#healthtech — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #healthtech, aggregated by home.social.
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𝑯𝒆𝒂𝒕𝒉𝑨𝑰2026
#HealthAI2026 #PrecisionMedicine #AIHealthcare #HealthcareInnovation #FutureOfHealthcare #HealthTech #MedicalAI #AIInMedicine #DigitalHealth #HealthcareTechnology #MedicalInnovation #ClinicalAI
@[email protected] @[email protected] @[email protected] @[email protected] @[email protected] @[email protected] @mayoclinic @stanfordmed @ucl @umn_infectiousdiseases @europe @czechrepublic @precisionmedicine @PennStateBiology @charlesuniprg
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Canada has the tech to connect health records. Why isn’t it being used?
#Canada #Health #DigitalHealth #Policy #HALO #EMR #ElectronicHealthRecords #HealthCareInnovation #Data #HealthTech #Interoperability #HealthSystemReform #Alberta #Ontario #MedicalCare
https://the-14.com/canada-has-the-tech-to-connect-health-records-why-isnt-it-being-used/ -
Canada has the tech to connect health records. Why isn’t it being used?
#Canada #Health #DigitalHealth #Policy #HALO #EMR #ElectronicHealthRecords #HealthCareInnovation #Data #HealthTech #Interoperability #HealthSystemReform #Alberta #Ontario #MedicalCare
https://the-14.com/canada-has-the-tech-to-connect-health-records-why-isnt-it-being-used/ -
Canada has the tech to connect health records. Why isn’t it being used?
#Canada #Health #DigitalHealth #Policy #HALO #EMR #ElectronicHealthRecords #HealthCareInnovation #Data #HealthTech #Interoperability #HealthSystemReform #Alberta #Ontario #MedicalCare
https://the-14.com/canada-has-the-tech-to-connect-health-records-why-isnt-it-being-used/ -
Canada has the tech to connect health records. Why isn’t it being used?
#Canada #Health #DigitalHealth #Policy #HALO #EMR #ElectronicHealthRecords #HealthCareInnovation #Data #HealthTech #Interoperability #HealthSystemReform #Alberta #Ontario #MedicalCare
https://the-14.com/canada-has-the-tech-to-connect-health-records-why-isnt-it-being-used/ -
DATE: May 27, 2026 at 04:30AM
SOURCE: STAT HEALTH TECHTITLE: STAT+: How Stanford patients help expose ‘fault lines’ in health AI adoption
At Stanford University, it’s easy to get carried away with technology. The computer mouse was invented there. So was Google. And now, it’s pumping out a myriad of tools for artificial intelligence in health care.
But for the last year and a half, Stanford’s hospital has been asking patients about new AI tools before they roll them out.
Eric Gries is one of those people, handpicked by Stanford as part of a “patient panel.” Gries was the caregiver for his wife while she was first on a left ventricular assist device (LVAD), then had a heart transplant. He later became the temporary caregiver for his brother-in-law when he also had a heart transplant.
Continue to STAT+ to read the full story…
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STAT News reports "from the frontiers of health and medicine".
Learn more at https://www.statnews.com/category/health-tech/ .
See also their complete Mastodon account at @STAT .
This robot is NOT affiliated with STAT news and merely rebroadcasts from their site. Responses posted here are not monitored.
-------------------------------------------------
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
.
Healthcare security & privacy posts not related to IT or infosec are at @HIPAABot . Even so, they mix in some infosec with the legal & regulatory information..
.
-------------------------------------------------#healthcare #healthtech #healthcaretech #healthtechnology #medgadget #medicine #doctor
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DATE: May 27, 2026 at 04:30AM
SOURCE: STAT HEALTH TECHTITLE: STAT+: How Stanford patients help expose ‘fault lines’ in health AI adoption
At Stanford University, it’s easy to get carried away with technology. The computer mouse was invented there. So was Google. And now, it’s pumping out a myriad of tools for artificial intelligence in health care.
But for the last year and a half, Stanford’s hospital has been asking patients about new AI tools before they roll them out.
Eric Gries is one of those people, handpicked by Stanford as part of a “patient panel.” Gries was the caregiver for his wife while she was first on a left ventricular assist device (LVAD), then had a heart transplant. He later became the temporary caregiver for his brother-in-law when he also had a heart transplant.
Continue to STAT+ to read the full story…
-------------------------------------------------
STAT News reports "from the frontiers of health and medicine".
Learn more at https://www.statnews.com/category/health-tech/ .
See also their complete Mastodon account at @STAT .
This robot is NOT affiliated with STAT news and merely rebroadcasts from their site. Responses posted here are not monitored.
-------------------------------------------------
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
.
Healthcare security & privacy posts not related to IT or infosec are at @HIPAABot . Even so, they mix in some infosec with the legal & regulatory information..
.
-------------------------------------------------#healthcare #healthtech #healthcaretech #healthtechnology #medgadget #medicine #doctor
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DATE: May 27, 2026 at 04:30AM
SOURCE: STAT HEALTH TECHTITLE: STAT+: How Stanford patients help expose ‘fault lines’ in health AI adoption
At Stanford University, it’s easy to get carried away with technology. The computer mouse was invented there. So was Google. And now, it’s pumping out a myriad of tools for artificial intelligence in health care.
But for the last year and a half, Stanford’s hospital has been asking patients about new AI tools before they roll them out.
Eric Gries is one of those people, handpicked by Stanford as part of a “patient panel.” Gries was the caregiver for his wife while she was first on a left ventricular assist device (LVAD), then had a heart transplant. He later became the temporary caregiver for his brother-in-law when he also had a heart transplant.
Continue to STAT+ to read the full story…
-------------------------------------------------
STAT News reports "from the frontiers of health and medicine".
