 102.2). Hospitals participating in the COVID-19 response had a lower HSMR (HSMR, 95.6; 95% CI, 93.9 to 97.4) than hospitals not participating in the COVID-19 response (HSMR, 124.3; 95% CI, 119.3 to 129.4).
Conclusions
This study suggests that the COVID-19 pandemic may have negatively impacted the quality of care in hospitals, especially general hospitals with relatively few beds. In light of the COVID-19 pandemic, it is necessary to prevent excessive workloads in hospitals and to properly employ and coordinate the workforce.
Summary
Korean summary
코로나19 대유행 지역은 비감염 지역과 달리 2019년에 비해 2020년에 HSMR이 크게 증가했고, 상대적으로 병상 수가 적은 종합병원에서 HSMR이 증가했다. 코로나19 대응에 참여하는 병원은 병원 규모와 관계없이 HSMR이 낮은 경향을 보였다. 감염병 유행 시 병원의 과도한 업무량이 부여되지 않게하고 인력을 적절하게 고용하여 조정하는 것이 필요하다.
Special Article
Strategies for Appropriate Patient-centered Care to Decrease the Nationwide Cost of Cancers in Korea
Jong-Myon Bae
J Prev Med Public Health. 2017;50(4):217-227.   Published online June 16, 2017
DOI: https://doi.org/10.3961/jpmph.17.069
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  • 158 Download
  • 2 Crossref
AbstractAbstract PDFSupplementary Material
In terms of years of life lost to premature mortality, cancer imposes the highest burden in Korea. In order to reduce the burden of cancer, the Korean government has implemented cancer control programs aiming to reduce cancer incidence, to increase survival rates, and to decrease cancer mortality. However, these programs may paradoxically increase the cost burden. For examples, a cancer screening program for early detection could bring about over-diagnosis and over-treatment, and supplying medical services in a paternalistic manner could lead to defensive medicine or futile care. As a practical measure to reduce the cost burden of cancer, appropriate cancer care should be established. Ensuring appropriateness requires patient-doctor communication to ensure that utility values are shared and that autonomous decisions are made regarding medical services. Thus, strategies for reducing the cost burden of cancer through ensuring appropriate patient-centered care include introducing value-based medicine, conducting cost-utility studies, and developing patient decision aids.
Summary

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  • A Systematic Review of Economic Evaluation of Thyroid Cancer
    Mijin Kim, Woojin Lim, Kyungsik Kim, Ja Seong Bae, Byung Joo Lee, Bon Seok Koo, Eun Kyung Lee, Eu Jeong Ku, June Young Choi, Bo Hyun Kim, Sue K. Park
    International Journal of Thyroidology.2022; 15(2): 74.     CrossRef
  • Ethical, pedagogical, socio-political and anthropological implications of quaternary prevention
    Marc Jamoulle, Michel Roland, Jong-Myon Bae, Bruno Heleno, Giorgio Visentin, Gustavo Diniz Ferreira Gusso, Maciek Godycki-Ćwirko, Miguel Pizzanell, Patrick Ouvrard, Ricardo La Valle, Luis Filipe Gomes, Daniel Widmer, Jorge Bernstein, Mariana Mariño, Hamil
    Revista Brasileira de Medicina de Família e Comunidade.2018; 13(40): 1.     CrossRef
Original Articles
Variations in the Hospital Standardized Mortality Ratios in Korea
Eun-Jung Lee, Soo-Hee Hwang, Jung-A Lee, Yoon Kim
J Prev Med Public Health. 2014;47(4):206-215.   Published online July 31, 2014
DOI: https://doi.org/10.3961/jpmph.2014.47.4.206
  • 10,369 View
  • 109 Download
  • 2 Crossref
AbstractAbstract PDF
Objectives
The hospital standardized mortality ratio (HSMR) has been widely used because it allows for robust risk adjustment using administrative data and is important for improving the quality of patient care.
Methods
All inpatients discharged from hospitals with more than 700 beds (66 hospitals) in 2008 were eligible for inclusion. Using the claims data, 29 most responsible diagnosis (MRDx), accounting for 80% of all inpatient deaths among these hospitals, were identified, and inpatients with those MRDx were selected. The final study population included 703 571 inpatients including 27 718 (3.9% of all inpatients) in-hospital deaths. Using logistic regression, risk-adjusted models for predicting in-hospital mortality were created for each MRDx. The HSMR of individual hospitals was calculated for each MRDx using the model coefficients. The models included age, gender, income level, urgency of admission, diagnosis codes, disease-specific risk factors, and comorbidities. The Elixhauser comorbidity index was used to adjust for comorbidities.
Results
For 26 out of 29 MRDx, the c-statistics of these mortality prediction models were higher than 0.8 indicating excellent discriminative power. The HSMR greatly varied across hospitals and disease groups. The academic status of the hospital was the only factor significantly associated with the HSMR.
Conclusions
We found a large variation in HSMR among hospitals; therefore, efforts to reduce these variations including continuous monitoring and regular disclosure of the HSMR are required.
Summary

