Log In Sign up
The largest database of trusted experimental protocols
> Procedures > Health Care Activity > National Health Insurance

National Health Insurance

Discover how PubCompare.ai enhances reproducibility and accuracy in National Health Insurance research.
Our AI-driven protocol optimization helps you locate the best protocols from literature, preprints, and patents.
Compare findings and identify the most effective solutions for your National Health Insurance needs.
Experience the power of data-driven decision making with PubCompare.ai.

Most cited protocols related to «National Health Insurance»

1
Genetics and Health Profile of Estonian Biobank
Cited 129 times
The EstBB is a population-based biobank at the Institute of Genomics, University of Tartu. The current cohort size is 200,000 individuals (aged ≥18 years), reflecting the age, sex and geographical distribution of the adult Estonian population. Overall, 83% of the samples are from Estonian individuals, 14% from Russian people and 3% from other ethnicities. All participants were recruited by general practitioners, physicians in hospitals and during promotional events. After recruitment, all participants completed a questionnaire about their health status, lifestyle and diet. Specifically, the questionnaire included personal data (place of birth, place(s) of living, nationality, among others), genealogical data (family history of medical conditions spanning four generations), educational and occupational history, and lifestyle data (physical activity, dietary habits (food frequency questionnaires), smoking status, alcohol consumption, women’s health and quality of life). The EstBB database is linked with national registries (such as the Cancer Registry and Causes of Death Registry), hospital databases and the database of the national health insurance fund, which holds treatment and procedure service bills. Diseases and health problems are recorded as ICD-10 codes and prescribed medicine according to the ATC classification. These health data are continuously updated through periodical linking to national electronic databases and registries. All participants were genotyped with genome-wide chip arrays and further imputed with a population-specific imputation panel consisting of 2,244 high-coverage (30 times) whole-genome sequence data from individuals and 16,271,975 high-quality variants57 (link). Researchers at the EstBB ran an association analysis of the 15 phenotypes (Supplementary Table 8) used in this study in 136,724 individuals. The association analysis was conducted with SAIGE52 mixed models with age, sex and ten PCs used as covariates.
We used the Pan UKBB (https://pan.ukbb.broadinstitute.org/) project European subset association analysis summary statistics in the UKBB replication58 (Supplementary Table 7).
As both the EstBB and the UKBB are on human genome build 37, we lifted over the coordinates to build 38 to match FinnGen. Variants were then matched on the basis of chromosome, position, reference and alternative alleles.
Inverse variance weighted meta-analysis was used to perform a meta-analysis on the three cohorts (code available at https://github.com/FINNGEN/META_ANALYSIS).
Kurki M.I., Karjalainen J., Palta P., Sipilä T.P., Kristiansson K., Donner K.M., Reeve M.P., Laivuori H., Aavikko M., Kaunisto M.A., Loukola A., Lahtela E., Mattsson H., Laiho P., Della Briotta Parolo P., Lehisto A.A., Kanai M., Mars N., Rämö J., Kiiskinen T., Heyne H.O., Veerapen K., Rüeger S., Lemmelä S., Zhou W., Ruotsalainen S., Pärn K., Hiekkalinna T., Koskelainen S., Paajanen T., Llorens V., Gracia-Tabuenca J., Siirtola H., Reis K., Elnahas A.G., Sun B., Foley C.N., Aalto-Setälä K., Alasoo K., Arvas M., Auro K., Biswas S., Bizaki-Vallaskangas A., Carpen O., Chen C.Y., Dada O.A., Ding Z., Ehm M.G., Eklund K., Färkkilä M., Finucane H., Ganna A., Ghazal A., Graham R.R., Green E.M., Hakanen A., Hautalahti M., Hedman Å.K., Hiltunen M., Hinttala R., Hovatta I., Hu X., Huertas-Vazquez A., Huilaja L., Hunkapiller J., Jacob H., Jensen J.N., Joensuu H., John S., Julkunen V., Jung M., Junttila J., Kaarniranta K., Kähönen M., Kajanne R., Kallio L., Kälviäinen R., Kaprio J., Kerimov N., Kettunen J., Kilpeläinen E., Kilpi T., Klinger K., Kosma V.M., Kuopio T., Kurra V., Laisk T., Laukkanen J., Lawless N., Liu A., Longerich S., Mägi R., Mäkelä J., Mäkitie A., Malarstig A., Mannermaa A., Maranville J., Matakidou A., Meretoja T., Mozaffari S.V., Niemi M.E., Niemi M., Niiranen T., O´Donnell C.J., Obeidat M., Okafo G., Ollila H.M., Palomäki A., Palotie T., Partanen J., Paul D.S., Pelkonen M., Pendergrass R.K., Petrovski S., Pitkäranta A., Platt A., Pulford D., Punkka E., Pussinen P., Raghavan N., Rahimov F., Rajpal D., Renaud N.A., Riley-Gillis B., Rodosthenous R., Saarentaus E., Salminen A., Salminen E., Salomaa V., Schleutker J., Serpi R., Shen H.Y., Siegel R., Silander K., Siltanen S., Soini S., Soininen H., Sul J.H., Tachmazidou I., Tasanen K., Tienari P., Toppila-Salmi S., Tukiainen T., Tuomi T., Turunen J.A., Ulirsch J.C., Vaura F., Virolainen P., Waring J., Waterworth D., Yang R., Nelis M., Reigo A., Metspalu A., Milani L., Esko T., Fox C., Havulinna A.S., Perola M., Ripatti S., Jalanko A., Laitinen T., Mäkelä T.P., Plenge R., McCarthy M., Runz H., Daly M.J, & Palotie A. (2023). FinnGen provides genetic insights from a well-phenotyped isolated population. Nature, 613(7944), 508-518.
Full text: Click here
Publication 2023
Adult Alleles Chromosomes Diet DNA Chips Ethnicity Europeans Food General Practitioners Genome Genome, Human Malignant Neoplasms National Health Insurance Pharmaceutical Preparations Phenotype Physicians Population Group Woman
2
Nationwide Cohort Study Using Taiwan NHIRD
Cited 90 times