Learn more at https://www.statnews.com/category/health-tech/ .
See also their complete Mastodon account at @STAT .
This robot is NOT affiliated with STAT news and merely rebroadcasts from their site. Responses posted here are not monitored.
-------------------------------------------------
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
.
Healthcare security & privacy posts not related to IT or infosec are at @HIPAABot . Even so, they mix in some infosec with the legal & regulatory information..
.
-------------------------------------------------#healthcare #healthtech #healthcaretech #healthtechnology #medgadget #medicine #doctor
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DATE: May 27, 2026 at 04:30AM
SOURCE: STAT HEALTH TECHTITLE: STAT+: How Stanford patients help expose ‘fault lines’ in health AI adoption
At Stanford University, it’s easy to get carried away with technology. The computer mouse was invented there. So was Google. And now, it’s pumping out a myriad of tools for artificial intelligence in health care.
But for the last year and a half, Stanford’s hospital has been asking patients about new AI tools before they roll them out.
Eric Gries is one of those people, handpicked by Stanford as part of a “patient panel.” Gries was the caregiver for his wife while she was first on a left ventricular assist device (LVAD), then had a heart transplant. He later became the temporary caregiver for his brother-in-law when he also had a heart transplant.
Continue to STAT+ to read the full story…
-------------------------------------------------
STAT News reports "from the frontiers of health and medicine".
Learn more at https://www.statnews.com/category/health-tech/ .
See also their complete Mastodon account at @STAT .
This robot is NOT affiliated with STAT news and merely rebroadcasts from their site. Responses posted here are not monitored.
-------------------------------------------------
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
.
Healthcare security & privacy posts not related to IT or infosec are at @HIPAABot . Even so, they mix in some infosec with the legal & regulatory information..
.
-------------------------------------------------#healthcare #healthtech #healthcaretech #healthtechnology #medgadget #medicine #doctor
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DATE: May 26, 2026 at 09:58AM
SOURCE: STAT HEALTH TECHTITLE: What do early data from Utah’s Doctronic AI pilot show?
You’re reading the web edition of STAT’s Health Tech newsletter, our guide to how technology is transforming the life sciences. Sign up to get it delivered in your inbox every Tuesday and Thursday.
Good morning health tech readers!
Read the rest…
-------------------------------------------------
STAT News reports "from the frontiers of health and medicine".
Learn more at https://www.statnews.com/category/health-tech/ .
See also their complete Mastodon account at @STAT .
This robot is NOT affiliated with STAT news and merely rebroadcasts from their site. Responses posted here are not monitored.
-------------------------------------------------
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
.
Healthcare security & privacy posts not related to IT or infosec are at @HIPAABot . Even so, they mix in some infosec with the legal & regulatory information..
.
-------------------------------------------------#healthcare #healthtech #healthcaretech #healthtechnology #medgadget #medicine #doctor
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DATE: May 26, 2026 at 09:58AM
SOURCE: STAT HEALTH TECHTITLE: What do early data from Utah’s Doctronic AI pilot show?
You’re reading the web edition of STAT’s Health Tech newsletter, our guide to how technology is transforming the life sciences. Sign up to get it delivered in your inbox every Tuesday and Thursday.
Good morning health tech readers!
Read the rest…
-------------------------------------------------
STAT News reports "from the frontiers of health and medicine".
Learn more at https://www.statnews.com/category/health-tech/ .
See also their complete Mastodon account at @STAT .
This robot is NOT affiliated with STAT news and merely rebroadcasts from their site. Responses posted here are not monitored.
-------------------------------------------------
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
.
Healthcare security & privacy posts not related to IT or infosec are at @HIPAABot . Even so, they mix in some infosec with the legal & regulatory information..
.
-------------------------------------------------#healthcare #healthtech #healthcaretech #healthtechnology #medgadget #medicine #doctor
-
DATE: May 26, 2026 at 09:58AM
SOURCE: STAT HEALTH TECHTITLE: What do early data from Utah’s Doctronic AI pilot show?
You’re reading the web edition of STAT’s Health Tech newsletter, our guide to how technology is transforming the life sciences. Sign up to get it delivered in your inbox every Tuesday and Thursday.
Good morning health tech readers!
Read the rest…
-------------------------------------------------
STAT News reports "from the frontiers of health and medicine".
Learn more at https://www.statnews.com/category/health-tech/ .
See also their complete Mastodon account at @STAT .
This robot is NOT affiliated with STAT news and merely rebroadcasts from their site. Responses posted here are not monitored.
-------------------------------------------------
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
.
Healthcare security & privacy posts not related to IT or infosec are at @HIPAABot . Even so, they mix in some infosec with the legal & regulatory information..
.
-------------------------------------------------#healthcare #healthtech #healthcaretech #healthtechnology #medgadget #medicine #doctor
-
DATE: May 26, 2026 at 09:58AM
SOURCE: STAT HEALTH TECHTITLE: What do early data from Utah’s Doctronic AI pilot show?
You’re reading the web edition of STAT’s Health Tech newsletter, our guide to how technology is transforming the life sciences. Sign up to get it delivered in your inbox every Tuesday and Thursday.
Good morning health tech readers!
Read the rest…
-------------------------------------------------
STAT News reports "from the frontiers of health and medicine".
Learn more at https://www.statnews.com/category/health-tech/ .
See also their complete Mastodon account at @STAT .
This robot is NOT affiliated with STAT news and merely rebroadcasts from their site. Responses posted here are not monitored.
-------------------------------------------------
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
.
Healthcare security & privacy posts not related to IT or infosec are at @HIPAABot . Even so, they mix in some infosec with the legal & regulatory information..
.