Citations

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  • Differences in trends in discharge location in a cohort of hospitalized patients with cancer and non-cancer diagnoses receiving specialist palliative care: A retrospective cohort study
    Michael Bonares, Kalli Stillos, Lise Huynh, Debbie Selby
    Palliative Medicine.2023; 37(8): 1241.     CrossRef
  • Functional training and timed nutrition intervention in infectious medical patients
    M Holst, L N Søndergaard, M D Bendtsen, J Andreasen
    European Journal of Clinical Nutrition.2016; 70(9): 1039.     CrossRef
Improving the Performance of Risk-adjusted Mortality Modeling for Colorectal Cancer Surgery by Combining Claims Data and Clinical Data
Won Mo Jang, Jae-Hyun Park, Jong-Hyock Park, Jae Hwan Oh, Yoon Kim
J Prev Med Public Health. 2013;46(2):74-81.   Published online March 28, 2013
DOI: https://doi.org/10.3961/jpmph.2013.46.2.74
  • 9,496 View
  • 75 Download
  • 6 Crossref
AbstractAbstract PDF
Objectives

The objective of this study was to evaluate the performance of risk-adjusted mortality models for colorectal cancer surgery.

Methods

We investigated patients (n=652) who had undergone colorectal cancer surgery (colectomy, colectomy of the rectum and sigmoid colon, total colectomy, total proctectomy) at five teaching hospitals during 2008. Mortality was defined as 30-day or in-hospital surgical mortality. Risk-adjusted mortality models were constructed using claims data (basic model) with the addition of TNM staging (TNM model), physiological data (physiological model), surgical data (surgical model), or all clinical data (composite model). Multiple logistic regression analysis was performed to develop the risk-adjustment models. To compare the performance of the models, both c-statistics using Hanley-McNeil pair-wise testing and the ratio of the observed to the expected mortality within quartiles of mortality risk were evaluated to assess the abilities of discrimination and calibration.

Results

The physiological model (c=0.92), surgical model (c=0.92), and composite model (c=0.93) displayed a similar improvement in discrimination, whereas the TNM model (c=0.87) displayed little improvement over the basic model (c=0.86). The discriminatory power of the models did not differ by the Hanley-McNeil test (p>0.05). Within each quartile of mortality, the composite and surgical models displayed an expected mortality ratio close to 1.

Conclusions

The addition of clinical data to claims data efficiently enhances the performance of the risk-adjusted postoperative mortality models in colorectal cancer surgery. We recommended that the performance of models should be evaluated through both discrimination and calibration.

Summary

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  • Estimating postoperative mortality in colorectal surgery- a systematic review of risk prediction models
    Alexios Dosis, Jack Helliwell, Aron Syversen, Jim Tiernan, Zhiqiang Zhang, David Jayne
    International Journal of Colorectal Disease.2023;[Epub]     CrossRef
  • Modified Tumor Budding as a Better Predictor of Lymph Node Metastasis in Early Gastric Cancer: Possible Real-World Applications
    Kwangil Yim, Won Mo Jang, Sung Hak Lee
    Cancers.2021; 13(14): 3405.     CrossRef
  • Investigación epidemiológica en cáncer colorrectal: perspectiva, prospectiva y retos bajo la óptica de explotación del Big-Data
    J.M. García Torrecillas, M. Ferrer Márquez, Á. Reina Duarte, F. Rubio-Gil
    SEMERGEN - Medicina de Familia.2016; 42(8): 509.     CrossRef
  • Variation between Hospitals with Regard to Diagnostic Practice, Coding Accuracy, and Case-Mix. A Retrospective Validation Study of Administrative Data versus Medical Records for Estimating 30-Day Mortality after Hip Fracture
    Jon Helgeland, Doris Tove Kristoffersen, Katrine Damgaard Skyrud, Anja Schou Lindman, Alanna M Chamberlain
    PLOS ONE.2016; 11(5): e0156075.     CrossRef
  • Model for risk adjustment of postoperative mortality in patients with colorectal cancer
    K Walker, P J Finan, J H van der Meulen
    British Journal of Surgery.2015; 102(3): 269.     CrossRef
  • Problems With Public Reporting of Cancer Quality Outcomes Data
    Paul Goldberg, Rena M. Conti
    Journal of Oncology Practice.2014; 10(3): 215.     CrossRef
Selecting the Best Prediction Model for Readmission
Eun Whan Lee
J Prev Med Public Health. 2012;45(4):259-266.   Published online July 31, 2012
DOI: https://doi.org/10.3961/jpmph.2012.45.4.259
  • 12,256 View
  • 104 Download
  • 35 Crossref
AbstractAbstract PDF
Objectives

This study aims to determine the risk factors predicting rehospitalization by comparing three models and selecting the most successful model.

