Differences in Spontaneous Intracerebral Hemorrhage Cases between Urban and Rural Regions of Taiwan: Big Data Analytics of Government Open Data

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Abstract

This study evaluated the differences in spontaneous intracerebral hemorrhage (sICH) between rural and urban areas of Taiwan with big data analysis. We used big data analytics and visualization tools to examine government open data, which included the residents’ health medical administrative data, economic status, educational status, and relevant information. The study subjects included sICH patients of Taipei region (29,741 cases) and Eastern Taiwan (4565 cases). The incidence of sICH per 100,000 population per year in Eastern Taiwan (71.3 cases) was significantly higher than that of the Taipei region (42.3 cases). The mean coverage area per hospital in Eastern Taiwan (452.4 km ²) was significantly larger than the Taipei region (24 km ²). The residents educational level in the Taipei region was significantly higher than that in Eastern Taiwan. The mean hospital length of stay in the Taipei region (17.9 days) was significantly greater than that in Eastern Taiwan (16.3 days) ( p < 0.001). There were no significant differences in other medical profiles between two areas. Distance and educational barriers were two possible reasons for the higher incidence of sICH in the rural area of Eastern Taiwan. Further studies are necessary in order to understand these phenomena in greater depth.

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Charlson comorbidity index derived from chart review or administrative data: agreement and prediction of mortality in intensive care patients

Knut Stavem, Henrik Hoel, Stein Arve Skjaker … (2017)

Purpose This study compared the Charlson comorbidity index (CCI) information derived from chart review and administrative systems to assess the completeness and agreement between scores, evaluate the capacity to predict 30-day and 1-year mortality in intensive care unit (ICU) patients, and compare the predictive capacity with that of the Simplified Acute Physiology Score (SAPS) II model. Patients and methods Using data from 959 patients admitted to a general ICU in a Norwegian university hospital from 2007 to 2009, we compared the CCI score derived from chart review and administrative systems. Agreement was assessed using % agreement, kappa, and weighted kappa. The capacity to predict 30-day and 1-year mortality was assessed using logistic regression, model discrimination with the c-statistic, and calibration with a goodness-of-fit statistic. Results The CCI was complete (n=959) when calculated from chart review, but less complete from administrative data (n=839). Agreement was good, with a weighted kappa of 0.667 (95% confidence interval: 0.596–0.714). The c-statistics for categorized CCI scores from charts and administrative data were similar in the model that included age, sex, and type of admission: 0.755 and 0.743 for 30-day mortality, respectively, and 0.783 and 0.775, respectively, for 1-year mortality. Goodness-of-fit statistics supported the model fit. Conclusion The CCI scores from chart review and administrative data showed good agreement and predicted 30-day and 1-year mortality in ICU patients. CCI combined with age, sex, and type of admission predicted mortality almost as well as the physiology-based SAPS II.

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Impact of an electronic clinical decision support system on workflow in antenatal care: the QUALMAT eCDSS in rural health care facilities in Ghana and Tanzania

Nathan Mensah, Felix Sukums, Timothy Awine … (2015)

Background The implementation of new technology can interrupt established workflows in health care settings. The Quality of Maternal Care (QUALMAT) project has introduced an electronic clinical decision support system (eCDSS) for antenatal care (ANC) and delivery in rural primary health care facilities in Africa. Objective This study was carried out to investigate the influence of the QUALMAT eCDSS on the workflow of health care workers in rural primary health care facilities in Ghana and Tanzania. Design A direct observation, time-and-motion study on ANC processes was conducted using a structured data sheet with predefined major task categories. The duration and sequence of tasks performed during ANC visits were observed, and changes after the implementation of the eCDSS were analyzed. Results In 24 QUALMAT study sites, 214 observations of ANC visits (144 in Ghana, 70 in Tanzania) were carried out at baseline and 148 observations (104 in Ghana, 44 in Tanzania) after the software was implemented in 12 of those sites. The median time spent combined for all centers in both countries to provide ANC at baseline was 6.5 min [interquartile range (IQR) =4.0–10.6]. Although the time spent on ANC increased in Tanzania and Ghana after the eCDSS implementation as compared to baseline, overall there was no significant increase in time used for ANC activities (0.51 min, p=0.06 in Ghana; and 0.54 min, p=0.26 in Tanzania) as compared to the control sites without the eCDSS. The percentage of medical history taking in women who had subsequent examinations increased after eCDSS implementation from 58.2% (39/67) to 95.3% (61/64) p<0.001 in Ghana but not in Tanzania [from 65.4% (17/26) to 71.4% (15/21) p=0.70]. Conclusions The QUALMAT eCDSS does not increase the time needed for ANC but partly streamlined workflow at sites in Ghana, showing the potential of such a system to influence quality of care positively.

