The effect of electronic medical record application on the length of stay in a Chinese general hospital: a department- and disease-focused interrupted time-series study

Effects of medical consortium policy on health services: an interrupted time-series analysis in Sanming, China

Objectives

China has implemented reforms to enhance the operational efficiency of three-level medical services through medical consortiums (MCs). This study evaluated the impact of MCs reform on health services in Sanming, China.

Methods

An interrupted time-series analysis (ITSA) was conducted to assess the impact of MCs on changes in health service levels and trends across the overall situation of MCs and different institutional types within MCs, including county hospitals and grassroots medical institutions. The evaluation focused on various indicators such as outpatient and emergency visits, inpatients, average length of stay, occupancy rate of hospital beds, and hospital bed turnover times. Monthly data were collected from April 2015 to June 2019 through reports on the Sanming Municipal Health Commission website and the Sanming public hospital management monitoring platform.

Results

After the intervention of MCs reform, a significant increase was observed in the total number of inpatients (β3 = 174.28, p < 0.05). However, no statistically significant change was observed in the total number of outpatient and emergency visits (β3 = 155.82, p = 0.91). Additionally, the implementation of MCs reform led to an amplification in service volumes provided by county hospitals, with significant increases in the number of outpatient and emergency visits (β3 = 1376.54, p < 0.05) and an upward trend in the number of inpatients (β3 = 98.87, p < 0.01). However, no significant changes were observed under the MCs policy for grassroots medical institutions regarding the number of outpatient and emergency visits (β3 = -1220.72, p = 0.22) and number of inpatients (β3 = 75.42, p = 0.09).

Conclusion

The Sanming MCs reform has achieved some progress in augmenting service volumes. Nevertheless, it has not led to an increase in service volumes at the grassroots medical institutions. There persists an insufficiency in the efficiency of services and a need for further improvement in primary healthcare. To address these concerns, it is imperative for county hospitals to offer targeted assistance that can enhance motivation among grassroots medical institutions. Besides the MCs should explore initiatives, including improved management of medical equipment, allocation of funding, and personnel resources.

Evaluating the Patient Boarding during Omicron Surge in Hong Kong: Time Series Analysis

The fifth wave of COVID-19 outbreaks in Hong Kong (HK) from January to March 2022 has the highest confirmed cases and deaths compared with previous waves. Severe hospital boarding (to inpatient wards) was noted in various Emergency Departments (EDs). Our objective is to identify factors associated with hospital boarding during Omicron surge in HK. We conducted a retrospective cohort study including all ED visits and inpatient (IP) ward admissions from January 1st to March 31st, 2022. Vector Autoregression model evaluated the effects of a single variable on the targeted hospital boarding variables. Admissions from elderly homes with 6 lag days held the highest positive value of statistical significance (t-stat = 2.827, P < .05) caused prolonged admission waiting time, while medical patients with 4 lag days had the highest statistical significance (t-stat = 2.530, P < .05) caused an increased number of boarding patients. Within one week after impulses, medical occupancy's influence on the waiting time varied from 0.289 on the 1st day to -0.315 on the 7th day. While occupancy of medical wards always positively affected blocked number of patients, and its response was maximized at 0.309 on the 2nd day. Number of confirmed COVID-19 cases was not the sole significant contributor, while occupancy of medical wards was still a critical factor associated with patient boarding. Increasing ward capacity and controlling occupancy were suggested during the outbreak. Moreover, streamlining elderly patients in ED could be an approach to relieve pressure on the healthcare system.

Analysis of the Effects of Electronic Medical Records and a Payment Scheme on the Length of Hospital Stay

Objectives: This study analyzed the effects of computerization of medical information systems and a hospital payment scheme on medical care outcomes. Specifically, we examined the effects of Electronic Medical Records (EMRs) and a diagnosis procedure combination/per-diem payment scheme (DPC/PDPS) on the average length of hospital stay (ALOS).Methods: Post-intervention changes in the monthly ALOS were measured using an interrupted time-series analysis.Results: The level changes observed in the monthly ALOS immediately post-DPC/PDPS were –1.942 (95% confidence interval [CI], –2.856 to –1.028), –1.885 (95% CI, –3.176 to –0.593), –1.581 (95% CI, –3.081 to –0.082) and –2.461 (95% CI, –3.817 to 1.105) days in all ages, <50, 50–64, and ≥65 years, respectively. During the post-DPC/PDPS period, trends of 0.107 (95% CI, 0.069 to 0.144), 0.048 (95% CI, –0.006 to 0.101), 0.183 (95% CI, 0.122 to 0.245) and 0.110 (95% CI, 0.054 to 0.167) days/month, respectively, were observed. During the post-EMR period, trends of –0.053 (95% CI, –0.080 to –0.027), –0.093 (95% CI, –0.135 to –0.052), and –0.049 (95% CI, –0.087 to –0.012) days/month were seen for all ages, 50–64 and ≥65 years, respectively.Conclusions: The increasing post-DPC/PDPS trends offset the decline in ALOS observed immediately post-DPC/PDPS, and the observed ALOS was longer than the counterfactual at the end of the DPC/PDPS study periods. Conversely, due to the downward trend seen after EMR introduction, the actual ALOS at the end of the EMR study period was shorter than the counterfactual, suggesting that EMRs might be more effective than the DPC/PDPS in sustainably reducing the LOS.

Use of interrupted time series methods in the evaluation of health system quality improvement interventions: a methodological systematic review

BACKGROUND

When randomisation is not possible, interrupted time series (ITS) design has increasingly been advocated as a more robust design to evaluating health system quality improvement (QI) interventions given its ability to control for common biases in healthcare QI. However, there is a potential risk of producing misleading results when this rather robust design is not used appropriately. We performed a methodological systematic review of the literature to investigate the extent to which the use of ITS has followed best practice standards and recommendations in the evaluation of QI interventions.

METHODS

We searched multiple databases from inception to June 2018 to identify QI intervention studies that were evaluated using ITS. There was no restriction on date, language and participants. Data were synthesised narratively using appropriate descriptive statistics. The risk of bias for ITS studies was assessed using the Cochrane Effective Practice and Organisation of Care standard criteria. The systematic review protocol was registered in PROSPERO (registration number: CRD42018094427).

RESULTS

Of 4061 potential studies and 2028 unique records screened for inclusion, 120 eligible studies assessed eight QI strategies and were from 25 countries. Most studies were published since 2010 (86.7%), reported data using monthly interval (71.4%), used ITS without a control (81%) and modelled data using segmented regression (62.5%). Autocorrelation was considered in 55% of studies, seasonality in 20.8% and non-stationarity in 8.3%. Only 49.2% of studies specified the ITS impact model. The risk of bias was high or very high in 72.5% of included studies and did not change significantly over time.

CONCLUSIONS

The use of ITS in the evaluation of health system QI interventions has increased considerably over the past decade. However, variations in methodological considerations and reporting of ITS in QI remain a concern, warranting a need to develop and reinforce formal reporting guidelines to improve its application in the evaluation of health system QI interventions.

The role of medical data in efficient patient care delivery: a review

Background

Implementing accurate data management systems ensure safe and efficient transfer of confidential health care data. However, health care professionals overlooked their important tasks of medical data processing. Hence, using high-quality electronic health record (EHR) applications in health care is important to minimize medical errors. Therefore, this review tries to indicate the roles of EHR in advancing quality health care service provisions.

Methods

The keywords identified were EHR, EMR, medical data processing, medical data retention, medical data destruction, health care, and patient care, and a few related terms with different combinations. PubMed (National Library of Medicine), Google Scholar, and Google search engine were used to search for articles from those databases. Searching was done using boolean words “AND”, “OR”, and “NOT” using all [All fields] and [MeSH Terms] searching strategies.

Results

Articles were screened using the title, checked by their abstract, and the remaining related full-text materials were included or excluded by two individuals deciding its eligibility. Finally, 73 materials issued from 2013–2018 were used for qualitatively synthesizing and reconciling the idea to produce this review article.

Conclusion

Poor medical data processing systems are the key reasons for medical errors. Employing standardized data management systems reduce errors and associated sufferings. Therefore, using electronic tools in the health care institution ensures safe and efficient data management. Therefore, it is important to establish appropriate medical data management systems for efficient health care delivery.

Time series analysis of temporal trends in the pertussis incidence in Mainland China from 2005 to 2016

Short-term forecast of pertussis incidence is helpful for advanced warning and planning resource needs for future epidemics. By utilizing the Auto-Regressive Integrated Moving Average (ARIMA) model and Exponential Smoothing (ETS) model as alterative models with R software, this paper analyzed data from Chinese Center for Disease Control and Prevention (China CDC) between January 2005 and June 2016. The ARIMA (0,1,0)(1,1,1)₁₂ model (AICc = 1342.2 BIC = 1350.3) was selected as the best performing ARIMA model and the ETS (M,N,M) model (AICc = 1678.6, BIC = 1715.4) was selected as the best performing ETS model, and the ETS (M,N,M) model with the minimum RMSE was finally selected for in-sample-simulation and out-of-sample forecasting. Descriptive statistics showed that the reported number of pertussis cases by China CDC increased by 66.20% from 2005 (4058 cases) to 2015 (6744 cases). According to Hodrick-Prescott filter, there was an apparent cyclicity and seasonality in the pertussis reports. In out of sample forecasting, the model forecasted a relatively high incidence cases in 2016, which predicates an increasing risk of ongoing pertussis resurgence in the near future. In this regard, the ETS model would be a useful tool in simulating and forecasting the incidence of pertussis, and helping decision makers to take efficient decisions based on the advanced warning of disease incidence.

Review of information technology for surgical patient care

BACKGROUND

Electronic health records (EHRs), computerized provider order entry (CPOE), and patient portals have experienced increased adoption by health care systems. The objective of this study was to review evidence regarding the impact of such health information technologies (HIT) on surgical practice.

MATERIALS AND METHODS

A search of Medline, EMBASE, CINAHL, and the Cochrane Library was performed to identify data-driven, nonsurvey studies about the effects of HIT on surgical care. Domain experts were queried for relevant articles. Two authors independently reviewed abstracts for inclusion criteria and analyzed full text of eligible articles.

RESULTS

A total of 2890 citations were identified. Of them, 32 observational studies and two randomized controlled trials met eligibility criteria. EHR or CPOE improved appropriate antibiotic administration for surgical procedures in 13 comparative observational studies. Five comparative observational studies indicated that electronically generated operative notes had increased accuracy, completeness, and availability in the medical record. The Internet as an information resource about surgical procedures was generally inadequate. Surgical patients and providers demonstrated rapid adoption of patient portals, with increasing proportions of online versus inperson outpatient surgical encounters.

CONCLUSIONS

The overall quality of evidence about the effects of HIT in surgical practice was low. Current data suggest an improvement in appropriate perioperative antibiotic administration and accuracy of operative reports from CPOE and EHR applications. Online consumer health educational resources and patient portals are popular among patients and families, but their impact has not been studied well in surgical populations. With increasing adoption of HIT, further research is needed to optimize the efficacy of such tools in surgical care.

An electronic medical record system with treatment recommendations based on patient similarity

As the core of health information technology (HIT), electronic medical record (EMR) systems have been changing to meet health care demands. To construct a new-generation EMR system framework with the capability of self-learning and real-time feedback, thus adding intelligence to the EMR system itself, this paper proposed a novel EMR system framework by constructing a direct pathway between the EMR workflow and EMR data. A prototype of this framework was implemented based on patient similarity learning. Patient diagnoses, demographic data, vital signs and structured lab test results were considered for similarity calculations. Real hospitalization data from 12,818 patients were substituted, and Precision @ Position measurements were used to validate self-learning performance. Our EMR system changed the way in which orders are placed by establishing recommendation order menu and shortcut applications. Two learning modes (EASY MODE and COMPLEX MODE) were provided, and the precision values @ position 5 of both modes were 0.7458 and 0.8792, respectively. The precision performance of COMPLEX MODE was better than that of EASY MODE (tested using a paired Wilcoxon-Mann-Whitney test, p < 0.001). Applying the proposed framework, the EMR data value was directly demonstrated in the clinical workflow, and intelligence was added to the EMR system, which could improve system usability, reliability and the physician's work efficiency. This self-learning mechanism is based on dynamic learning models and is not limited to a specific disease or clinical scenario, thus decreasing maintenance costs in real world applications and increasing its adaptability.