Journal: BMC Medical Informatics and Decision Making

Article Title: Mining comorbidities of opioid use disorder from FDA adverse event reporting system and patient electronic health records

doi: 10.1186/s12911-022-01869-8

Figure Lengend Snippet: Workflow of our study. OUD: opioid use disorder; RWR: random walk with restart; HER: electronic health record

Article Snippet: We have used the Explorys EHR database and the cloud‐based Explorys Cohort Discovery informatics tools for health outcomes research [ – ] and drug discovery [ – ].

Techniques:

Journal: JMIR Public Health and Surveillance

Article Title: Factors Influencing Data Quality in Electronic Health Record Systems in 50 Health Facilities in Rwanda and the Role of Clinical Alerts: Cross-Sectional Observational Study

doi: 10.2196/49127

Figure Lengend Snippet: Completeness of all 28 variables a in the paper records and electronic health records (EHRs) and the identification of variables included in the concordance score (N=3467).

Article Snippet: They showed a wide variation in data entry per month in different HFs and noted that this was likely affected by “patients’ volume, frequency of patients’ visits (encounters), EHRs mode of use, and active use of the system during care.” Haskew et al [ ] studied the quality of data collection and the effects of clinical alerts on HIV patient care in Western Kenya before and after the cloud-based implementation of an OpenMRS EHR.

Techniques:

Journal: JMIR Public Health and Surveillance

Article Title: Factors Influencing Data Quality in Electronic Health Record Systems in 50 Health Facilities in Rwanda and the Role of Clinical Alerts: Cross-Sectional Observational Study

doi: 10.2196/49127

Figure Lengend Snippet: Matching of the variables between the paper records and the electronic health records (EHRs; N=3467). Several variables were just below the 85% threshold.

Article Snippet: They showed a wide variation in data entry per month in different HFs and noted that this was likely affected by “patients’ volume, frequency of patients’ visits (encounters), EHRs mode of use, and active use of the system during care.” Haskew et al [ ] studied the quality of data collection and the effects of clinical alerts on HIV patient care in Western Kenya before and after the cloud-based implementation of an OpenMRS EHR.

Techniques:

Journal: JMIR Public Health and Surveillance

Article Title: Factors Influencing Data Quality in Electronic Health Record Systems in 50 Health Facilities in Rwanda and the Role of Clinical Alerts: Cross-Sectional Observational Study

doi: 10.2196/49127

Figure Lengend Snippet: Correlation between concordance scores and the reported electronic health record (EHR) availability or uptime (ranging from 1 to 5 Likert scale responses of EHR users).

Article Snippet: They showed a wide variation in data entry per month in different HFs and noted that this was likely affected by “patients’ volume, frequency of patients’ visits (encounters), EHRs mode of use, and active use of the system during care.” Haskew et al [ ] studied the quality of data collection and the effects of clinical alerts on HIV patient care in Western Kenya before and after the cloud-based implementation of an OpenMRS EHR.

Techniques:

Journal: JMIR Public Health and Surveillance

Article Title: Factors Influencing Data Quality in Electronic Health Record Systems in 50 Health Facilities in Rwanda and the Role of Clinical Alerts: Cross-Sectional Observational Study

doi: 10.2196/49127

Figure Lengend Snippet: Association of electronic health record (EHR) performance and use characteristics with concordance scores.

Article Snippet: They showed a wide variation in data entry per month in different HFs and noted that this was likely affected by “patients’ volume, frequency of patients’ visits (encounters), EHRs mode of use, and active use of the system during care.” Haskew et al [ ] studied the quality of data collection and the effects of clinical alerts on HIV patient care in Western Kenya before and after the cloud-based implementation of an OpenMRS EHR.

Techniques:

Journal: JMIR Public Health and Surveillance

Article Title: Factors Influencing Data Quality in Electronic Health Record Systems in 50 Health Facilities in Rwanda and the Role of Clinical Alerts: Cross-Sectional Observational Study

doi: 10.2196/49127

Figure Lengend Snippet: Graph showing percentage data matches for viral load results between paper records and electronic health records for each of the 25 health care centers in the intervention arm, ranked by percentage of matches.

Article Snippet: They showed a wide variation in data entry per month in different HFs and noted that this was likely affected by “patients’ volume, frequency of patients’ visits (encounters), EHRs mode of use, and active use of the system during care.” Haskew et al [ ] studied the quality of data collection and the effects of clinical alerts on HIV patient care in Western Kenya before and after the cloud-based implementation of an OpenMRS EHR.

Techniques:

Journal: JMIR Public Health and Surveillance

Article Title: Factors Influencing Data Quality in Electronic Health Record Systems in 50 Health Facilities in Rwanda and the Role of Clinical Alerts: Cross-Sectional Observational Study

doi: 10.2196/49127

Figure Lengend Snippet: Graph showing percentage data matches for drug pickups between paper records and electronic health records among the same 25 intervention health facilities (HFs) as in Figure 3. The overall scores were higher than in Figure 3 but only 1 (intervention) HF score was more than the data quality threshold.

Article Snippet: They showed a wide variation in data entry per month in different HFs and noted that this was likely affected by “patients’ volume, frequency of patients’ visits (encounters), EHRs mode of use, and active use of the system during care.” Haskew et al [ ] studied the quality of data collection and the effects of clinical alerts on HIV patient care in Western Kenya before and after the cloud-based implementation of an OpenMRS EHR.

Techniques: