Objective : The present study aimed to validate a questionnaire and measure the previous knowledge, attitude, and practice (KAP) of the general population and healthcare professionals regarding the debilitating disorder of dementia. Design : A questionnaire including 27 items was compiled by the authors and was circulated via the online platform. Setting :A questionnaire-based survey was conducted using the online modality. Participants : A convenience sampling method was used to recruit participants aged 18 and above from all walks of life. Measurements : Test-retest reliability, item analysis, and Cronbach’s alpha were calculated for the compiled questionnaire. The responses of the participants were assessed using descriptive statistics and the chi-square test. Results : A total of 503 responses were collected. The internal consistency (Cronbach's alpha=0.70) was acceptable and the test-retest reliability (0.823) was good. Eighty-one percent (408/503) of participants had heard the word dementia. Seven percent (27/408) of the participants who had heard the word dementia did not have any knowledge about the symptoms of dementia. Thirty-three percent (136/408) of participants believed that dementia could not be prevented. Almost half, i.e., 46% (187/408) of participants, considered dementia as a normal part of aging. Conclusions : The present study provides a fully validated questionnaire, which could prove helpful in research as it permits generating high-quality data and reducing measurement error. Knowledge of dementia among the general participants seems to be moderate and prompts towards the development of advocacy programs.

Patient Safety is at the center of all pharmacovigilance activities. As several covariates can impact the safety of a medicinal product in patients, a large amount of data is required for an accurate assessment of the safety and therefore, the benefit-risk balance of a medicinal product. Natural language processing, Artificial Intelligence, and Machine Learning are being popularly used to facilitate various pharmacovigilance activities in the Pharma industry. Artificial Intelligence and Machine learning if properly used in hospital settings can also facilitate the identification of adverse events from hospital records and discharge summaries and prescription errors, thus, alerting treating physicians regarding the same. However, the potential of using these techniques needs to be fully explored in hospital settings to facilitate the collection and evaluation of safety data.

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