**5. The data at the point of care**

The data is the most important resource in the AI process. Efficient and effective management of this data by AI depends on many factors. The data in healthcare consists of the details of the patients—personal demographic details, historical aspects of the illness, clinical observations, diagnostic evaluation, reports generated, treatment

including medication, outcomes, and financial data like costs, billing. For effective analytics, be it descriptive, predictive, prescriptive, or cognitive, the data shall be accurate and comprehensive. The healthcare providers play a major role.

Much of the data that forms the basis of AI development is generated at the time the patient is at the healthcare provider, at the point of care. The importance of recording the data at the point of care cannot be overemphasised, once the opportunity of recording an event is lost, the data could be lost forever [11].

How careful are the clinicians, the laboratory staff, and others in the health care team while recording the data at the stage of its occurrence? Do they record the deficiencies, errors, personal bias in ordering tests and their interpretation? Are the complications and untoward reactions reported and recorded? One should know the value of data and the vigilance to be maintained at the point of care—where it is generated. The details, the quality, its reliability, and totality, including a report on the unexpected events, complications and interpretations need proper documentation.

Digital case records like electronic health records (EHR) have significantly enhanced the scope of the data collection [12]. The EHR is a health record that keeps the demographic data, clinical details including symptomatic, historical, clinical, diagnostic, therapeutic and outcome data, nurses' notes, pharmacy, other therapists like physical therapists, insurance, and billing data. Healthcare providers and organisations collect, track, store, and transmit personal health information. With so much data accumulating, what is important and what is not in the perspective of AI is an issue. What goes into the databases is important [13]. The responsibility of the health care personnel is noteworthy. The comments of the former editor of New England Journal of Medicine [14] reflect the rather unfortunate situation. He regrets to note that the published work does not represent the true state with significant data unpublished. True picture of the illness, its presentation, features, outcomes and complications and untoward reactions may go unrepresented in the database. With the policy of insisting on publication for academic recognition, doubts are cast on the validity of the published work [15, 16]. One should remember the old saying, "If an event is not documented, it did not occur." The value-based care depends on the validity of the database and its true reflection of knowledge base [17]. Many countries are making electronic health records mandatory. These include Australia, Belgium, Canada, Denmark, the United Kingdom, and United States. The goal of these initiatives in health information technologies is to digitally transform the collection, display, transmission, and storage of patient leading to a steady increase in data at the point of care [18–20].

Two other dimensions need consideration when EHR is discussed. The EHR is not followed universally in all countries. Paper case records and data entry in non-digital format is common. The inadequacies that are incidental in paper records and their reflection in the database need consideration. The EHR or the paper case records talk of the patient data while he is in doctor's office or in the hospital. Modern technology offers a different dimension to the data.

With advances in mobile technology, digital patient monitoring, tele-healthcare, ambulatory care and wearable devices, the data is generated while the patient is away from the hospital, at home, work or elsewhere. These data provides the status of the patient during the period intervening the doctor's visits, contextual and historical data that influence the outcomes and insights that the AI generates. The providers have an obligation to incorporate this data in the patient records. Transfer of this self-collected data to the AI database and its influence on the insights provided by AI in healthcare is to be ensured. The mobile technology and the wearables generating

data also lead the concerns of patient privacy, transparency, interoperability, and data sharing across all platforms [21, 22]. The actual time when the event occurred and the uploading of the data is also important as the data is likely to change with times, especially in acute care situations.
