When Care Becomes Digital: Shared Experiences in Psychiatric Nursing-A Phenomenological Study.
Fatih Şahin, Özlem Ulaşoğlu
PMID 42294818WHAT IT FOUND
Nurses and patients in Turkish psychiatric units described routine digital documentation as eroding the human connection of care.
Nurses felt reduced to data clerks, while patients perceived them as distant and feared their private mental health records would be seen by others, weakening trust.
Key findings
01Nurses reported that screen-centered work pushed the humanistic core of their role into the background, making them feel like data entry clerks rather than caregivers.
02Patients stated that digital interactions lacked sincerity and emotional connection, with one noting the nurse looked more at the computer than making eye contact.
03Both groups experienced a trust paradox where digital records increased accountability but intensified fears of surveillance and unauthorized access to sensitive mental health data.
STILL TO COME
How it was doneWhat they foundWhat it means for RNs
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What it does not show
The study was conducted in a single state hospital in eastern Turkey, limiting its applicability to other healthcare systems or cultural contexts. All nurse participants were female, and the sample size was small (30 total), which restricts the diversity of professional and patient experiences captured. The term 'digitalisation' referred primarily to existing electronic health records and documentation systems, not to active artificial intelligence tools, so findings reflect current workflow pressures rather than AI-specific impacts. Data relied on self-reported interviews, which may be subject to recall bias or social desirability.
The easy way to misread this
Do not interpret these findings as evidence that artificial intelligence is currently degrading care in psychiatric settings. The study explicitly notes that 'digitalisation' referred to routine electronic health records and documentation systems, and participants' concerns about AI were anticipatory rather than based on experienced clinical AI use.