Methods

In order to predict the risk of rehospitalization within 28 days after discharge, 11 951 inpatients were recruited into this study between January and December 2009. Predictive models were constructed with three methods, logistic regression analysis, a decision tree, and a neural network, and the models were compared and evaluated in light of their misclassification rate, root asymptotic standard error, lift chart, and receiver operating characteristic curve.

Results

The decision tree was selected as the final model. The risk of rehospitalization was higher when the length of stay (LOS) was less than 2 days, route of admission was through the out-patient department (OPD), medical department was in internal medicine, 10th revision of the International Classification of Diseases code was neoplasm, LOS was relatively shorter, and the frequency of OPD visit was greater.

Conclusions

When a patient is to be discharged within 2 days, the appropriateness of discharge should be considered, with special concern of undiscovered complications and co-morbidities. In particular, if the patient is admitted through the OPD, any suspected disease should be appropriately examined and prompt outcomes of tests should be secured. Moreover, for patients of internal medicine practitioners, co-morbidity and complications caused by chronic illness should be given greater attention.

Summary

Citations

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  • Interpretable machine learning models for hospital readmission prediction: a two-step extracted regression tree approach
    Xiaoquan Gao, Sabriya Alam, Pengyi Shi, Franklin Dexter, Nan Kong
    BMC Medical Informatics and Decision Making.2023;[Epub]     CrossRef
  • Burden and patient characteristics associated with repeat consultation for unscheduled care within 30 days in primary care: a retrospective case control study with implications for aging and public health
    Valentin Richard, Leila Bouazzi, Clément Richard, Stéphane Sanchez
    Frontiers in Public Health.2023;[Epub]     CrossRef
  • Machine learning for predicting readmission risk among the frail: Explainable AI for healthcare
    Somya D. Mohanty, Deborah Lekan, Thomas P. McCoy, Marjorie Jenkins, Prashanti Manda
    Patterns.2022; 3(1): 100395.     CrossRef
  • AI Models for Predicting Readmission of Pneumonia Patients within 30 Days after Discharge
    Jiin-Chyr Hsu, Fu-Hsing Wu, Hsuan-Hung Lin, Dah-Jye Lee, Yung-Fu Chen, Chih-Sheng Lin
    Electronics.2022; 11(5): 673.     CrossRef
  • An AI-driven clinical care pathway to reduce 30-day readmission for chronic obstructive pulmonary disease (COPD) patients
    Lin Wang, Guihua Li, Chika F. Ezeana, Richard Ogunti, Mamta Puppala, Tiancheng He, Xiaohui Yu, Solomon S. Y. Wong, Zheng Yin, Aaron W. Roberts, Aryan Nezamabadi, Pingyi Xu, Adaani Frost, Robert E. Jackson, Stephen T. C. Wong
    Scientific Reports.2022;[Epub]     CrossRef
  • Medicare cost reduction in the US: A case study of hospital readmissions and value-based purchasing
    Mehmet C. Kocakulah, David Austill, Eric Henderson
    International Journal of Healthcare Management.2021; 14(1): 203.     CrossRef
  • Published models that predict hospital readmission: a critical appraisal
    Lisa Grossman Liu, James R Rogers, Rollin Reeder, Colin G Walsh, Devan Kansagara, David K Vawdrey, Hojjat Salmasian
    BMJ Open.2021; 11(8): e044964.     CrossRef
  • Predictive modelling of hospital readmission: Evaluation of different preprocessing techniques on machine learning classifiers
    Nor Hamizah Miswan, Chee Seng Chan, Chong Guan Ng
    Intelligent Data Analysis.2021; 25(5): 1073.     CrossRef
  • Designing a clinical decision support system to predict readmissions for patients admitted with all-cause conditions