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2017
Childbirth Compulsive Behavior Diagnosis Health Insurance Hospitalization National Health Insurance National Health Programs Outpatients Patient Discharge Patient Representatives Patients
3
Adolescent Mental Health Parent Questionnaire
Cited 74 times
We attempted to collect information from a parent or parent-surrogate of each adolescent in order to obtain an additional perspective on the adolescent’s mental health and its correlates. A parent self-report questionnaire (SAQ) was developed for this purpose. Although an interview with the parent would have been preferable because the mode of administration would be the same as that of the child, and follow-up questions to clarify responses could have been included, a self-report format was necessary because of budgetary constraints. As shown in Table 1, parent reports focused on the five adolescent disorders for which previous methodological research has most consistently shown that parental reports are important for making diagnoses: attention-deficit/hyperactivity disorder, conduct disorder, oppositional defiant disorder, major depressive episode, and dysthymic disorder.35 (link), 36 (link) As in previous studies, we combined diagnostic information obtained from adolescents and parents when making final diagnostic classifications.37 (link) The Strength and Difficulties Questionnaire (SDQ)20 (link) was also included in the SAQ in order to obtain a dimensional rating of child mental health problems as well as to provide calibration data that could be used to interpret the SDQ scores in the NHIS. The average administration time for the final SAQ in pilot studies was approximately 45 minutes.
Merikangas K.R., Avenevoli S., Costello E.J., Koretz D, & Kessler R.C. (2009). The National Comorbidity Survey Adolescent Supplement (NCS-A): I. Background and Measures. Journal of the American Academy of Child and Adolescent Psychiatry, 48(4), 367-369.
Publication 2009
Adolescent Child Conduct Disorder Diagnosis Disorder, Attention Deficit-Hyperactivity Dysthymic Disorder Mental Health National Health Insurance Oppositional Defiant Disorder Parent
4
Korean National Health Insurance Cohort
Cited 61 times
The data from the Korean National Health Insurance Service-Health Screening Cohort was used [8 (link)]. The Korean National Health Insurance Service (NHIS) chooses about 10% of random samples (n=about 515,000) directly from all people who had a health check-up from 2002 through 2003 year (n=about 5,150,000). The age and sex specific distributions of the cohort population is described in online [9 (link),10 ].
All of ≥40 years old Koreans and their families are requested to have a biannual health check without cost [11 (link)]. Because all Korean citizens are registered with a 13-digit resident registration number for lifelong, the thorough population statistics can be calculated in this study. All Koreans have to register in the NHIS. The 13-digit resident registration number has to be used in all Korean hospitals and clinics. Thus, the medical records was prevented to be overlapped, even in case of a patient moves from one place to another. In addition, the Korean Health Insurance Review and Assessment (HIRA) system managed all medical treatments in Korea. The causes and date of death diagnosed by medical doctors on the death certificate are legally announced to administrative entity.
This NHIS included health insurance claim codes (procedures and prescriptions), diagnostic codes using the International Classification of Disease-10 (ICD-10), death records, socioeconomic data and health check-up data (body mass index [BMI], drinking, smoking habit, blood pressure, urinalysis, hemoglobin, fasting glucose, lipid parameters, creatinine, and liver enzymes) for each participant over the period from 2002 to 2013 [10 ,11 (link)].
Kim S.Y., Min C., Oh D.J, & Choi H.G. (2019). Tobacco Smoking and Alcohol Consumption Are Related to Benign Parotid Tumor: A Nested Case-Control Study Using a National Health Screening Cohort. Clinical and Experimental Otorhinolaryngology, 12(4), 412-419.
Publication 2019
Blood Pressure Creatinine Diagnosis Enzymes Fingers Glucose Health Insurance Health Services, National Hemoglobin Index, Body Mass Koreans Lipids Liver National Health Insurance Patients Physicians Prescriptions Urinalysis
5
Leveraging Claims Data for Comprehensive Health Assessment
Cited 44 times
The clinical diagnoses in the claims records were coded using the clinicians' judgment based on clinical history, diagnostic criteria, image, or biochemical data. The present analysis used NHIRD claims records for the one year preceding the 2005 Taiwan NHIS. We set May 15, 2005, which was approximately the median date of the survey, as the date for interviewing all participants. Both ambulatory and inpatient claims records for the period from May 16, 2004 to May 15, 2005 were extracted from the NHIRD for subsequent analysis. We used ICD9-CM codes in any diagnostic position, rather than only the primary position, to identify 14 clinical diagnoses from the ambulatory and inpatient claims in the NHIRD. Claims records from all types of healthcare settings were included. Among outpatient claims records during the study period, approximately 82.4% were Western Medicine, 7.8% were dental services, and 9.8% were Chinese Medicine. The target medications were identified using the Anatomical Therapeutic Chemical (ATC) classification system [8] (link). Given that a medication can be used for different indications, we defined a participant as a medication user only if the patient had a prescription record for a targeted medication with a corresponding diagnosis. For example, diuretics can be prescribed for hypertension or renal diseases; therefore, only those participants who received a prescribed diuretic and had a diagnosis of hypertension were defined as antihypertensive users. Regarding health system utilization, we assessed the type of medical services provided for each claims record in the NHIRD. The detailed ICD9-CM codes for clinical diagnoses and ATC codes for medication are provided in Table S1.
Wu C.S., Lai M.S., Gau S.S., Wang S.C, & Tsai H.J. (2014). Concordance between Patient Self-Reports and Claims Data on Clinical Diagnoses, Medication Use, and Health System Utilization in Taiwan. PLoS ONE, 9(12), e112257.
Full text: Click here
Publication 2014
Antihypertensive Agents Chinese Dental Care Diagnosis Diuretics High Blood Pressures Inpatient Kidney Diseases National Health Insurance Outpatients Patients Pharmaceutical Preparations Therapeutics

