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In recent years, so-called personalized medicine has become widespread and developed (Hamburg & Collins, 2010). The transition to personalized medicine within the paradigm of P4 medicine (Predictive, Preventive, Participatory and Personalized) (Sobradillo, Pozo, & Agustí, 2011) is inseparably linked to the transition from evidence-based (or volume-based) medicine to value-based approach. This approach can be expressed as the ratio of the change in the quality of patient life and the number of resources spent on the treatment (the number of tests, procedures, prescription drugs, medical hours, etc.) (Bae, 2015). The most valuable is the care delivery, which is based on rigorous scientific knowledge and has the minimum cost with maximum benefit for patients. Costs are determined not so much by the money spending of treatment, as by the time and effort the patient spends on the treatment, and also by the number of staff-hours. That is why it is necessary to carry out comprehensive efforts that will reduce costs and improve the quality of treatment to provide a quality healthcare delivery.
One of such areas is the improvement of human-computer interaction (HCI) between physicians and medical information systems. On the one hand, satisfaction with the system will allow physicians to enter more correctly and quickly all required information, and on the other hand, it will improve the quality of the data itself for their subsequent analysis.
The Western world invests significant resources to digitize healthcare with a particular emphasis on the creation of an integrated electronic health record (EHR) to improve the efficiency and quality of care (Fitzpatrick & Ellingsen, 2013). EHR offers several critical advantages over paper health records (PHR) related to the quality of care, efficiency and high level of patient safety (Hsiao, Hing, & Ashman, 2014). In addition, EHR is a valuable source of quality assurance of medical practice and research (Middleton, 2014). Practical use of EHR requires structured data entry. It can be a challenge for users due to EHR method of interaction, which does not coincide with their mental models and do not meet the requirements of document flow (Belden, Grayson, & Barnes, 2009; Friedberg et al., 2013). Poorly designed and cumbersome user interfaces of EHR input data can complicate the structured data-entry that will lead to a deterioration of data quality and incompleteness of data (Khajouei, Peek, Wierenga, Kersten, & Jaspers, 2010). Consequently, this can lead to suboptimal functioning of information systems of medical technology, integrated into the EHR, for example, computerized support for making clinical decisions (CDSS). CDSS is one of the most effective strategies for improving clinical decisions (Roshanov et al., 2013). CDSS often requires a large amount of data about the patient (demographic data, data on complaints, symptoms, medical history, physical examination, laboratory and other tests).
Despite the fact that researchers aim to improve the quality of service, most of them reported only about the improvement of the professional performance and attempted to identify the critical success factors for CDSS have provided conflicting results (Bright et al., 2012; Roshanov et al., 2013). CDSS take their information from forms were filled in EHR and can provide incomplete advice due to incomplete and unstructured EHR data (Jaspers, Smeulers, Vermeulen, & Peute, 2011). However, often the application of the existing approaches to design DSS health care and medicine is faced with significant difficulties for several reasons considered further concerning hospital practice. First, health information systems (HIS) in use often do not provide the functionality of DSS or the possibility to add such options. DSS deployment with existing HIS will complicate physicians’ work because with filling paper records and entering data into HIS they will have to double the data in the CDSS.