    Huey-Jen Lai, Tan-Hsu Tan, Chih-Sheng Lin, Yung-Fu Chen, Hsuan-Hung Lin
    Journal of Ambient Intelligence and Humanized Computing.2020;[Epub]     CrossRef
  • Implementation of Artificial Intelligence-Based Clinical Decision Support to Reduce Hospital Readmissions at a Regional Hospital
    Santiago Romero-Brufau, Kirk D. Wyatt, Patricia Boyum, Mindy Mickelson, Matthew Moore, Cheristi Cognetta-Rieke
    Applied Clinical Informatics.2020; 11(04): 570.     CrossRef
  • Independent prospective validation of a medication‐based 15‐day readmission risk stratification algorithm in a tertiary acute care hospital
    Denise Yeo, T. W. Chew, Y. F. Lai
    JACCP: JOURNAL OF THE AMERICAN COLLEGE OF CLINICAL PHARMACY.2019; 2(1): 40.     CrossRef
  • Risk Assessment of Acute, All-Cause 30-Day Readmission in Patients Aged 65+: a Nationwide, Register-Based Cohort Study
    Mona K. Pedersen, Gunnar L. Nielsen, Lisbeth Uhrenfeldt, Søren Lundbye-Christensen
    Journal of General Internal Medicine.2019; 34(2): 226.     CrossRef
  • A machine learning model for predicting ICU readmissions and key risk factors: analysis from a longitudinal health records
    Alvaro Ribeiro Botelho Junqueira, Farhaan Mirza, Mirza Mansoor Baig
    Health and Technology.2019; 9(3): 297.     CrossRef
  • Development of a predictive score for potentially avoidable hospital readmissions for general internal medicine patients
    Anne-Laure Blanc, Thierry Fumeaux, Jérôme Stirnemann, Elise Dupuis Lozeron, Aimad Ourhamoune, Jules Desmeules, Pierre Chopard, Arnaud Perrier, Nicolas Schaad, Pascal Bonnabry, Enrico Mossello
    PLOS ONE.2019; 14(7): e0219348.     CrossRef
  • An integrated machine learning framework for hospital readmission prediction
    Shancheng Jiang, Kwai-Sang Chin, Gang Qu, Kwok L. Tsui
    Knowledge-Based Systems.2018; 146: 73.     CrossRef
  • Development and prospective validation of a model estimating risk of readmission in cancer patients
    Carl R. Schmidt, Jennifer Hefner, Ann S. McAlearney, Lisa Graham, Kristen Johnson, Susan Moffatt‐Bruce, Timothy Huerta, Timothy M. Pawlik, Susan White
    Journal of Surgical Oncology.2018; 117(6): 1113.     CrossRef
  • Characterization, Categorization, and 5-Year Mortality of Medicine High Utilizer Inpatients
    Joyeeta G. Dastidar, Min Jiang
    Journal of Palliative Care.2018; 33(3): 167.     CrossRef
  • Predicting Hospital Readmission via Cost-Sensitive Deep Learning
    Haishuai Wang, Zhicheng Cui, Yixin Chen, Michael Avidan, Arbi Ben Abdallah, Alexander Kronzer
    IEEE/ACM Transactions on Computational Biology and Bioinformatics.2018; 15(6): 1968.     CrossRef
  • Using decision trees to explore the association between the length of stay and potentially avoidable readmissions: A retrospective cohort study
    Mohammad S. Alyahya, Heba H. Hijazi, Hussam A. Alshraideh, Amjad D. Al-Nasser
    Informatics for Health and Social Care.2017; 42(4): 361.     CrossRef
  • Identifying Potentially Avoidable Readmissions: A Medication‐Based 15‐Day Readmission Risk Stratification Algorithm
    Sreemanee Raaj Dorajoo, Vincent See, Chen Teng Chan, Joyce Zhenyin Tan, Doreen Su Yin Tan, Siti Maryam Binte Abdul Razak, Ting Ting Ong, Narendran Koomanan, Chun Wei Yap, Alexandre Chan
    Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy.2017; 37(3): 268.     CrossRef
  • The association between number of doctors per bed and readmission of elderly patients with pneumonia in South Korea
    Joo Eun Lee, Tae Hyun Kim, Kyoung Hee Cho, Kyu-Tae Han, Eun-Cheol Park
    BMC Health Services Research.2017;[Epub]     CrossRef
  • Preventing hospital readmissions: the importance of considering ‘impactibility,’ not just predicted risk
    Adam Steventon, John Billings