Most recents protocols related to «National Health Insurance»

1
Prevalence of Potentially Inappropriate Medications in Finnish Older Adults
Since the Meds75+ database is intended to be used for older people aged 75 years or older this age limit was applied in the study population. In this retrospective cross-sectional study, the study population was drawn from the entire population of older people aged 75 years or older in Finland in 2017–2019. We obtained data on purchased prescription medicines, which reflects PIM use better than prescriptions only. The Prescription Centre of Finland is a national register held by the SII and contains all human medicine prescriptions and their medicine purchases delivered from pharmacies in Finland since 2017 [29 , 30 ]. The database covers all Finnish citizens except persons living permanently in institutions (< 1% of persons aged ≥75 years) and medicines given in hospitals. The study cohort consisted of older people who had purchased at least one prescription medicine, reimbursed by National Health Insurance (tax-supported public social security coverage) or not, considered as a PIM (see Additional file 1) in 2017–2019 and were aged 75 years or over at the time of purchase. In order to calculate the total number of PIMs purchased by an individual, we used a pseudonymized identification number for every person.
Altogether the data consisted of 523,263 older person and their 14,488,277 medicine purchases from 1 January 2017 to 31 December 2019. To allow comparison between study years, the data was split into three cohorts, one for each year. After exclusion of prescription purchases with incorrect age criteria, missing gender, ATC code not included in the PIM summary table or invalid pharmaceutical form (e.g. topical estrogen), the final data used to assess the prevalence of PIMs in Finland consisted of 497,663 older people and their 11,685,648 medicine purchases.
The population characteristics were presented as mean values with standard deviations. The prevalence of PIM use was calculated based on census data obtained from Statistics Finland (mean population aged 75 years or older in 2017: N = 500,820.5; 2018: N = 506,884.5; and 2019: N = 518,276.0) [31 ]. The annual prevalence was calculated by dividing the number of older persons with at least one PIM purchase by the average population of each year. The prevalence of PIM use was presented as annual percentages per criteria. The number of PIMs per person was calculated as each individual ATC code regardless of the number of purchases. We also reported the most commonly used PIM classes and their prevalence per criteria during the three-year observation period. In addition, the most commonly used PIMs per criteria were presented. For numerical values, relevant descriptive statistics were presented (mean, standard deviation, median). The statistical analyses were performed using the IBM© SPSS© Statistics software, version 26.
Paulamäki J., Jyrkkä J., Hyttinen V, & Jämsen E. (2023). Prevalence of potentially inappropriate medication use in older population: comparison of the Finnish Meds75+ database with eight published criteria. BMC Geriatrics, 23, 139.
Full text: Click here
Publication 2023
ARID1A protein, human Estrogens Homo sapiens National Health Insurance Pharmaceutical Preparations Prescription Drugs
2
National Health Insurance Research Database in Taiwan
Cited 1 time
Taiwan's National Health Insurance (NHI) system covers 99% of the population. The National Health Insurance Research Database (NHIRD) was developed based on information collected from the population in Taiwan [13 (link)]. The International Classification of Diseases 9th revision Clinical Modification (ICD-9-CM) was used in the NHIRD from 1995 to 2016. The ICD-10 has been used since 2017 [14 (link)]. Details of this data source and design are described elsewhere [15 (link)]. Briefly, information about prisoners from January 1st, 2013, to December 31st, 2013, was presented in a subset of the NHIRD. Our study included information from 2013 (January 1st to December 31st); therefore, we used the ICD-9 to present the results. This study was approved by the Institutional Review Board of Cheng-Hsin General Hospital (CHGH-IRB: (471) 104–07). All the procedures were in accordance with the required guidelines, and all the participants provided informed consent.
