Dataset Information

The effect of the Iranian health transformation plan on hospitalization rate: insights from an interrupted time series analysis.

ABSTRACT: BACKGROUND:Healthcare policy- and decision-makers make efforts to build and maintain high-performing and effective health systems, implementing effectiveness programs and health reforms. In May 2014, the Iranian Ministry of Health and Medical Education has launched a series of ambitious reforms, known as the Health Transformation Plan (HTP). This study aimed to determine the effect of the HTP on hospitalization rate in Iranian public hospitals affiliated to the Ministry of Health and Medical Education. METHODS:This study was designed as a quasi-experimental, counterfactual study utilizing the interrupted time series analysis (ITSA), comparing the trend of hospitalization rate before and after the HTP implementation in 16 hospitals in the Lorestan province. Data was collected from March 2012 to February 2019. RESULTS:In the first month of the HTP implementation, an increase of 2.627 [95% CI: 1.62-3.63] was noted (P?

SUBMITTER: Beiranvand S

PROVIDER: S-EPMC7168862 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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The effect of the Iranian health transformation plan on hospitalization rate: insights from an interrupted time series analysis.

Beiranvand Siavash S Saki Mandana M Behzadifar Meysam M Bakhtiari Ahad A Behzadifar Masoud M Keshvari Mohammad M Bragazzi Nicola Luigi NL

BMC health services research 20200419 1

<h4>Background</h4>Healthcare policy- and decision-makers make efforts to build and maintain high-performing and effective health systems, implementing effectiveness programs and health reforms. In May 2014, the Iranian Ministry of Health and Medical Education has launched a series of ambitious reforms, known as the Health Transformation Plan (HTP). This study aimed to determine the effect of the HTP on hospitalization rate in Iranian public hospitals affiliated to the Ministry of Health and Med ...[more]

PMID: 32306975

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Project description:BACKGROUND:Implementation of the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) in the U.S. on October 1, 2015 was a significant policy change with the potential to affect established injury morbidity trends. This study used data from a single state to demonstrate 1) the use of a statistical method to estimate the effect of this coding transition on injury hospitalization trends, and 2) interpretation of significant changes in injury trends in the context of the structural and conceptual differences between ICD-9-CM and ICD-10-CM, the new ICD-10-CM-specific coding guidelines, and proposed ICD-10-CM-based framework for reporting of injuries by intent and mechanism. Segmented regression analysis was used for statistical modeling of interrupted time series monthly data to evaluate the effect of the transition to ICD-10-CM on Kentucky hospitalizations' external-cause-of-injury completeness (percentage of records with principal injury diagnoses supplemented with external-cause-of-injury codes), as well as injury hospitalization trends by intent or mechanism, January 2012-December 2017. RESULTS:The segmented regression analysis showed an immediate significant drop in external-cause-of-injury completeness during the transition month, but returned to its pre-transition levels in November 2015. There was a significant immediate change in the percentage of injury hospitalizations coded for unintentional (3.34%) and undetermined intent (-?3.39%). There were immediate significant changes in the level of injury hospitalization rates due to poisoning, suffocation, struck by/against, other transportation, unspecified mechanism, and other specified not elsewhere classifiable mechanism. Significant change in slope after the transition (without immediate level change) was identified for assault, firearm, cut/pierce, and motor vehicle traffic injury rates. The observed trend changes reflected structural and conceptual features of ICD-10-CM coding (e.g., poisoning and suffocations are now captured via diagnosis codes only), new coding guidelines (e.g., requiring coding of injury intent as "accidental" if it is unknown or unspecified), and CDC proposed external-cause-of-injury code groupings by injury intent and mechanism. Researchers can replicate this methodology assessing trends in injuries or other ICD-10-CM-coded conditions using administrative billing data sets. CONCLUSIONS:The CDC 's Proposed Framework for Presenting Injury Data Using ICD-10-CM External Cause of Injury Codes provided a logical transition from the ICD-9-CM-based matrix on injury hospitalization trends by intent and mechanism. Our findings are intended to raise awareness that changes in the ICD-10-CM coding system must be understood to assure accurate interpretation of injury trends.

Dataset Information

The effect of the Iranian health transformation plan on hospitalization rate: insights from an interrupted time series analysis.

Publications

The effect of the Iranian health transformation plan on hospitalization rate: insights from an interrupted time series analysis.

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