-------------------------------------------------#healthcare #healthtech #healthcaretech #healthtechnology #medgadget #medicine #doctor
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The Anti-Woke Teacher #America Desperately Needs Ag -
https://kensbookinfo.blogspot.com/p/us-government.html#4#Research #Ireland, #Irish Blood Transfusion Service back -
https://kensbookinfo.blogspot.com/p/health.html#12#Australia braces for week of wild weather -
https://kensbookinfo.blogspot.com/p/uk.html#4#Newparties scramble election strategy -
https://kensbookinfo.blogspot.com/p/etc-states.html#27#Healthtech company xWave plans 30 jobs after NHS d -
https://kensbookinfo.blogspot.com/p/etc-states.html#11View all latest news https://kensbookinfo.blogspot.com/2026/03/latest-news.html
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The Anti-Woke Teacher #America Desperately Needs Ag -
https://kensbookinfo.blogspot.com/p/us-government.html#4#Research #Ireland, #Irish Blood Transfusion Service back -
https://kensbookinfo.blogspot.com/p/health.html#12#Australia braces for week of wild weather -
https://kensbookinfo.blogspot.com/p/uk.html#4#Newparties scramble election strategy -
https://kensbookinfo.blogspot.com/p/etc-states.html#27#Healthtech company xWave plans 30 jobs after NHS d -
https://kensbookinfo.blogspot.com/p/etc-states.html#11View all latest news https://kensbookinfo.blogspot.com/2026/03/latest-news.html
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https://www.europesays.com/afrika/21501/ Neues Gesetz soll Gründung und Wachstum von Start-ups erleichtern | Special | Ghana #Accelerator #BusinessAngel #CrowdFunding #Digitalisierung #Finanzierung #Fintech #Ghana #Healthtech #Inkubator #StartUpÖkosystem #VentureCapital
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https://www.europesays.com/at/176945/ B12-Diagnostik im Wandel: Holotranscobalamin ersetzt den reinen Serumwert #AT #Austria #Blutwerte #Diagnostik #Ernährung #Gesundheit #Health #HealthTech #Holotranscobalamin #Homocystein #labor #Methylmalonsäure #Österreich #Supplementierung #Vegan #VitaminB12
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truly make a difference. Let each step you take count towards real progress.
#maximizingyourhealthwithdata #healthhabittracker #data-drivenwellness #digitalhealth #wellnessjourney #sustainablehealth #healthylifestyle #motivatedminded #selfimprovement #decisionfatigue #ixvena #healthtech [2/2]
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Muck Rack Secures STAT Content Deal, Bolstering Health Sector Monitoring
Muck Rack now includes STAT's health and medicine news. This helps health companies track industry news more easily.
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Muck Rack, which tracks news from over 600,000 sources, has added STAT's health reporting. This gives users more access to important health industry news.
#MuckRack, #STATNews, #HealthTech, #Biotech, #PharmaNews
https://newsletter.tf/muck-rack-adds-stat-health-news/ -
DATE: May 21, 2026 at 04:00PM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: A new AI tool spots hidden signs of adult ADHD months before a formal diagnosis
Scientists have developed a new artificial intelligence tool that can predict whether an adult has attention-deficit hyperactivity disorder by looking at their past medical records. The predictive model suggests that subtle patterns in everyday healthcare visits can identify undiagnosed individuals months before a doctor formally spots the condition. This research was recently published in the journal European Psychiatry.
Attention-deficit hyperactivity disorder is a common neurodevelopmental condition that affects roughly 5 to 7.2 percent of children and about 2.5 percent of adults globally. People with this condition experience varying degrees of inattention, hyperactivity, and impulsivity that interfere with daily life. Getting a proper diagnosis as an adult tends to be quite difficult.
Doctors often struggle to identify the condition in older patients because the symptoms frequently overlap with other mental health challenges. When a diagnosis is delayed, individuals often experience academic or work impairments, increased accident rates, and a lower overall quality of life. An earlier diagnosis provides evidence-based opportunities for pharmacological treatment and therapy, which helps prevent many of these negative outcomes.
Artificial intelligence has recently shown promise in helping doctors spot hidden patterns in patient data. Many previous attempts to use machine learning to detect attention-deficit hyperactivity disorder have mostly relied on brain scans, structured behavioral assessments, or specialized physiological tests. These types of medical data are expensive and not routinely collected for the average patient.
To create a more practical tool, the researchers decided to focus on electronic health records. These records are the standard digital files that clinics and hospitals already maintain for every patient. By training a computer program to read standard medical histories, the authors hoped to create a cost-effective screening method that relies purely on information doctors already have on hand.
The scientists analyzed historical medical data from a regional healthcare system in southwestern Sweden. The database included information from primary care clinics, specialist visits, and hospital admissions between 2011 and 2022. They gathered detailed data on patient demographics, specific medical diagnoses, clinical procedures, and prescribed medications.
To build their model, the researchers started with a group of 3,570 adults who had been formally diagnosed with attention-deficit hyperactivity disorder or prescribed related medications. They also selected a control group of adults who had visited psychiatric outpatient clinics but did not have the disorder. During the design phase, the predictive model struggled to tell the two groups apart when the control group included patients with depression or anxiety.
To fix this issue, the researchers removed individuals with depression and anxiety from the control group. Because the cognitive and behavioral symptoms of depression and anxiety overlap heavily with attention issues, removing them allowed the computer to focus on the unique signatures of attention-deficit hyperactivity disorder. This adjustment left a final control group of 5,126 adults, which still provided plenty of data for the program.
The authors then fed this data into a machine learning system based on a “transformer” architecture. A transformer is a sophisticated type of artificial intelligence technology that excels at understanding sequences of information. Instead of reading words in a sentence, this specific transformer was trained to read the sequence of a patient’s medical visits and prescription codes over time.
