Preliminary physician and pharmacist survey of the National Health Insurance PharmaCloud system in Taiwan

Tseng, Yu Ting; Chang, Elizabeth H.; Kuo, Li Jen; Shen, Wan Chen; Bai, Kuan Jen; Wang, Chih Chi; Chen, Hsiang-Yin

doi:10.1016/j.cmpb.2017.07.004

Cited by 11 publications

(24 citation statements)

References 25 publications

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“…Our DNN model, which estimates stroke risk by analyzing only the EHR, shows a competitive performance to the QRISK3 score and superior performance to other risk scores. While cloud-based health care systems, such as PharmaCloud[55], have been successfully implemented on the National Health Insurance system in Taiwan since 2015, physicians now have real-time access to patients' medication and disease diagnosis records in any clinic or hospital in Taiwan. In addition to avoiding duplicate prescriptions, we may use these EHRs for disease risk assessment by using this DNN model in the future.…”

Section: Discussionmentioning

confidence: 99%

Development of an intelligent decision support system for ischemic stroke risk assessment in a population-based electronic health record database

Hung¹,

Lin²,

Lan³

et al. 2019

PLoS ONE

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BackgroundIntelligent decision support systems (IDSS) have been applied to tasks of disease management. Deep neural networks (DNNs) are artificial intelligent techniques to achieve high modeling power. The application of DNNs to large-scale data for estimating stroke risk needs to be assessed and validated. This study aims to apply a DNN for deriving a stroke predictive model using a big electronic health record database.Methods and resultsThe Taiwan National Health Insurance Research Database was used to conduct a retrospective population-based study. The database was divided into one development dataset for model training (~70% of total patients for training and ~10% for parameter tuning) and two testing datasets (each ~10%). A total of 11,192,916 claim records from 840,487 patients were used. The primary outcome was defined as any ischemic stroke in inpatient records within 3 years after study enrollment. The DNN was evaluated using the area under the receiver operating characteristic curve (AUC or c-statistic). The development dataset included 672,214 patients (a total of 8,952,000 records) of whom 2,060 patients had stroke events. The mean age of the population was 35.5±20.2 years, with 48.5% men. The model achieved AUC values of 0.920 (95% confidence interval [CI], 0.908–0.932) in testing dataset 1 and 0.925 (95% CI, 0.914–0.937) in testing dataset 2. Under a high sensitivity operating point, the sensitivity and specificity were 92.5% and 79.8% for testing dataset 1; 91.8% and 79.9% for testing dataset 2. Under a high specificity operating point, the sensitivity and specificity were 80.3% and 87.5% for testing dataset 1; 83.7% and 87.5% for testing dataset 2. The DNN model maintained high predictability 5 years after being developed. The model achieved similar performance to other clinical risk assessment scores.ConclusionsUsing a DNN algorithm on this large electronic health record database is capable of obtaining a high performing model for assessment of ischemic stroke risk. Further research is needed to determine whether such a DNN-based IDSS could lead to an improvement in clinical practice.

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Section: Discussionmentioning

confidence: 99%

Development of an intelligent decision support system for ischemic stroke risk assessment in a population-based electronic health record database

Hung¹,

Lin²,

Lan³

et al. 2019

PLoS ONE

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“…A higher score corresponds to a more positive outcome towards satisfaction, more positive attitudes and more positive intentions to use the PharmaCloud system. The results showed that satisfaction score with system quality exhibited 3.42 ± 0.44 for pharmacists and 2.36 ± 0.79 for physicians . The PharmaCloud database only could provide patients' medication record from NHI‐contracted medical institutions.…”

Section: Discussionmentioning

confidence: 99%

“…Although the study period was different between pre and post stages, it could be considered no significant difference based on the similar patient characteristics ( Table 1) showed that satisfaction score with system quality exhibited 3.42 ± 0.44 for pharmacists and 2.36 ± 0.79 for physicians. 6 The PharmaCloud database only could provide patients' medication record from NHI-contracted medical institutions. We only would obtain a BPMH compared with a gold standard medication list after talking with patients and/or caregivers.…”

Section: Discussionmentioning

confidence: 99%

“…Patients sign the PharmaCloud consent form allowing their physicians to access their medication history in the PharmaCloud. After we have obtained patients' informed consent, NHI can authorize daily download of real‐time medication records from the NHI PharmaCloud system into the hospital's database . If patients have cognitive impairments (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…After we have obtained patients' informed consent, NHI can authorize daily download of real-time medication records from the NHI PharmaCloud system into the hospital's database. 6 If patients have cognitive impairments (e.g. children, dementia etc.…”

Section: Introductionmentioning

confidence: 99%

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The impact of a medication reconciliation programme at geriatric hospital admission: A pre‐/postintervention study

Hung

Chen

et al. 2019

Brit J Clinical Pharma

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Aims The aim of this study was to improve medication reconciliation and reduce the occurrence of duplicate prescriptions by pharmacists and physicians within 72 hours of hospital admission using an intelligent prescription system combined with the National Health Insurance PharmaCloud system to integrate the database with the medical institution computerized physician order entry (CPOE) system. Methods This 2‐year intervention study was implemented in the geriatric ward of a hospital in Taiwan. We developed an integrated CPOE system linked with the PharmaCloud database and established an electronic platform for coordinated communication with all healthcare professionals. Patients provided written informed consent to access their PharmaCloud records. We compared the intervention effectiveness within 72 hours of admission for improvement in pharmacist medication reconciliation, increased at‐home medications documentation and decreased costs from duplicated at‐home prescriptions. Results The medication reconciliation rate within 72 hours of admission increased from 44.0% preintervention to 86.8% postintervention (relative risk = 1.97, 95% confidence interval [CI]: 1.69–2.31; P < .001). The monthly average of patients who brought and took home medications documented in the CPOE system during hospitalization increased by 7.54 (95% CI 5.58–20.49, P = .22). The monthly average of home medications documented increased by 102.52 (95% CI 38.44–166.60; P = .01). Savings on the monthly average prescription expenditures of at‐home medication increased by US$ 2,795.52 (95% CI US$1310.41–4280.63; P < .01). Conclusion Integrating medication data from PharmaCloud to the hospital's medical chart system improved pharmacist medication reconciliation, which decreased duplicated medications and reduced in‐hospital medication costs.

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The impact of a medication record sharing program among diabetes patients under a single-payer system: The role of inquiry rate

Lin

Cheng

2018

International Journal of Medical Informatics

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Preliminary physician and pharmacist survey of the National Health Insurance PharmaCloud system in Taiwan

Cited by 11 publications

References 25 publications

Development of an intelligent decision support system for ischemic stroke risk assessment in a population-based electronic health record database

Development of an intelligent decision support system for ischemic stroke risk assessment in a population-based electronic health record database

The impact of a medication reconciliation programme at geriatric hospital admission: A pre‐/postintervention study

The impact of a medication record sharing program among diabetes patients under a single-payer system: The role of inquiry rate

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