The lives of persons living with mental illness are affected by psychological, biological, social, economic, and environmental factors over the life course. It is therefore unlikely that simple preventive strategies, clinical treatments, therapeutic interventions, or policy options will succeed as singular solutions for the challenges of mental illness. Persons living with mental illness receive services and supports in multiple settings across the health care continuum that are often fragmented, uncoordinated, and inadequately responsive. Appropriate assessment is an important tool that health systems must deploy to respond to the strengths, preferences, and needs of persons with mental illness. However, standard approaches are often focused on measurement of psychiatric symptoms without taking a broader perspective to address issues like growth, development, and aging; physical health and disability; social relationships; economic resources; housing; substance use; involvement with criminal justice; stigma; and recovery. Using conglomerations of instruments to cover more domains is impractical, inconsistent, and incomplete while posing considerable assessment burden. interRAI mental health instruments were developed by a network of over 100 researchers, clinicians, and policy experts from over 35 nations. This includes assessment systems for adults in inpatient psychiatry, community mental health, emergency departments, mobile crisis teams, and long-term care settings, as well as a screening system for police officers. A similar set of instruments is available for child/youth mental health. The instruments form an integrated mental health information system because they share a common assessment language, conceptual basis, clinical emphasis, data collection approach, data elements, and care planning protocols. The key applications of these instruments include care planning, outcome measurement, quality improvement, and resource allocation. The composition of these instruments and psychometric properties are reviewed, and examples related to homeless are used to illustrate the various applications of these assessment systems.

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The interRAI Suite of Mental Health Assessment Instruments: An Integrated System for the Continuum of Care.

Real world evidence in cardiovascular medicine: ensuring data validity in electronic health record-based studies.

The increased use of electronic medical records (EMRs) in Canadian primary health care practice has resulted in an expansion of the availability of EMR data. Potential users of these data need to understand their quality in relation to the uses to which they are applied. Herein, we propose a basic model for assessing primary health care EMR data quality, comprising a set of data quality measures within four domains. We describe the process of developing and testing this set of measures, share the results of applying these measures in three EMR-derived datasets, and discuss what this reveals about the measures and EMR data quality. The model is offered as a starting point from which data users can refine their own approach, based on their own needs. Using an iterative process, measures of EMR data quality were created within four domains: comparability; completeness; correctness; and currency. We used a series of process steps to develop the measures. The measures were then operationalized, and tested within three datasets created from different EMR software products. A set of eleven final measures were created. We were not able to calculate results for several measures in one dataset because of the way the data were collected in that specific EMR. Overall, we found variability in the results of testing the measures (e.g. sensitivity values were highest for diabetes, and lowest for obesity), among datasets (e.g. recording of height), and by patient age and sex (e.g. recording of blood pressure, height and weight). This paper proposes a basic model for assessing primary health care EMR data quality. We developed and tested multiple measures of data quality, within four domains, in three different EMR-derived primary health care datasets. The results of testing these measures indicated that not all measures could be utilized in all datasets, and illustrated variability in data quality. This is one step forward in creating a standard set of measures of data quality. Nonetheless, each project has unique challenges, and therefore requires its own data quality assessment before proceeding.

/pdf/a-basic-model-for-assessing-primary-health-care-electronic-lospckoq01.pdf

A basic model for assessing primary health care electronic medical record data quality

Consistent medical care among people living with HIV is essential for both individual and public health. HIV-positive individuals who are 'retained in care' are more likely to be prescribed antiretroviral medication and achieve HIV viral suppression, effectively eliminating the risk of transmitting HIV to others. However, in the United States, less than half of HIV-positive individuals are retained in care. Interventions to improve retention in care are resource intensive, and there is currently no systematic way to identify patients at risk for falling out of care who would benefit from these interventions. We developed a machine learning model to identify patients at risk for dropping out of care in an urban HIV care clinic using electronic medical records and geospatial data. The machine learning model has a mean positive predictive value of 34.6% [SD: 0.15] for flagging the top 10% highest risk patients as needing interventions, performing better than the previous state-of-the-art logistic regression model (PPV of 17% [SD: 0.06]) and the baseline rate of 11.1% [SD: 0.02]. Machine learning methods can improve the prediction ability in HIV care clinics to proactively identify patients at risk for not returning to medical care.

