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The time-dependent study of comorbidities provides insight into disease progression and trajectory. We hypothesize that understanding longitudinal disease characteristics can lead to more timely intervention and improve clinical outcomes. As a first step, we developed an efficient and easy-to-install toolkit, the Time-based Elixhauser Comorbidity Index (TECI), which pre-calculates time-based Elixhauser comorbidities and can be extended to common data models (CDMs).

A Structured Query Language (SQL)-based toolkit, TECI, was built to pre-calculate time-specific Elixhauser comorbidity indices using data from a clinical data repository (CDR). Then it was extended to the Informatics for Integrating Biology and the Bedside (I2B2) and Observational Medical Outcomes Partnership (OMOP) CDMs.

At the University of Arkansas for Medical Sciences (UAMS), the TECI toolkit was successfully installed to compute the indices from CDR data, and the scores were integrated into the I2B2 and OMOP CDMs. Comorbidity scores calculated by TECI were validated against scores available in the 2015 quarter 1-3 Nationwide Readmissions Database (NRD) and scores calculated using the comorbidities using a previously validated algorithm on the 2015 quarter 4 NRD. Furthermore, TECI identified 18,846 UAMS patients that had changes in comorbidity scores over time (year 2013 to 2019). Comorbidities for a random sample of patients were independently reviewed, and in all cases, the results were found to be 100% accurate.

TECI facilitates the study of comorbidities within a time-dependent context, allowing better understanding of disease associations and trajectories, which has the potential to improve clinical outcomes.

TECI facilitates the study of comorbidities within a time-dependent context, allowing better understanding of disease associations and trajectories, which has the potential to improve clinical outcomes.

This study assessed the technical feasibility and aimed to determine the factors influencing intention to use Electronic Medical Records (EMRs) at Marie Stopes International, Myanmar (MSI-M).

A cross-sectional survey was conducted among 112 participants who were working at the clinics and head office of MSI-M. Demographic information, type of office, technical feasibility, information communication technology knowledge, computer usage, and user acceptance towards the proposed system were obtained from the participants.

The results indicated low health information technology usage and network availability at MSI-M clinics. Positive perception of EMRs was found among the staff members of MSI-M, which was reflected by positive responses regarding perceived usefulness (average score of 4.15), perceived ease of use (average score of 4.03), and intention to use (average score of 4.10) on a 5-point Likert scale. Statistically, staff from the head office expressed less desire to implement an EMR system (odds ratio = 0.07; 95% confidence interval, 0.01-0.97), especially when they do not perceive the usefulness of the system (odds ratio = 5.05; 95% confidence interval, 2.39-10.69).

Since health information technology usage and network availability were low in MSI-M, it is important to strengthen the information and communication technology infrastructure and introduce a policy for capacity building at MSI-M. Adequate training and strong leadership support are recommended for the successful initial implementation and sustainability of an EMR system at MSI-M.

Since health information technology usage and network availability were low in MSI-M, it is important to strengthen the information and communication technology infrastructure and introduce a policy for capacity building at MSI-M. Adequate training and strong leadership support are recommended for the successful initial implementation and sustainability of an EMR system at MSI-M.

Considering the rising menace of coronavirus disease 2019 (COVID-19), it is essential to explore the methods and resources that might predict the case numbers expected and identify the locations of outbreaks. selleck Hence, we have done the following study to explore the potential use of Google Trends (GT) in predicting the COVID-19 outbreak in India.

The Google search terms used for the analysis were “coronavirus”, “COVID”, “COVID 19”, “corona”, and “virus”. GTs for these terms in Google Web, News, and YouTube, and the data on COVID-19 case numbers were obtained. Spearman correlation and lag correlation were used to determine the correlation between COVID-19 cases and the Google search terms.

“Coronavirus” and “corona” were the terms most commonly used by Internet surfers in India. Correlation for the GTs of the search terms “coronavirus” and “corona” was high (r > 0.7) with the daily cumulative and new COVID-19 cases for a lag period ranging from 9 to 21 days. The maximum lag period for predicting COVID-19 cases was found to be with the News search for the term “coronavirus”, with 21 days, i.e., the search volume for “coronavirus” peaked 21 days before the peak number of cases reported by the disease surveillance system.

Our study revealed that GTs may predict outbreaks of COVID-19, 2 to 3 weeks earlier than the routine disease surveillance, in India. Google search data may be considered as a supplementary tool in COVID-19 monitoring and planning in India.

Our study revealed that GTs may predict outbreaks of COVID-19, 2 to 3 weeks earlier than the routine disease surveillance, in India. Google search data may be considered as a supplementary tool in COVID-19 monitoring and planning in India.

To find out the factors influencing discharge process turnaround time (TAT) and to accurately predict the discharge process TAT.

The discharge process of cardiology department inpatients in a tertiary care hospital was mapped over a month. The likely factors influencing discharge TAT were tested for significance by ANOVA. Multiple linear regression (MLR) was used to predict the TAT. The sample was divided into testing and training sets for regression. A model was generated using the training set and compared with the testing set for accuracy.

After a process map was plotted, the significant factors influencing the TAT were identified to be the treating doctor, and pending evaluations on the day of discharge. The MLR model was developed with Python libraries based on the two factors identified. The model predicted the discharge TAT with a 69% R2 value and 32.4 minutes (standard error) on the testing set and a 77.3% R2 value and 26.7 minutes (standard error) on the overall sample.

This study was an initiation to find out factors influencing discharge TAT and how those factors can be used to predict discharge in the hospital of interest.