    BMJ Quality & Safety.2017; 26(10): 782.     CrossRef
  • Assessing risk of hospital readmissions for improving medical practice
    Parimal Kulkarni, L. Douglas Smith, Keith F. Woeltje
    Health Care Management Science.2016; 19(3): 291.     CrossRef
  • Comparison of Clinical Risk Factors Among Pediatric Patients With Single Admission, Multiple Admissions (Without Any 7-Day Readmissions), and 7-Day Readmission
    Jeffrey C. Winer, Elena Aragona, Alan I. Fields, David C. Stockwell
    Hospital Pediatrics.2016; 6(3): 119.     CrossRef
  • An ontology-based system to predict hospital readmission within 30 days
    Huda Al Ghamdi, Riyad Alshammari, Muhammad Imran Razzak
    International Journal of Healthcare Management.2016; 9(4): 236.     CrossRef
  • Impact of selected pre-processing techniques on prediction of risk of early readmission for diabetic patients in India
    Reena Duggal, Suren Shukla, Sarika Chandra, Balvinder Shukla, Sunil Kumar Khatri
    International Journal of Diabetes in Developing Countries.2016; 36(4): 469.     CrossRef
  • Predicting Patients at Risk for 3-Day Postdischarge Readmissions, ED Visits, and Deaths
    Deepak Agrawal, Cheng-Bang Chen, Ronald W. Dravenstott, Christopher T. B. Strömblad, John Andrew Schmid, Jonathan D. Darer, Priyantha Devapriya, Soundar Kumara
    Medical Care.2016; 54(11): 1017.     CrossRef
  • Utility of models to predict 28-day or 30-day unplanned hospital readmissions: an updated systematic review
    Huaqiong Zhou, Phillip R Della, Pamela Roberts, Louise Goh, Satvinder S Dhaliwal
    BMJ Open.2016; 6(6): e011060.     CrossRef
  • Multimorbidity in risk stratification tools to predict negative outcomes in adult population
    Edurne Alonso-Morán, Roberto Nuño-Solinis, Graziano Onder, Giuseppe Tonnara
    European Journal of Internal Medicine.2015; 26(3): 182.     CrossRef
  • Predicting 30-day Hospital Readmission with Publicly Available Administrative Database
    K. Zhu, Z. Lou, J. Zhou, N. Ballester, N. Kong, P. Parikh
    Methods of Information in Medicine.2015; 54(06): 560.     CrossRef
  • Emergency Department Non-Urgent Visits and Hospital Readmissions Are Associated with Different Socio-Economic Variables in Italy
    Pamela Barbadoro, Elena Di Tondo, Vincenzo Giannicola Menditto, Lucia Pennacchietti, Februa Regnicoli, Francesco Di Stanislao, Marcello Mario D’Errico, Emilia Prospero, Chiara Lazzeri
    PLOS ONE.2015; 10(6): e0127823.     CrossRef
  • Using Decision Trees to Manage Hospital Readmission Risk for Acute Myocardial Infarction, Heart Failure, and Pneumonia
    John P. Hilbert, Scott Zasadil, Donna J. Keyser, Pamela B. Peele
    Applied Health Economics and Health Policy.2014; 12(6): 573.     CrossRef
  • A three-step approach for the derivation and validation of high-performing predictive models using an operational dataset: congestive heart failure readmission case study
    Samir E AbdelRahman, Mingyuan Zhang, Bruce E Bray, Kensaku Kawamoto
    BMC Medical Informatics and Decision Making.2014;[Epub]     CrossRef
  • Nationwide prospective study on readmission after umbilical or epigastric hernia repair
    F. Helgstrand, L. N. Jørgensen, J. Rosenberg, H. Kehlet, T. Bisgaard
    Hernia.2013; 17(4): 487.     CrossRef
  • Data Mining Application in Customer Relationship Management for Hospital Inpatients
    Eun Whan Lee
    Healthcare Informatics Research.2012; 18(3): 178.     CrossRef
Special Articles
Korean National Health Insurance Value Incentive Program: Achievements and Future Directions
Sun Min Kim, Won Mo Jang, Hyun Ah Ahn, Hyang Jeong Park, Hye Sook Ahn
J Prev Med Public Health. 2012;45(3):148-155.   Published online May 31, 2012
DOI: https://doi.org/10.3961/jpmph.2012.45.3.148
  • 9,959 View
  • 101 Download
  • 26 Crossref
AbstractAbstract PDF

