Study Setting
The Calgary Health Zone is one of the largest fully integrated, publicly funded, single-payer healthcare systems in Canada. It provides all medical and surgical care to residents of Calgary and surrounding communities in Southern Alberta. Contained in the region are 13 academic and community hospitals. Calgary, Alberta has a population of 1.5 million. In 2016, 70.2% of Calgarians were between the ages of 15 and 64. The average life expectancy in Calgary is 82.9 years. There are 4 large hospitals in Calgary, 3 of which were used in this study. All 3 hospitals have inpatient, intensive care and short-stay beds. The Foothills Medical Centre is an urban teaching hospital with a Level 1 trauma centre and 766 beds. It provides care to over 2 million people from southern Alberta. The Peter Lougheed Hospital is a 600-bed urban teaching hospital. It serves the northeastern part of the city, which has a high influx of immigrants and lower socioeconomic status Calgarians. Lastly, the Rockyview General Hospital is a 650-bed urban community hospital which provides medical, surgical, and psychiatric services to Calgary and Southern Alberta. The fourth hospital, South Health Campus is an urban Calgary hospital that had recently opened upon commencement of the chart review, and was therefore not included in the study. All Calgary hospitals are administered by Alberta Health Services, the single health authority in the province of Alberta.
Study Cohort
In this retrospective study, six registered nurses performed a chart review of 3,045 randomly selected inpatient charts (one chart for one patient) at three acute care hospitals in Calgary. In From January 2015 to January 2016, there were 111,148 inpatient admissions to Calgary hospitals. The nurses had varying clinical experience, both in years of practice and area of expertise. The intent of the chart review was to identify 50 of a derived set of Charlson and Elixhauser health conditions. The chart review took place between August 2016 and June 2017. Patients met inclusion criteria if they were adults (> = 18 years), had an Alberta personal health care number, and had an inpatient visit for any service outside of obstetrics between January 1st, 2015 and June 30th, 2015. A final number of 3,011 patient charts were used for this study. From the initial 3,045, 30 were in the field of obstetrics and thus were excluded. An additional 4 patient charts were missing information from the chart review and were excluded (Figure 1).
Datasets Used
The provincial electronic health record system for Alberta Health Services began in 1997, and has since been growing in complexity, as healthcare services continue to expand in electronic availability. Since 2006, the system used by Calgary healthcare providers for documentation in the inpatient electronic medical record is Sunrise Clinical ManagerTM (SCM)- EMR. However, only data collected since 2011 has been validated for research use [9]. SCM-EMR contains the electronic medical record, as well as laboratory and diagnostic imaging reporting. It includes free text (e.g., DS), structured data (laboratory data), and numerical bedside monitor trend data.
In this study, SCM-EMR was used to draw information on the 3,011 patient charts used in the chart review. The nurses were given access to both SCM-EMR and the paper copies of the chart to complete the chart review. For every patient visit, the chart reviewer firstly reviewed the patient’s electronic chart, and then reviewed the paper chart if additional information was needed for data abstraction. For the purpose of a larger study with the same research group at the University of Calgary, the data produced by SCM-EMR on these 3,011 patients was also analyzed. During the analysis, it was found that 893 of the 3,011 inpatient charts were missing a DS.
Extended Chart Review
A random 10% sample (85 charts) was drawn from the 893 missing DS charts to assess for the presence of a DS in paper format, which would not have been detected in the SCM-EMR data. Additionally, the 85 charts were assessed for information on the unit and department from which the patient was discharged, as well as the attending physician’s specialty.
Study Variables
For those with a missing electronic DS, a list of possible associated variables was collected. These variables were compiled using expertise from physicians and nurses. Age was categorized according to the parameters used by the Centre for Disease Control and Prevention. Length of stay parameters were drawn from the Discharge Abstract Database within the Canadian Institute for Health Informatics (CIHI). According to CIHI, the average length of stay for Albertan patients in 2018 was 7.8 days [10]. Therefore, the four categories used to classify length of stay were deemed appropriate by study authors. The day of the week was used to assess whether the presence of a DS was influenced by the patient’s discharge falling on a weekday or weekend. Due to decreased hospital staff during weekends, efficiency and documentation can be affected. Study authors included the day of the week variable as a measure to observe whether decreased efficiency on weekends would result in the absence of a DS. The DS is crucial for interprofessional communication, particularly when a patient is being transferred from one facility to another. Therefore, disposition was included as a variable to assess whether an interfacility transfer affected the presence of a DS in the EMR. Finally, the 17 Charlson comorbidities are a common set of secondary diseases that can accompany the primary disease of a patient [11]. The Charlson Comorbidity Index (CCI) is used to weigh the comorbidities, and to perform risk adjustment in outcomes research from administration databases (such as inpatient mortality). Both the CCI and the individual Charlson comorbidities were included in the analysis for associated variables.
Statistical Analysis
Study variables were reported as numbers and percentages for categorical variables. Chi-Square test or Fisher’s exact test, when appropriate, were performed to assess the differences of associated variables between missing DS and non-missing DS charts. Logistic regression was used to identify the variables that were independently associated with missing DS. Logistic regression results were reported using odds ratios (ORs) and 95% confidence intervals (95% CIs). A p-value of <0.05 was used for statistical significance. Analyses were performed using Stat 14.0 (Stat Corp, College Station, TX, USA).