Predictive Analytics for Retention in Care in an Urban HIV Clinic.

Objective To determine the proportion of patients receiving statins for primary or secondary prevention of cardiovascular disease (CVD), as well as to describe lipid-screening trends. Design Retrospective chart review using the Manitoba Primary Care Research Network repository. Setting Manitoba. Participants A total of 149 262 patients. Main outcome measures Proportion of patients who were taking statins for primary or secondary prevention of cardiovascular disease (CVD), who did not have evidence of CVD recorded in their charts, and who underwent lipid screening; distribution of statins among age groups; and the proportion of patients eligible for lipid screening when the age cutoffs of the 2012 and 2016 Canadian Cardiovascular Society guidelines were applied. Results Of the 149 262 patients, 139 025 (93%) did not have CVD recorded in their electronic medical records and made up the primary prevention group; of these 139 025 patients, 5955 (4%) were taking statins. Also in the primary prevention group, 14 814 (11%) patients were 75 years of age and older; of these patients, 1374 (9%) were taking statins. A total of 10 237 of the 149 262 (7%) patients had CVD recorded in their charts (secondary prevention group); 3013 (29%) of these patients were taking statins. When the 2016 Canadian Cardiovascular Society guidelines age cutoffs were applied, 56% of patients (83 119 of 149 262) were eligible for lipid screening, and 31% (26 024 of 83 119) of them had evidence of screening in the past 5 years. Of the total population of those aged 75 and older, 28% (5597 of 20 188) had undergone lipid screening. Of the total population taking statins, 28% (2481 of 8968) had undergone lipid testing while taking statins. Conclusion In Manitoba, less than 5% of the primary prevention population and less than 30% of the secondary prevention population had received repeat statin prescriptions from their primary care providers. This represents a possible practice gap that warrants future research, as statins offer considerable morbidity and mortality benefits in these patients.

https://www.cfp.ca/content/cfp/63/11/e495.full.pdf

Statin-prescribing trends for primary and secondary prevention of cardiovascular disease.

Data quality of electronic medical records in Manitoba: do problem lists accurately reflect chronic disease billing diagnoses?

Objective To determine if the problem list (health conditions) in primary care electronic medical records (EMRs) accurately reflects the conditions for which chronic medications are prescribed in the EMR. Design A retrospective analysis of EMR data. Setting Eighteen primary care clinics across rural and urban Manitoba using the Accuro EMR. Participants Data from the EMRs of active patients seen in an 18-month period (December 18, 2011, to June 18, 2013, or December 3, 2012, to June 3, 2014) were used. Main outcome measures The likelihood of documentation in the EMR problem list of those specific chronic diseases for which drug prescriptions were documented in the EMR. Regression modeling was performed to determine the effect of clinic patient load and remuneration type on the completeness of EMR problem lists. Results Overall problem-list completeness was low but was highest for diabetes and lowest for insomnia. Fee-for-service clinics generally had lower problem-list completeness than salaried clinics did for all prescription medications examined. Panel size did not affect problem-list completeness rates. Conclusion The low EMR problem-list completeness suggests that this field is not reliable for use in quality improvement initiatives or research until higher reliability has been demonstrated. Further research is recommended to explore the reasons for the poor quality and to support improvement efforts.

Roberto Duarte

Papers

Data quality of electronic medical records in Manitoba: do problem lists accurately reflect chronic disease billing diagnoses?

Data quality in electronic medical records in Manitoba: Do problem lists reflect chronic disease as defined by prescriptions?