These models use a mathematical technique called positional encoding to understand the exact chronological order of events. This allows the system to grasp how a patient’s health trajectory changes over the course of several months or years. The researchers tested whether the model could predict a diagnosis six, twelve, and eighteen months before the actual diagnosis date.
They evaluated the final model on an entirely separate set of 800 patients, splitting this test group evenly with 400 diagnosed individuals and 400 individuals without the condition. Testing the model on a separate group ensures that the artificial intelligence is evaluated on fresh information it has never seen before. The findings suggest that the model can successfully predict adult attention-deficit hyperactivity disorder using routine clinical data.
The artificial intelligence performed best when predicting a diagnosis six months in advance. At this six-month mark, the model correctly identified 80 percent of the patients who actually had the disorder. It also correctly ruled out the condition in 77 percent of the patients who did not have it.
The model achieved a score of 0.79 on a mathematical metric called the Area Under the Curve. This metric evaluates how well a predictive model distinguishes between two groups, with a score of 1.0 being perfect and 0.5 being no better than a random guess. The results remained fairly stable even when predicting diagnoses eighteen months into the past.
The scientists also examined which specific medical codes the computer used to make its predictions. To do this, they used an analytical technique called Shapley Additive Explanations. This method helps open the “black box” of artificial intelligence by showing exactly which demographic factors or clinical codes increase or reduce the predicted risk.
The analysis revealed that previous diagnoses related to substance use were strong indicators of a future attention-deficit hyperactivity disorder diagnosis. For example, medical codes indicating the use of stimulants, including heavy caffeine use, were highly predictive. The model also flagged codes related to specific blood alcohol levels ranging from 0.60 to 0.79 milligrams per 100 milliliters.
These findings align with previous research, which indicates that adults with undiagnosed attention-related issues sometimes try to self-medicate with caffeine, alcohol, or other substances. The computer program also picked up on medical codes related to childbirth complications. The data suggests that mothers who experience issues such as obstructed labor or abnormal fetal positions have a slightly higher chance of a later attention-deficit hyperactivity disorder diagnosis.
Researchers suspect this reflects broader physical and psychosocial challenges rather than a direct physical cause. Additionally, the scientists noticed distinct demographic and healthcare utilization patterns between the two groups. The diagnosed individuals tended to be younger, averaging around 31 years old compared to 52 years old in the control group.
They also had significantly more primary care and psychiatrist visits than the control group, but fewer hospital admissions and shorter hospital stays. While these findings are promising, the authors caution against viewing this artificial intelligence as a replacement for human doctors. The tool is not designed to formally diagnose anyone on its own.
Instead, it is meant to act as an early warning system that operates quietly in the background of a hospital’s computer network. By flagging patients who exhibit suspicious patterns of healthcare use, the system can simply notify doctors that a specific person might benefit from a comprehensive psychological evaluation. A trained healthcare professional must still sit down with the patient to conduct structured interviews and confirm the diagnosis.
One limitation of the study is the exclusion of patients with depression and anxiety from the control group. In a real clinical setting, doctors frequently need to distinguish between attention-deficit hyperactivity disorder and depression. Because the model was not trained on patients with these specific overlapping conditions, it might face challenges when deployed in a general psychiatric population.
The researchers also noted a slight discrepancy in how the model treated men and women. The artificial intelligence successfully identified the condition in 75.2 percent of the female patients, but only caught 66.7 percent of the male cases. The false positive rate remained consistent across genders, but the disparity in successful identification highlights the need for further evaluation to ensure equitable performance.
Moving forward, scientists hope to test this model in different healthcare systems outside of Sweden. Medical coding practices can vary significantly from one country to another, so the algorithm must prove its adaptability. The authors also suggest exploring how this data-driven approach might align with newer, more flexible ways of classifying mental health conditions in the future.
The study, “Early detection of adults ADHD using electronic health records: A machine learning study“, was authored by Omar Hamed, Farzaneh Etminani, Peter Jacobsson, and Thomas Davidsson.
-------------------------------------------------
DAILY EMAIL DIGEST: Email [email protected] -- no subject or message needed.
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot
NYU Information for Practice puts out 400-500 good quality health-related research posts per week but its too much for many people, so that bot is limited to just subscribers. You can read it or subscribe at @PsychResearchBot
Since 1991 The National Psychologist has focused on keeping practicing psychologists current with news, information and items of interest. Check them out for more free articles, resources, and subscription information: https://www.nationalpsychologist.com
EMAIL DAILY DIGEST OF RSS FEEDS -- SUBSCRIBE: http://subscribe-article-digests.clinicians-exchange.org
READ ONLINE: http://read-the-rss-mega-archive.clinicians-exchange.org
It's primitive... but it works... mostly...
-------------------------------------------------
#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #ADHD #AdultADHD #MedicineAI #ElectronicHealthRecords #MentalHealthTech #AIinHealthcare #EarlyDetection #NeurodevelopmentalDisorders #HealthTech #ADHDResearch
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DATE: May 21, 2026 at 04:00PM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: A new AI tool spots hidden signs of adult ADHD months before a formal diagnosis
Scientists have developed a new artificial intelligence tool that can predict whether an adult has attention-deficit hyperactivity disorder by looking at their past medical records. The predictive model suggests that subtle patterns in everyday healthcare visits can identify undiagnosed individuals months before a doctor formally spots the condition. This research was recently published in the journal European Psychiatry.
Attention-deficit hyperactivity disorder is a common neurodevelopmental condition that affects roughly 5 to 7.2 percent of children and about 2.5 percent of adults globally. People with this condition experience varying degrees of inattention, hyperactivity, and impulsivity that interfere with daily life. Getting a proper diagnosis as an adult tends to be quite difficult.
Doctors often struggle to identify the condition in older patients because the symptoms frequently overlap with other mental health challenges. When a diagnosis is delayed, individuals often experience academic or work impairments, increased accident rates, and a lower overall quality of life. An earlier diagnosis provides evidence-based opportunities for pharmacological treatment and therapy, which helps prevent many of these negative outcomes.
Artificial intelligence has recently shown promise in helping doctors spot hidden patterns in patient data. Many previous attempts to use machine learning to detect attention-deficit hyperactivity disorder have mostly relied on brain scans, structured behavioral assessments, or specialized physiological tests. These types of medical data are expensive and not routinely collected for the average patient.
To create a more practical tool, the researchers decided to focus on electronic health records. These records are the standard digital files that clinics and hospitals already maintain for every patient. By training a computer program to read standard medical histories, the authors hoped to create a cost-effective screening method that relies purely on information doctors already have on hand.
The scientists analyzed historical medical data from a regional healthcare system in southwestern Sweden. The database included information from primary care clinics, specialist visits, and hospital admissions between 2011 and 2022. They gathered detailed data on patient demographics, specific medical diagnoses, clinical procedures, and prescribed medications.
To build their model, the researchers started with a group of 3,570 adults who had been formally diagnosed with attention-deficit hyperactivity disorder or prescribed related medications. They also selected a control group of adults who had visited psychiatric outpatient clinics but did not have the disorder. During the design phase, the predictive model struggled to tell the two groups apart when the control group included patients with depression or anxiety.
To fix this issue, the researchers removed individuals with depression and anxiety from the control group. Because the cognitive and behavioral symptoms of depression and anxiety overlap heavily with attention issues, removing them allowed the computer to focus on the unique signatures of attention-deficit hyperactivity disorder. This adjustment left a final control group of 5,126 adults, which still provided plenty of data for the program.
The authors then fed this data into a machine learning system based on a “transformer” architecture. A transformer is a sophisticated type of artificial intelligence technology that excels at understanding sequences of information. Instead of reading words in a sentence, this specific transformer was trained to read the sequence of a patient’s medical visits and prescription codes over time.
These models use a mathematical technique called positional encoding to understand the exact chronological order of events. This allows the system to grasp how a patient’s health trajectory changes over the course of several months or years. The researchers tested whether the model could predict a diagnosis six, twelve, and eighteen months before the actual diagnosis date.
They evaluated the final model on an entirely separate set of 800 patients, splitting this test group evenly with 400 diagnosed individuals and 400 individuals without the condition. Testing the model on a separate group ensures that the artificial intelligence is evaluated on fresh information it has never seen before. The findings suggest that the model can successfully predict adult attention-deficit hyperactivity disorder using routine clinical data.
The artificial intelligence performed best when predicting a diagnosis six months in advance. At this six-month mark, the model correctly identified 80 percent of the patients who actually had the disorder. It also correctly ruled out the condition in 77 percent of the patients who did not have it.
The model achieved a score of 0.79 on a mathematical metric called the Area Under the Curve. This metric evaluates how well a predictive model distinguishes between two groups, with a score of 1.0 being perfect and 0.5 being no better than a random guess. The results remained fairly stable even when predicting diagnoses eighteen months into the past.
The scientists also examined which specific medical codes the computer used to make its predictions. To do this, they used an analytical technique called Shapley Additive Explanations. This method helps open the “black box” of artificial intelligence by showing exactly which demographic factors or clinical codes increase or reduce the predicted risk.
The analysis revealed that previous diagnoses related to substance use were strong indicators of a future attention-deficit hyperactivity disorder diagnosis. For example, medical codes indicating the use of stimulants, including heavy caffeine use, were highly predictive. The model also flagged codes related to specific blood alcohol levels ranging from 0.60 to 0.79 milligrams per 100 milliliters.
These findings align with previous research, which indicates that adults with undiagnosed attention-related issues sometimes try to self-medicate with caffeine, alcohol, or other substances. The computer program also picked up on medical codes related to childbirth complications. The data suggests that mothers who experience issues such as obstructed labor or abnormal fetal positions have a slightly higher chance of a later attention-deficit hyperactivity disorder diagnosis.
Researchers suspect this reflects broader physical and psychosocial challenges rather than a direct physical cause. Additionally, the scientists noticed distinct demographic and healthcare utilization patterns between the two groups. The diagnosed individuals tended to be younger, averaging around 31 years old compared to 52 years old in the control group.
They also had significantly more primary care and psychiatrist visits than the control group, but fewer hospital admissions and shorter hospital stays. While these findings are promising, the authors caution against viewing this artificial intelligence as a replacement for human doctors. The tool is not designed to formally diagnose anyone on its own.
Instead, it is meant to act as an early warning system that operates quietly in the background of a hospital’s computer network. By flagging patients who exhibit suspicious patterns of healthcare use, the system can simply notify doctors that a specific person might benefit from a comprehensive psychological evaluation. A trained healthcare professional must still sit down with the patient to conduct structured interviews and confirm the diagnosis.
One limitation of the study is the exclusion of patients with depression and anxiety from the control group. In a real clinical setting, doctors frequently need to distinguish between attention-deficit hyperactivity disorder and depression. Because the model was not trained on patients with these specific overlapping conditions, it might face challenges when deployed in a general psychiatric population.
The researchers also noted a slight discrepancy in how the model treated men and women. The artificial intelligence successfully identified the condition in 75.2 percent of the female patients, but only caught 66.7 percent of the male cases. The false positive rate remained consistent across genders, but the disparity in successful identification highlights the need for further evaluation to ensure equitable performance.
Moving forward, scientists hope to test this model in different healthcare systems outside of Sweden. Medical coding practices can vary significantly from one country to another, so the algorithm must prove its adaptability. The authors also suggest exploring how this data-driven approach might align with newer, more flexible ways of classifying mental health conditions in the future.
The study, “Early detection of adults ADHD using electronic health records: A machine learning study“, was authored by Omar Hamed, Farzaneh Etminani, Peter Jacobsson, and Thomas Davidsson.
-------------------------------------------------
DAILY EMAIL DIGEST: Email [email protected] -- no subject or message needed.
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot
NYU Information for Practice puts out 400-500 good quality health-related research posts per week but its too much for many people, so that bot is limited to just subscribers. You can read it or subscribe at @PsychResearchBot
Since 1991 The National Psychologist has focused on keeping practicing psychologists current with news, information and items of interest. Check them out for more free articles, resources, and subscription information: https://www.nationalpsychologist.com
EMAIL DAILY DIGEST OF RSS FEEDS -- SUBSCRIBE: http://subscribe-article-digests.clinicians-exchange.org
READ ONLINE: http://read-the-rss-mega-archive.clinicians-exchange.org
It's primitive... but it works... mostly...
-------------------------------------------------
#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #ADHD #AdultADHD #MedicineAI #ElectronicHealthRecords #MentalHealthTech #AIinHealthcare #EarlyDetection #NeurodevelopmentalDisorders #HealthTech #ADHDResearch
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DATE: May 21, 2026 at 04:00PM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: A new AI tool spots hidden signs of adult ADHD months before a formal diagnosis
Scientists have developed a new artificial intelligence tool that can predict whether an adult has attention-deficit hyperactivity disorder by looking at their past medical records. The predictive model suggests that subtle patterns in everyday healthcare visits can identify undiagnosed individuals months before a doctor formally spots the condition. This research was recently published in the journal European Psychiatry.
Attention-deficit hyperactivity disorder is a common neurodevelopmental condition that affects roughly 5 to 7.2 percent of children and about 2.5 percent of adults globally. People with this condition experience varying degrees of inattention, hyperactivity, and impulsivity that interfere with daily life. Getting a proper diagnosis as an adult tends to be quite difficult.
Doctors often struggle to identify the condition in older patients because the symptoms frequently overlap with other mental health challenges. When a diagnosis is delayed, individuals often experience academic or work impairments, increased accident rates, and a lower overall quality of life. An earlier diagnosis provides evidence-based opportunities for pharmacological treatment and therapy, which helps prevent many of these negative outcomes.
Artificial intelligence has recently shown promise in helping doctors spot hidden patterns in patient data. Many previous attempts to use machine learning to detect attention-deficit hyperactivity disorder have mostly relied on brain scans, structured behavioral assessments, or specialized physiological tests. These types of medical data are expensive and not routinely collected for the average patient.
To create a more practical tool, the researchers decided to focus on electronic health records. These records are the standard digital files that clinics and hospitals already maintain for every patient. By training a computer program to read standard medical histories, the authors hoped to create a cost-effective screening method that relies purely on information doctors already have on hand.
The scientists analyzed historical medical data from a regional healthcare system in southwestern Sweden. The database included information from primary care clinics, specialist visits, and hospital admissions between 2011 and 2022. They gathered detailed data on patient demographics, specific medical diagnoses, clinical procedures, and prescribed medications.
To build their model, the researchers started with a group of 3,570 adults who had been formally diagnosed with attention-deficit hyperactivity disorder or prescribed related medications. They also selected a control group of adults who had visited psychiatric outpatient clinics but did not have the disorder. During the design phase, the predictive model struggled to tell the two groups apart when the control group included patients with depression or anxiety.
To fix this issue, the researchers removed individuals with depression and anxiety from the control group. Because the cognitive and behavioral symptoms of depression and anxiety overlap heavily with attention issues, removing them allowed the computer to focus on the unique signatures of attention-deficit hyperactivity disorder. This adjustment left a final control group of 5,126 adults, which still provided plenty of data for the program.
The authors then fed this data into a machine learning system based on a “transformer” architecture. A transformer is a sophisticated type of artificial intelligence technology that excels at understanding sequences of information. Instead of reading words in a sentence, this specific transformer was trained to read the sequence of a patient’s medical visits and prescription codes over time.
These models use a mathematical technique called positional encoding to understand the exact chronological order of events. This allows the system to grasp how a patient’s health trajectory changes over the course of several months or years. The researchers tested whether the model could predict a diagnosis six, twelve, and eighteen months before the actual diagnosis date.
They evaluated the final model on an entirely separate set of 800 patients, splitting this test group evenly with 400 diagnosed individuals and 400 individuals without the condition. Testing the model on a separate group ensures that the artificial intelligence is evaluated on fresh information it has never seen before. The findings suggest that the model can successfully predict adult attention-deficit hyperactivity disorder using routine clinical data.
The artificial intelligence performed best when predicting a diagnosis six months in advance. At this six-month mark, the model correctly identified 80 percent of the patients who actually had the disorder. It also correctly ruled out the condition in 77 percent of the patients who did not have it.
The model achieved a score of 0.79 on a mathematical metric called the Area Under the Curve. This metric evaluates how well a predictive model distinguishes between two groups, with a score of 1.0 being perfect and 0.5 being no better than a random guess. The results remained fairly stable even when predicting diagnoses eighteen months into the past.
The scientists also examined which specific medical codes the computer used to make its predictions. To do this, they used an analytical technique called Shapley Additive Explanations. This method helps open the “black box” of artificial intelligence by showing exactly which demographic factors or clinical codes increase or reduce the predicted risk.
The analysis revealed that previous diagnoses related to substance use were strong indicators of a future attention-deficit hyperactivity disorder diagnosis. For example, medical codes indicating the use of stimulants, including heavy caffeine use, were highly predictive. The model also flagged codes related to specific blood alcohol levels ranging from 0.60 to 0.79 milligrams per 100 milliliters.