Jiesisibieke Z.L., Lin J., Lin Y.C., Hsiao Y.Y, & Tung T.H. (2023). Prevalence of skin diseases in Taiwan prisons: a population-based study. BMC Public Health, 23, 460.
Full text: Click here
Publication 2023
National Health Insurance Prisoners
3
Epidemiology of Ruptured Abdominal Aortic Aneurysm in South Korea
In South Korea, the National Health Insurance (NHI) system is an obligatory universal health insurance system that covers 98% of the population. The Health Insurance and Review Assessment (HIRA) database is a government-operated organization that reviews and assesses NHI claims. Data from the year 2010 onwards are publicly accessible online and include sociodemographic information, utilization of inpatient and outpatient services, medical dispensing claims, and as well as diagnoses according to the ICD 10th revision, clinical modification (ICD-10) code.
All patients diagnosed with rAAA from the HIRA database by ICD-10 code I71.3 from January 1, 2010 to December 31, 2020 were identified and reviewed.
Jo E.A., Seong S., Ahn S., Mo H., Jung I.M., Kim H.K., Ko H., Han A., Min S, & Min S.K. (2023). Validation of I71.3 code for ruptured abdominal aortic aneurysm in Korea: misplaced diagnosis in claims data. Annals of Surgical Treatment and Research, 104(3), 170-175.
Publication 2023
Health Insurance Health Services, Outpatient Inpatient National Health Insurance Patients
4
Dementia Risk in Acute Ischemic Stroke
We used a population-based dataset from the Korean National Health Information Database (K-NHID). The K-NHID incorporates data from the Korean National Health Insurance Service (NHIS), a mandatory health insurance program that covers more than 97% of the Korean population. All insured adults are provided with biennial general health checkups, including demographic, clinical, and laboratory variables, and a self-administered questionnaire regarding lifestyle and behavior habits. Furthermore, the K-NHID consists of demographic information (including age, sex, weight, height, income, and socioeconomic status) and medical claim data (inpatient and outpatient usage of medical services, medical bills, prescription records, and date of death). This study was approved by the institutional review board of Dongtan Sacred Heart Hospital (IRB number: HDT 2022-04-002). Participants who underwent national health checkups provided written informed consent for the use of their data for research purposes. All study methods were performed in accordance with the Declaration of Helsinki.
We identified 1,005,879 patients who were newly diagnosed with acute IS between January 2010 and December 2016. Ischemic stroke was defined by the diagnosis of ICD-10 codes I63 and I64 with admission and combined brain imaging studies, including computed tomography (CT) or magnetic resonance image (MRI)12 (link). This definition has been widely used in previous K-NHID studies and is well-validated with high accuracy, having 92.2% positive predictive value and 91.2% sensitivity13 (link)–16 (link). We excluded patients who did not undergo two consecutive health checkups within two years before and after the diagnosis of IS. Among the remaining 264,639 patients, those younger than 40 years and those with a history of dementia before the index date (second health examination after IS diagnosis) were also excluded. Lastly, a 1-year washout period from the index date to remove the early occurrence of dementia additionally excluded 7290 patients. Finally, 223,426 patients with IS were included in the study (Fig. 1).

