ReviewCurrent psychiatry reports2016
Automated Decision-Making and Big Data: Concerns for People With Mental Illness.
Review in Current psychiatry reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Use of data-driven decision-making among agencies serving individuals with criminal-legal system involvement: a qualitative study.BMC health services research · 2025Article
- Telepsychiatry in an Era of Digital Mental Health Startups.Current psychiatry reports · 2023Review
- Ethics and governance of trustworthy medical artificial intelligence.BMC medical informatics and decision making · 2023Article
- Time to Have Effective Regulation of the Mental Health Apps Market: Maximize Gains and Minimize Harms.Indian journal of psychological medicine · 2022Article
- AI revolution in healthcare and medicine and the (re-)emergence of inequalities and disadvantages for ageing population.Frontiers in sociology · 2022Article
- Internet of things issues related to psychiatry.International journal of bipolar disorders · 2021Review
- Increasing Cybercrime Since the Pandemic: Concerns for Psychiatry.Current psychiatry reports · 2021Review
- Artificial intelligence for good health: a scoping review of the ethics literature.BMC medical ethics · 2021Article
- Computer-Assisted CBT and Mobile Apps for Depression: Assessment and Integration Into Clinical Care.Focus (American Psychiatric Publishing) · 2020Article
- Smartphones in mental health: a critical review of background issues, current status and future concerns.International journal of bipolar disorders · 2020Review
- Distress, Suicidality, and Affective Disorders at the Time of Social Networks.Current psychiatry reports · 2019Review
- Computer-Assisted Cognitive-Behavior Therapy and Mobile Apps for Depression and Anxiety.Current psychiatry reports · 2019Review
- Internet use by older adults with bipolar disorder: international survey results.International journal of bipolar disorders · 2018Article
- Ethical perspectives on recommending digital technology for patients with mental illness.International journal of bipolar disorders · 2017Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automated decision-making by computer algorithms based on data from our behaviors is fundamental to the digital economy. Automated decisions impact everyone, occurring routinely in education, employment, health care, credit, and government services. Technologies that generate tracking data, including smartphones, credit cards, websites, social media, and sensors, offer unprecedented benefits. However, people are vulnerable to errors and biases in the underlying data and algorithms, especially those with mental illness. Algorithms based on big data from seemingly unrelated sources may create obstacles to community integration. Voluntary online self-disclosure and constant tracking blur traditional concepts of public versus private data, medical versus non-medical data, and human versus automated decision-making. In contrast to sharing sensitive information with a physician in a confidential relationship, there may be numerous readers of information revealed online; data may be sold repeatedly; used in proprietary algorithms; and are effectively permanent. Technological changes challenge traditional norms affecting privacy and decision-making, and continued discussions on new approaches to provide privacy protections are needed.
Indexed as
Identifiers
27783339What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.