These findings align with previous research, which indicates that adults with undiagnosed attention-related issues sometimes try to self-medicate with caffeine, alcohol, or other substances. The computer program also picked up on medical codes related to childbirth complications. The data suggests that mothers who experience issues such as obstructed labor or abnormal fetal positions have a slightly higher chance of a later attention-deficit hyperactivity disorder diagnosis.
Researchers suspect this reflects broader physical and psychosocial challenges rather than a direct physical cause. Additionally, the scientists noticed distinct demographic and healthcare utilization patterns between the two groups. The diagnosed individuals tended to be younger, averaging around 31 years old compared to 52 years old in the control group.
They also had significantly more primary care and psychiatrist visits than the control group, but fewer hospital admissions and shorter hospital stays. While these findings are promising, the authors caution against viewing this artificial intelligence as a replacement for human doctors. The tool is not designed to formally diagnose anyone on its own.
Instead, it is meant to act as an early warning system that operates quietly in the background of a hospital’s computer network. By flagging patients who exhibit suspicious patterns of healthcare use, the system can simply notify doctors that a specific person might benefit from a comprehensive psychological evaluation. A trained healthcare professional must still sit down with the patient to conduct structured interviews and confirm the diagnosis.
One limitation of the study is the exclusion of patients with depression and anxiety from the control group. In a real clinical setting, doctors frequently need to distinguish between attention-deficit hyperactivity disorder and depression. Because the model was not trained on patients with these specific overlapping conditions, it might face challenges when deployed in a general psychiatric population.
The researchers also noted a slight discrepancy in how the model treated men and women. The artificial intelligence successfully identified the condition in 75.2 percent of the female patients, but only caught 66.7 percent of the male cases. The false positive rate remained consistent across genders, but the disparity in successful identification highlights the need for further evaluation to ensure equitable performance.
Moving forward, scientists hope to test this model in different healthcare systems outside of Sweden. Medical coding practices can vary significantly from one country to another, so the algorithm must prove its adaptability. The authors also suggest exploring how this data-driven approach might align with newer, more flexible ways of classifying mental health conditions in the future.
The study, “Early detection of adults ADHD using electronic health records: A machine learning study“, was authored by Omar Hamed, Farzaneh Etminani, Peter Jacobsson, and Thomas Davidsson.
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DATE: May 21, 2026 at 04:30AM
SOURCE: STAT HEALTH TECHTITLE: STAT+: After warning letter, Whoop and FDA in discussions about controversial blood pressure feature
SAN FRANCISCO — Wearable maker Whoop has not resolved a dispute with the Food and Drug Administration over a blood pressure feature the company launched last year, according to a top health executive at the company.
FDA warned Whoop last July over the feature that gives users a daily estimate of their systolic and diastolic blood pressure, saying that it’s a medical device that requires agency review. Whoop argues that the feature is exempt from FDA review because it is intended for wellness purposes.
In January, FDA updated its guidance on general wellness products to include those that provide information about blood pressure, provided they are not intended to treat and diagnose disease. To avoid being classified as medical devices, these products also must meet other criteria, including that they be non-invasive and that they “do not include values that mimic those used clinically unless validated.”
Continue to STAT+ to read the full story…
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DATE: May 21, 2026 at 04:30AM
SOURCE: STAT HEALTH TECHTITLE: STAT+: After warning letter, Whoop and FDA in discussions about controversial blood pressure feature
SAN FRANCISCO — Wearable maker Whoop has not resolved a dispute with the Food and Drug Administration over a blood pressure feature the company launched last year, according to a top health executive at the company.
FDA warned Whoop last July over the feature that gives users a daily estimate of their systolic and diastolic blood pressure, saying that it’s a medical device that requires agency review. Whoop argues that the feature is exempt from FDA review because it is intended for wellness purposes.
In January, FDA updated its guidance on general wellness products to include those that provide information about blood pressure, provided they are not intended to treat and diagnose disease. To avoid being classified as medical devices, these products also must meet other criteria, including that they be non-invasive and that they “do not include values that mimic those used clinically unless validated.”
Continue to STAT+ to read the full story…
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DATE: May 21, 2026 at 04:30AM
SOURCE: STAT HEALTH TECHTITLE: STAT+: After warning letter, Whoop and FDA in discussions about controversial blood pressure feature
SAN FRANCISCO — Wearable maker Whoop has not resolved a dispute with the Food and Drug Administration over a blood pressure feature the company launched last year, according to a top health executive at the company.
FDA warned Whoop last July over the feature that gives users a daily estimate of their systolic and diastolic blood pressure, saying that it’s a medical device that requires agency review. Whoop argues that the feature is exempt from FDA review because it is intended for wellness purposes.
In January, FDA updated its guidance on general wellness products to include those that provide information about blood pressure, provided they are not intended to treat and diagnose disease. To avoid being classified as medical devices, these products also must meet other criteria, including that they be non-invasive and that they “do not include values that mimic those used clinically unless validated.”
Continue to STAT+ to read the full story…
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STAT News reports "from the frontiers of health and medicine".
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DATE: May 21, 2026 at 04:30AM
SOURCE: STAT HEALTH TECHTITLE: STAT+: After warning letter, Whoop and FDA in discussions about controversial blood pressure feature
SAN FRANCISCO — Wearable maker Whoop has not resolved a dispute with the Food and Drug Administration over a blood pressure feature the company launched last year, according to a top health executive at the company.
FDA warned Whoop last July over the feature that gives users a daily estimate of their systolic and diastolic blood pressure, saying that it’s a medical device that requires agency review. Whoop argues that the feature is exempt from FDA review because it is intended for wellness purposes.