Flowchart of the selection of subjects.

Cheon D.Y., Han K.D., Kim C.H., Oh M.S., Lee B.C., Kim Y., Lee S.H., Kim C., Lim J.S., Lee M, & Yu K.H. (2023). Association between exercise habit changes and incident dementia after ischemic stroke. Scientific Reports, 13, 3959.
Full text: Click here
Publication 2023
Adult Brain Dementia Diagnosis Health Insurance Health Services, Outpatient Heart Inpatient Koreans National Health Insurance Patients Population Health Stroke, Ischemic X-Ray Computed Tomography Youth
5
Taiwanese National Health Insurance Data Analysis
This retrospective cohort study utilized comprehensive healthcare data accessed from the National Health Insurance Research Database (NHIRD) of Taiwan. Moreover, 99% of the Taiwanese population were enrolled in the Taiwanese National Health Insurance (NHI), which was a compulsory-enrollment and single-payer social insurance. Researchers were allowed access to each enrollee’s anonymized registration records, including ambulatory care, inpatient data, emergency data, and diagnostic codes for academic purposes using an undisclosed identification code. The diagnostic codes of HSCT and malignancies designated by the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) were identified between 2000 and 2013.
Liu H.L., Chen Y.H., Chung C.H., Wu G.J., Tsao C.H., Sun C.A., Chien W.C, & Hung C.T. (2023). Risk of Secondary Malignancies in Hematopoietic Stem Cell Transplantation Recipients: A Nationwide Population-Based Study in Taiwan. Balkan Medical Journal, 40(2), 131-138.
Publication 2023
Care, Ambulatory Comprehensive Health Care Compulsive Behavior Diagnosis Emergencies Inpatient Malignant Neoplasms National Health Insurance

Top products related to «National Health Insurance»