Since the reformation of the National Health Insurance Act in 2000, the Health Insurance Review and Assessment Service (HIRA) in the Republic of Korea has performed quality assessments for healthcare providers. The HIRA Value Incentive Program (VIP), established in July 2007, provides incentives for excellent-quality institutions and disincentives for poor-quality ones. The program is implemented based on data collected between July 2007 and December 2009. The goal of the VIP is to improve the overall quality of care and decrease the quality gaps among healthcare institutions. Thus far, the VIP has targeted acute myocardial infarction (AMI) and Caesarian section (C-section) care. The incentives and disincentives awarded to the hospitals by their composite quality scores of the AMI and C-section scores. The results of the VIP showed continuous and marked improvement in the composite quality scores of the AMI and C-section measures between 2007 and 2010. With the demonstrated success of the VIP project, the Ministry of Health and Welfare expanded the program in 2011 to include general hospitals. The HIRA VIP was deemed applicable to the Korean healthcare system, but before it can be expanded further, the program must overcome several major concerns, as follows: inclusion of resource use measures, rigorous evaluation of impact, application of the VIP to the changing payment system, and expansion of the VIP to primary care clinics.

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  • Relationship between patient outcomes and patterns of fragmented cancer care in older adults with gastric cancer: A nationwide cohort study in South Korea
    Dong-Woo Choi, Seungju Kim, Sun Jung Kim, Dong Wook Kim, Kwang Sun Ryu, Jae Ho Kim, Yoon-Jung Chang, Kyu-Tae Han
    Journal of Geriatric Oncology.2024; 15(2): 101685.     CrossRef
  • Effects of intensive care unit quality assessment on changes in medical staff in medical institutions and in-hospital mortality
    Seungju Kim, Gui Ok Kim, Syalrom Lee, Yong Uk Kwon
    Human Resources for Health.2024;[Epub]     CrossRef
  • Beta-blocker therapy in patients with acute myocardial infarction: not all patients need it
    Seung-Jae Joo
    Acute and Critical Care.2023; 38(3): 251.     CrossRef
  • Patient Perspectives of Chronic Disease Management and Unmet Care Needs in South Korea: A Qualitative Study
    Kyunghee Yi, Sujin Kim
    Journal of Patient Experience.2023;[Epub]     CrossRef
  • Epidemiological changes in cytomegalovirus end-organ diseases in a developed country: A nationwide, general-population-based study
    Seul Gi Yoo, Kyung Do Han, Kyoung Hwa Lee, Joohee Lim, Yeonju La, Da Eun Kwon, Sang Hoon Han
    Journal of Microbiology, Immunology and Infection.2022; 55(5): 812.     CrossRef
  • Improvement in Age at Mortality and Changes in Causes of Death in the Population with Diabetes: An Analysis of Data from the Korean National Health Insurance and Statistical Information Service, 2006 to 2018
    Eugene Han, Sun Ok Song, Hye Soon Kim, Kang Ju Son, Sun Ha Jee, Bong-Soo Cha, Byung-Wan Lee
    Endocrinology and Metabolism.2022; 37(3): 466.     CrossRef
  • Feasibility of Capturing Adverse Events From Insurance Claims Data Using International Classification of Diseases, Tenth Revision, Codes Coupled to Present on Admission Indicators
    Juyoung Kim, Eun Young Choi, Won Lee, Hae Mi Oh, Jeehee Pyo, Minsu Ock, So Yoon Kim, Sang-il Lee
    Journal of Patient Safety.2022; 18(5): 404.     CrossRef
  • Trends and disparities in avoidable, treatable, and preventable mortalities in South Korea, 2001-2020: comparison of capital and non-capital areas
    Sang Jun Eun
    Epidemiology and Health.2022; 44: e2022067.     CrossRef
  • Performance and Challenges in Implementing the National Quality Assessment Program
    Bo Yeon Kim
    Health Insurance Review & Assessment Service Research.2021; 1(1): 23.     CrossRef
  • The effect of pay for performance on income inequality between medical and non-medical staff
    Mohammad Mohammadi, Mehdi Yousefi, Amin Mohammadi, Elahe Pourahmadi, Hossein Ebrahimipour, Saeed Malek Sadati
    Journal of Health Administration.2021; 24(3): 43.     CrossRef
  • Cardiovascular and Bleeding Risks Associated With Nonsteroidal Anti-Inflammatory Drugs After Myocardial Infarction
    Dong Oh Kang, Hyonggin An, Geun U Park, Yunjin Yum, Eun Jin Park, Yoonjee Park, Won Young Jang, Woohyeun Kim, Jah Yeon Choi, Seung-Young Roh, Jin Oh Na, Jin Won Kim, Eung Ju Kim, Seung-Woon Rha, Chang Gyu Park, Hong Seog Seo, Cheol Ung Choi
    Journal of the American College of Cardiology.2020; 76(5): 518.     CrossRef
  • Dilemmas Within the Korean Health Insurance System
    Donghwi Park, Min Cheol Chang
    Journal of Preventive Medicine and Public Health.2020; 53(4): 285.     CrossRef
  • Performance Pay in Hospitals: An Experiment on Bonus–Malus Incentives
    Nadja Kairies-Schwarz, Claudia Souček
    International Journal of Environmental Research and Public Health.2020; 17(22): 8320.     CrossRef
  • Increased Age of Death and Change in Causes of Death Among Persons With Diabetes Mellitus From the Korean National Health Insurance and Statistical Information Service, 2006 to 2018
    Eugene Han, Sun Ok Song, Hye Soon Kim, Kang Ju Son, Sun Ha Jee, Bong-Soo Cha, Byung-Wan Lee
    SSRN Electronic Journal .2020;[Epub]     CrossRef
  • Design and effects of outcome-based payment models in healthcare: a systematic review
    F. P. Vlaanderen, M. A. Tanke, B. R. Bloem, M. J. Faber, F. Eijkenaar, F. T. Schut, P. P. T. Jeurissen
    The European Journal of Health Economics.2019; 20(2): 217.     CrossRef
  • Accuracy of an administrative database for pancreatic cancer by international classification of disease 10th codes: A retrospective large-cohort study
    Young-Jae Hwang, Seon Mee Park, Soomin Ahn, Jong-Chan Lee, Young Soo Park, Nayoung Kim
    World Journal of Gastroenterology.2019; 25(37): 5619.     CrossRef
  • Relationship between nurse staffing level and adult nursing-sensitive outcomes in tertiary hospitals of Korea: Retrospective observational study
    Chul-Gyu Kim, Kyun-Seop Bae
    International Journal of Nursing Studies.2018; 80: 155.     CrossRef
  • Designing a Framework for “Iranian Pay for Performance” Program for Non-Medical Workforce in Hospitals
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Designing an Effective Pay-for-performance System in the Korean National Health Insurance
Hyoung-Sun Jeong
J Prev Med Public Health. 2012;45(3):127-136.   Published online May 31, 2012
DOI: https://doi.org/10.3961/jpmph.2012.45.3.127
  • 16,135 View
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AbstractAbstract PDF

