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University of New Mexico Study Reveals Underreported Self-Harm Histories in Veterans Using Machine Learning

University of New Mexico Study Reveals Underreported Self-Harm Histories in Veterans Using Machine Learning

The University of New Mexico School of Medicine conducted a study analyzing electronic health records of over 1.3 million patients from the Veterans Health Administration. The study, published in the Journal of Medical Internet Research, found that traditional diagnosis codes identified self-harm history in only 1.85% of patients. However, a novel machine learning method developed by the researchers estimated that 7.9% of patients had documented self-harm histories, indicating a significant underreporting. The study combined machine learning with expert chart review and statistical calibration to achieve these results. Additionally, it was found that among veterans with a diagnosis code for self-harm, only 22.6% had self-harm listed on the VHA problem list. This discrepancy highlights potential gaps in mental health service needs and planning.

University of New Mexico Study Reveals Underreported Self-Harm Histories in Veterans

University of New Mexico Study Reveals Underreported Self-Harm Histories in Veterans

The University of New Mexico School of Medicine conducted a study analyzing electronic health records of over 1.3 million patients from the Veterans Health Administration. The study found that traditional diagnosis codes captured self-harm history in only 1.85% of patients, whereas a machine learning method estimated this figure to be 7.9%, indicating a significant underreporting. The research, published in the Journal of Medical Internet Research, utilized a novel machine learning approach combined with expert chart review and statistical calibration. The findings suggest that relying solely on diagnosis codes may lead to a substantial underestimation of the need for mental health services among veterans.

Machine Learning Uncovers Hidden Self-Harm Histories in Veterans' Health Records

Machine Learning Uncovers Hidden Self-Harm Histories in Veterans' Health Records

Researchers from the University of New Mexico School of Medicine have developed a machine learning method to detect hidden self-harm histories in veterans' health records. Analyzing over 1.3 million electronic health records from the Veterans Health Administration, the study found that traditional diagnosis codes captured only 1.85% of patients with self-harm histories. In contrast, the machine learning approach estimated this figure at 7.9%, revealing a significant underestimation. The study, published in the Journal of Medical Internet Research, highlights the potential of machine learning to improve the accuracy of health data and inform mental health service planning.

AI Uncovers Hidden Self-Harm Patterns in Veterans' Health Records

AI Uncovers Hidden Self-Harm Patterns in Veterans' Health Records

Researchers at The University of New Mexico School of Medicine have developed a machine learning method to identify self-harm patterns in veterans' health records. The study analyzed over 1.3 million electronic health records from the Veterans Health Administration (VHA) and found that traditional diagnosis codes captured only a fraction of self-harm incidents. The new method, called PULSNAR, estimates the probability of undocumented self-harm by analyzing patterns in the data. This approach revealed that self-harm was present in about 7.9% of patients, compared to the 1.85% identified through diagnosis codes alone. The study highlights the importance of comprehensive data analysis in understanding mental health needs.

UNM Researchers Use AI to Uncover Veteran Self-Harm Gaps in Health Records

UNM Researchers Use AI to Uncover Veteran Self-Harm Gaps in Health Records

Researchers at the University of New Mexico School of Medicine have conducted a study analyzing electronic health records of over 1.3 million patients from the Veterans Health Administration (VHA). The study highlights a significant gap in how health systems track self-harm, revealing that diagnosis codes capture only about one-fourth of clinically documented self-harm history. The research, published in the Journal of Medical Internet Research, utilized a machine learning method called PULSNAR to estimate the presence of self-harm history that is not visible through diagnosis codes. The study found that documented self-harm was present in about 7.9% of patients, compared to the 1.85% visible through diagnosis codes alone. This gap is crucial as it affects clinical awareness and planning for mental health services.