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Using an integrated COC index and multilevel measurements to verify the care outcome of patients with multiple chronic conditions

Chien-Lung Chan, Huey-Jen You, Hsin-Tsung Huang … (2012)

Background The increasing prevalence of multiple chronic conditions has accentuated the importance of coordinating and integrating health care services. Patients with better continuity of care (COC) have a lower utilization rate of emergency department (ED) services, lower hospitalization and better care outcomes. Previous COC studies have focused on the care outcome of patients with a single chronic condition or that of physician-patient relationships; few studies have investigated the care outcome of patients with multiple chronic conditions. Using multi-chronic patients as subjects, this study proposes an integrated continuity of care (ICOC) index to verify the association between COC and care outcomes for two scopes of chronic conditions, at physician and medical facility levels. Methods This study used a dataset of 280,840 subjects, obtained from the Longitudinal Health Insurance Database (LHID 2005), compiled by the National Health Research Institutes, of the National Health Insurance Bureau of Taiwan. Principal Component Analysis (PCA) was used to integrate the indices of density, dispersion and sequence into ICOC to measure COC outcomes - the utilization rate of ED services and hospitalization. A Generalized Estimating Equations model was used to verify the care outcomes. Results We discovered that the higher the COC at medical facility level, the lower the utilization rate of ED services and hospitalization for patients; by contrast, the higher the COC at physician level, the higher the utilization rate of ED services (odds ratio > 1; Exp(β) = 2.116) and hospitalization (odds ratio > 1; Exp(β) = 1.688). When only those patients with major chronic conditions with the highest number of medical visits were considered, it was found that the higher the COC at both medical facility and physician levels, the lower the utilization rate of ED services and hospitalization. Conclusions The study shows that ICOC is more stable than single indices and it can be widely used to measure the care outcomes of different chronic conditions to accumulate empirical evidence. Concentrated care of multi-chronic patients by a single physician often results in unsatisfactory care outcomes. This highlights the need for referral mechanisms and integration of specialties inside or outside medical facilities, in order to optimize patient-centered care.

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Author and article information

Journal

Journal ID (nlm-ta): Int J Environ Res Public Health

Journal ID (iso-abbrev): Int J Environ Res Public Health

Journal ID (publisher-id): ijerph

Title: International Journal of Environmental Research and Public Health

Publisher: MDPI

ISSN (Print): 1661-7827

ISSN (Electronic): 1660-4601

Publication date (Electronic): 10 December 2017

Publication date (Print): December 2017

Volume: 14

Issue: 12

Electronic Location Identifier: 1548

Affiliations

[1 ]Department of Information Management, Yuan Ze University, Tao-Yuan 320, Taiwan; ting.ns@ 123456gmail.com

[2 ]Department of Neurosurgery, Taipei Hospital, New Taipei City 242, Taiwan

[3 ]Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Tao-Yuan 320, Taiwan; tinin@ 123456saturn.yzu.edu.tw

[4 ]Department of Computer Science and Engineering, Yuan Ze University, Tao-Yuan 320, Taiwan; krlai@ 123456saturn.yzu.edu.tw (K.R.L.); pan@ 12345651donate.com (R.-H.P.); miranda84315@ 123456gmail.com (K.-H.W.); w2301231@ 123456gmail.com (J.-M.C.)

[5 ]Department of Information Management, Tunghai University, Taichung 407, Taiwan

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[* ]Correspondence: clchan@ 123456im.yzu.edu.tw ; Tel.: +886-3-4638800-2605

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Ren-Hao Pan https://orcid.org/0000-0003-1296-7465

Chien-Lung Chan https://orcid.org/0000-0002-7486-7075

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Publisher ID: ijerph-14-01548

DOI: 10.3390/ijerph14121548

PMC ID: 5750966

PubMed ID: 29232864

SO-VID: 58bbbb69-13b6-4cdd-b1c6-ee156e7da552

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Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

Differences in Spontaneous Intracerebral Hemorrhage Cases between Urban and Rural Regions of Taiwan: Big Data Analytics of Government Open Data

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Most cited references 18

Charlson comorbidity index derived from chart review or administrative data: agreement and prediction of mortality in intensive care patients

Impact of an electronic clinical decision support system on workflow in antenatal care: the QUALMAT eCDSS in rural health care facilities in Ghana and Tanzania

Using an integrated COC index and multilevel measurements to verify the care outcome of patients with multiple chronic conditions

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