The challenge facing the Korean National Health Insurance includes what to spend money on in order to elevate the 'value for money.' This article reviewed the changing issues associated with quality of care in the Korean health insurance system and envisioned a picture of an effective pay-for-performance (P4P) system in Korea taking into consideration quality of care and P4P systems in other countries. A review was made of existing systematic reviews and a recent Organization for Economic Cooperation and Development survey. An effective P4P in Korea was envisioned as containing three features: measures, basis for reward, and reward. The first priority is to develop proper measures for both efficiency and quality. For further improvement of quality indicators, an electronic system for patient history records should be built in the near future. A change in the level or the relative ranking seems more desirable than using absolute level alone for incentives. To stimulate medium- and small-scale hospitals to join the program in the next phase, it is suggested that the scope of application be expanded and the level of incentives adjusted. High-quality indicators of clinical care quality should be mapped out by combining information from medical claims and information from patient registries.

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  • Distribution of monetary incentives in health insurance scheme influences acupuncture treatment choices: An experimental study
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    PLOS ONE.2019; 14(6): e0218154.     CrossRef
  • Assessing quality of primary diabetes care in South Korea and Taiwan using avoidable hospitalizations
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    BMJ Open.2016; 6(4): e009538.     CrossRef
  • Pay for performance in the inpatient sector: A review of 34 P4P programs in 14 OECD countries
    Ricarda Milstein, Jonas Schreyoegg
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  • Incidence of Adult In-Hospital Cardiac Arrest Using National Representative Patient Sample in Korea
    Yuri Choi, In Ho Kwon, Jinwoo Jeong, Junyoung Chung, Younghoon Roh
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  • The Possibility of Expanding Pay-for-Performance Program as a Provider Payment System
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Original Articles
Perceived Service Quality among Outpatients Visiting Hospitals and Clinics and Their Willingness to Re-utilize the Same Medical Institutions.
Minsoo Jung, Keon Hyung Lee, Mankyu Choi
J Prev Med Public Health. 2009;42(3):151-159.
DOI: https://doi.org/10.3961/jpmph.2009.42.3.151
  • 4,948 View
  • 83 Download
  • 11 Crossref
AbstractAbstract PDF
OBJECTIVES
This study was to determine how the perception and the satisfaction of outpatients who utilized clinics and hospitals are structurally related with their willingness to utilize the same institution in the future. METHODS: Three hundred and ten responses (via convenient sampling) were collected from 5 hospitals and 20 clinics located in Seoul listed in the "Korea National Hospital Directory 2005". Service quality was utilized as the satisfaction measurement tool. For analysis, we used a structural equation modeling method. RESULTS: The determining factors for general satisfaction with medical services are as follows: medical staff, reasonability of payment, comfort and accessibility. Such results may involve increased competition in the medical market and increased demands for quality medical services, which drive the patients to visit hospitals on their own on the basis of changed determining factors for satisfaction. CONCLUSIONS: The structural equation model showed that the satisfaction of outpatients with the quality of medical services is influenced by a few sub-dimensional satisfaction factors. Among these sub-dimensional satisfaction factors, the satisfaction with medical staff and payment were determined to exert a significant effect on overall satisfaction with the quality of medical services. The structural relationship in which overall satisfaction perceived by patients significantly influences their willingness to use the same institution in the future was also verified.
Summary