UNM Researchers Use AI to Identify Gaps in Veteran Self-Harm Records

UNM Researchers Use AI to Identify Gaps in Veteran Self-Harm Records

Researchers at the University of New Mexico School of Medicine have conducted a study analyzing electronic health records of over 1.3 million patients from the Veterans Health Administration (VHA). The study, published in the Journal of Medical Internet Research, highlights a significant gap in how health systems track self-harm. It found that diagnosis codes captured only about one-fourth of clinically documented self-harm history. The research utilized a machine learning method called PULSNAR to estimate the presence of self-harm history that was not visible through diagnosis codes. The study revealed that documented self-harm was present in about 7.9% of patients, compared to the 1.85% visible through diagnosis codes alone. This discrepancy underscores the challenges in accurately tracking mental health conditions, which can affect clinical awareness and planning for mental health services.

UNM Researchers Use AI to Identify Veteran Self-Harm in Medical Records

UNM Researchers Use AI to Identify Veteran Self-Harm in Medical Records

Researchers at the University of New Mexico School of Medicine have developed a machine learning method to identify self-harm among veterans, which is often under-recorded in medical records. The study analyzed over 1.3 million patient records from the Veterans Health Administration and found that diagnosis codes captured only a fraction of self-harm cases. The method, called PULSNAR, estimates the probability of self-harm history being present but not coded, revealing a significant gap in clinical documentation. This research aims to improve the visibility of mental health issues in veterans, which is crucial for effective treatment and prevention strategies.

Study Questions Reliability of Mental Health Diagnostic Interviews, Calls for Improved Tools

Study Questions Reliability of Mental Health Diagnostic Interviews, Calls for Improved Tools

A recent study published in Jama Network Open has raised concerns about the reliability of diagnostic interviews used to assess mental health conditions such as depression, anxiety, and substance use disorders. The study, led by Laura Duncan from McMaster University, highlights that these interviews, often considered the 'gold standard' in clinical and research settings, lack consistent validity and reliability. The research utilized Cohen's kappa coefficient to evaluate the reliability of these interviews across various mental health conditions, finding that substance use disorders, particularly opioid use disorder, showed higher reliability due to behavior-based criteria. Dr. Michael First, a psychiatrist at Columbia University, expressed frustration over the study's lack of detailed information on the reliability of specific diagnostic tools. The study underscores the need for more rigorous methods and the potential for future approaches that consider symptoms on a spectrum rather than strict diagnostic...

Psychologist Warns of AI Chatbots' Impact on Teen Mental Health

Psychologist Warns of AI Chatbots' Impact on Teen Mental Health

A clinical psychologist has raised concerns about the increasing reliance of teenagers on AI chatbots like ChatGPT for mental health support. The psychologist observed that teens are using these chatbots to seek immediate relief from symptoms of depression and anxiety, bypassing traditional mental health services. This trend is attributed to the younger generation's expectation for instant gratification and personalized responses. The psychologist noted that while AI chatbots offer a sense of connection, they also pose significant risks, including the potential for teens to follow harmful advice without consulting family or professionals. Early research suggests that this reliance on AI for mental health support could lead to dangerous outcomes, such as increased isolation and even suicide.

Anomaly Insights Launches AI Tool to Improve Managed Care Negotiations

Anomaly Insights Launches AI Tool to Improve Managed Care Negotiations

Anomaly Insights Launches AI Tool to Improve Managed Care Negotiations

Psychologist Raises Concerns Over Teen Use of AI Chatbots for Mental Health Support

Psychologist Raises Concerns Over Teen Use of AI Chatbots for Mental Health Support

A clinical psychologist has expressed alarm over the increasing use of AI chatbots by teenagers seeking mental health support. The psychologist noted that teens are turning to AI platforms like ChatGPT for immediate relief from symptoms of depression and anxiety, bypassing traditional mental health services. This trend is attributed to the desire for instant gratification and the convenience of AI chatbots, which offer quick responses without the need for appointments. The psychologist highlighted the potential dangers of relying on AI for mental health advice, including the risk of teens not discussing chatbot advice with loved ones, which could lead to severe consequences such as teen suicides linked to AI interactions. The psychologist emphasized the need for adults to engage with young people and model healthy behaviors, encouraging open conversations about their use of AI chatbots.

Novellia Secures $18M Series A to Enhance Patient Data Platform

Novellia Secures $18M Series A to Enhance Patient Data Platform

Novellia Secures $18M Series A to Enhance Patient Data Platform