1
Sas version 9 by SAS Institute
Sourced in United States, Austria, Japan, Cameroon, Germany, United Kingdom, Canada, Belgium, Israel, Denmark, Australia, New Caledonia, France, Argentina, Sweden, Ireland, India
SAS version 9.4 is a statistical software package. It provides tools for data management, analysis, and reporting. The software is designed to help users extract insights from data and make informed decisions.
2
Sas 9 by SAS Institute
Sourced in United States, Austria, Japan, Belgium, United Kingdom, Cameroon, China, Denmark, Canada, Israel, New Caledonia, Germany, Poland, India, France, Ireland, Australia
SAS 9.4 is an integrated software suite for advanced analytics, data management, and business intelligence. It provides a comprehensive platform for data analysis, modeling, and reporting. SAS 9.4 offers a wide range of capabilities, including data manipulation, statistical analysis, predictive modeling, and visual data exploration.
3
Sas software version 9 by SAS Institute
Sourced in United States, Austria, Japan, Belgium, Brazil, United Kingdom, Cameroon
SAS software version 9.4 is a comprehensive data analysis and management solution. It provides advanced statistical and analytical capabilities for organizations to manage, analyze, and report on their data. The software includes a range of tools and features to support various data-driven tasks, such as data manipulation, statistical modeling, and predictive analytics.
4
Sas software by SAS Institute
Sourced in United States, Japan, Austria, Germany, United Kingdom, France, Cameroon, Denmark, Israel, Sweden, Belgium, Italy, China, New Zealand, India, Brazil, Canada
SAS software is a comprehensive analytical platform designed for data management, statistical analysis, and business intelligence. It provides a suite of tools and applications for collecting, processing, analyzing, and visualizing data from various sources. SAS software is widely used across industries for its robust data handling capabilities, advanced statistical modeling, and reporting functionalities.
5
Stata 13 by StataCorp
Sourced in United States, Denmark, United Kingdom, Austria, Sweden
Stata 13 is a comprehensive, integrated statistical software package developed by StataCorp. It provides a wide range of data management, statistical analysis, and graphical capabilities. Stata 13 is designed to handle complex data structures and offers a variety of statistical methods for researchers and analysts.
6
Stata version 14 by StataCorp
Sourced in United States, Denmark, Austria, United Kingdom, Japan, Canada
Stata version 14 is a software package for data analysis, statistical modeling, and graphics. It provides a comprehensive set of tools for data management, analysis, and reporting. Stata version 14 includes a wide range of statistical techniques, including linear regression, logistic regression, time series analysis, and more. The software is designed to be user-friendly and offers a variety of data visualization options.
7
Stata 16 by StataCorp
Sourced in United States, United Kingdom, Austria
Stata 16 is a comprehensive statistical software package designed for data analysis, management, and visualization. It provides a wide range of tools and functionalities for researchers, academics, and professionals working with quantitative data. Stata 16 offers advanced statistical methods, data manipulation capabilities, and flexible programming features to support various analytical tasks.
8
Stata version 13 by StataCorp
Sourced in United States, Denmark, Austria, United Kingdom
Stata version 13 is a software package designed for data analysis, statistical modeling, and visualization. It provides a comprehensive set of tools for managing, analyzing, and presenting data. Stata 13 offers a wide range of statistical methods, including regression analysis, time-series analysis, and multilevel modeling, among others. The software is suitable for use in various fields, such as economics, social sciences, and medical research.
9
Spss 23 by IBM
Sourced in United States, United Kingdom, Germany, Belgium, Japan, Spain, Austria, France, India
SPSS 23.0 is a software application for statistical analysis. It provides a comprehensive set of tools for data management, analysis, and reporting. The core function of SPSS 23.0 is to enable users to perform a wide range of statistical procedures, including descriptive statistics, regression analysis, and hypothesis testing.
10
Sas v9 by SAS Institute
Sourced in United States, Austria, United Kingdom, Cameroon, Belgium, Israel, Japan, Australia, France, Germany
SAS v9.4 is a software product developed by SAS Institute. It is a comprehensive data analysis and statistical software suite. The core function of SAS v9.4 is to provide users with tools for data management, analysis, and reporting.

More about "National Health Insurance"

Explore the power of data-driven decision making in National Health Insurance research with PubCompare.ai.
Our innovative AI-driven platform enhances reproducibility and accuracy, helping you navigate the complex landscape of healthcare protocols.
Discover the best practices from published literature, preprints, and patents, and compare findings to identify the most effective solutions for your specific National Health Insurance needs.
Streamline your research process with our cutting-edge tools.
Leverage the capabilities of statistical software like SAS version 9.4, Stata version 14, and SPSS 23.0 to analyze and interpret data, ensuring evidence-based decision making.
Stay ahead of the curve with the latest advancements in National Health Insurance research, and empower your team with the insights they need to drive impactful change.
Experience the transformative power of PubCompare.ai and unlock new possibilities in the field of healthcare policy and management.
Embark on a data-driven journey and make informed decisions that positively impact National Health Insurance outcomes.
Elevate your research and uncover the most effective strategies for addressing the challenges faced by the healthcare industry.