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  • Factors associated with the download and use of mobile personal health record applications in Korean hospitals
    Hyeon Seok Kim, Dahye Lee, Kee Nyun Kim, Sang Mi Kim, Young-Taek Park
    Health Informatics Journal.2023;[Epub]     CrossRef
  • Factors Associated with Website Operation among Small Hospitals and Medical and Dental Clinics in Korea
    Young-Taek Park, Young Jae Kim, Kwang Gi Kim
    Healthcare Informatics Research.2022; 28(4): 355.     CrossRef
  • Patient satisfaction and loyalty to the healthcare organization
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    International Journal of Pharmaceutical and Healthcare Marketing.2021; 15(4): 496.     CrossRef
  • Factors of quality of care and their association with smartphone based PHR adoption in South Korean hospitals
    Byung Kwan Choi, Young-Taek Park, Hyeoun-Ae Park, Chris Lane, Emmanuel C. Jo, Sunghong Kang
    BMC Medical Informatics and Decision Making.2021;[Epub]     CrossRef
  • The effect of patient satisfaction with academic hospitals on their loyalty
    Mina Rostami, Leila Ahmadian, Yunes Jahani, Aliakbar Niknafs
    The International Journal of Health Planning and Management.2019;[Epub]     CrossRef
  • Association between Types of Usual Source of Care and User Perception of Overall Health Care Service Quality in Korea
    Nak-Jin Sung, Jae-Ho Lee
    Korean Journal of Family Medicine.2019; 40(3): 143.     CrossRef
  • Assessment of patients’ satisfaction and associated factors among outpatients received mental health services at public hospitals of Mekelle Town, northern Ethiopia
    Haftom Desta, Tesfay Berhe, Solomon Hintsa
    International Journal of Mental Health Systems.2018;[Epub]     CrossRef
  • Analysis of Appropriate Outpatient Consultation Time for Clinical Departments
    Chan Hee Lee, Hyunsun Lim, Youngnam Kim, Ai Hee Park, Eun-Cheol Park, Jung-Gu Kang
    Health Policy and Management.2014; 24(3): 254.     CrossRef
  • Positioning Patient-Perceived Medical Services to Develop a Marketing Strategy
    Minsoo Jung, Myung-Sun Hong
    The Health Care Manager.2012; 31(1): 52.     CrossRef
  • Analysis of the Factors Related to the Needs of Patients with Cancer
    Jung-A Lee, Sun-Hee Lee, Jong-Hyock Park, Jae-Hyun Park, Sung-Gyeong Kim, Ju-Hyun Seo
    Journal of Preventive Medicine and Public Health.2010; 43(3): 222.     CrossRef
Dimensions of Consumer Ratings of a Hospital Outpatient Service Quality.
Ki Tae Moon, Seung Hum Yu, Woo Hyun Cho, Dong Kee Kim, Yunwhan Lee
Korean J Prev Med. 2000;33(4):495-504.
  • 2,065 View
  • 20 Download
AbstractAbstract PDF
OBJECTIVES
To examine various dimensions of consumer ratings of health care service with factor analysis and to find which factors influence the overall quality of health care service. METHODS: A cross-sectional study was conducted on outpatients of a general hospital located in Sungnam City. A self-administered questionnaire was used to assess the consumer? ratings of health care service received. The response rate was 92.8% with a total of 537 persons completing the questionnaire. Factor analysis was performed on 34 items evaluating the quality of health care service. Items were grouped into 5 dimensions as a result of factor analysis and the reliability and validity of influence on patient service assessment were evaluated for each dimension. RESULTS: The 5 dimensions were as follows ; 1) physician services, 2) non-physician services, 3) process 4) facilities, and 5) cleanliness. A positive correlation with the quality of health care service was found for the dimensions of non-physician services and process, while no significant correlation was found for the dimensions of physician services, facilities, and cleanliness. CONCLUSIONS: The result of this study may provide basic information for the development of future self-administered questionnaires of consumer ratings and for the evaluation of quality improvement activities in hospital outpatient settings.
Summary

JPMPH : Journal of Preventive Medicine and Public Health