In January, FDA updated its guidance on general wellness products to include those that provide information about blood pressure, provided they are not intended to treat and diagnose disease. To avoid being classified as medical devices, these products also must meet other criteria, including that they be non-invasive and that they “do not include values that mimic those used clinically unless validated.”
Continue to STAT+ to read the full story…
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STAT News reports "from the frontiers of health and medicine".
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DATE: May 20, 2026 at 03:28PM
SOURCE: STAT HEALTH TECHTITLE: Congressional Democrats try to force a vote to end Medicare AI prior authorization pilot
Senate and House Democrats introduced resolutions Wednesday to overturn a Medicare pilot that uses artificial intelligence to approve or deny care. The technology experiment has led to allegations of delayed care for seniors in at least one of the six states it is currently being implemented.
The newest effort to force a vote to stop the experiment follows a Government Accountability Office ruling from last week. The GAO determined that the Wasteful and Inappropriate Service Reduction program (WISeR) is subject to Congressional approval and should have been submitted to Congress before going into effect in January.
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DATE: May 20, 2026 at 03:28PM
SOURCE: STAT HEALTH TECHTITLE: Congressional Democrats try to force a vote to end Medicare AI prior authorization pilot
Senate and House Democrats introduced resolutions Wednesday to overturn a Medicare pilot that uses artificial intelligence to approve or deny care. The technology experiment has led to allegations of delayed care for seniors in at least one of the six states it is currently being implemented.
The newest effort to force a vote to stop the experiment follows a Government Accountability Office ruling from last week. The GAO determined that the Wasteful and Inappropriate Service Reduction program (WISeR) is subject to Congressional approval and should have been submitted to Congress before going into effect in January.
Read the rest…
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STAT News reports "from the frontiers of health and medicine".
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DATE: May 20, 2026 at 03:28PM
SOURCE: STAT HEALTH TECHTITLE: Congressional Democrats try to force a vote to end Medicare AI prior authorization pilot
Senate and House Democrats introduced resolutions Wednesday to overturn a Medicare pilot that uses artificial intelligence to approve or deny care. The technology experiment has led to allegations of delayed care for seniors in at least one of the six states it is currently being implemented.
The newest effort to force a vote to stop the experiment follows a Government Accountability Office ruling from last week. The GAO determined that the Wasteful and Inappropriate Service Reduction program (WISeR) is subject to Congressional approval and should have been submitted to Congress before going into effect in January.
Read the rest…
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DATE: May 20, 2026 at 03:28PM
SOURCE: STAT HEALTH TECHTITLE: Congressional Democrats try to force a vote to end Medicare AI prior authorization pilot
Senate and House Democrats introduced resolutions Wednesday to overturn a Medicare pilot that uses artificial intelligence to approve or deny care. The technology experiment has led to allegations of delayed care for seniors in at least one of the six states it is currently being implemented.
The newest effort to force a vote to stop the experiment follows a Government Accountability Office ruling from last week. The GAO determined that the Wasteful and Inappropriate Service Reduction program (WISeR) is subject to Congressional approval and should have been submitted to Congress before going into effect in January.
Read the rest…
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DATE: May 20, 2026 at 03:28PM
SOURCE: STAT HEALTH TECHTITLE: Congressional Democrats try to force a vote to end Medicare AI prior authorization pilot
Senate and House Democrats introduced resolutions Wednesday to overturn a Medicare pilot that uses artificial intelligence to approve or deny care. The technology experiment has led to allegations of delayed care for seniors in at least one of the six states it is currently being implemented.
The newest effort to force a vote to stop the experiment follows a Government Accountability Office ruling from last week. The GAO determined that the Wasteful and Inappropriate Service Reduction program (WISeR) is subject to Congressional approval and should have been submitted to Congress before going into effect in January.
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DATE: May 20, 2026 at 04:30AM
SOURCE: STAT HEALTH TECHTITLE: STAT+: OpenEvidence makes its pitch to hospitals. ‘We’re not crazy monsters’
SAN FRANCISCO — OpenEvidence rode the wave of early enthusiasm for large language models by building a free chatbot for doctors. Physicians, especially trainees, have flocked to the platform to help make patient care decisions; the company claims that about 650,000 U.S. physicians use it actively.
In just four years, OpenEvidence has leveraged that popularity into a $12 billion valuation, going directly to clinicians and avoiding health tech’s traditional hospital procurement process. But the company is facing competitive pressures and questions about whether its ad-based business model can continue to propel the company forward.
At the STAT Breakthrough Summit West, OpenEvidence chief technology officer Zachary Ziegler said that while the company has grown by building for individual clinicians, it’s time to aim bigger.
Continue to STAT+ to read the full story…
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-
DATE: May 20, 2026 at 04:30AM
SOURCE: STAT HEALTH TECHTITLE: STAT+: OpenEvidence makes its pitch to hospitals. ‘We’re not crazy monsters’
SAN FRANCISCO — OpenEvidence rode the wave of early enthusiasm for large language models by building a free chatbot for doctors. Physicians, especially trainees, have flocked to the platform to help make patient care decisions; the company claims that about 650,000 U.S. physicians use it actively.
In just four years, OpenEvidence has leveraged that popularity into a $12 billion valuation, going directly to clinicians and avoiding health tech’s traditional hospital procurement process. But the company is facing competitive pressures and questions about whether its ad-based business model can continue to propel the company forward.
At the STAT Breakthrough Summit West, OpenEvidence chief technology officer Zachary Ziegler said that while the company has grown by building for individual clinicians, it’s time to aim bigger.
Continue to STAT+ to read the full story…
-------------------------------------------------
STAT News reports "from the frontiers of health and medicine".
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-------------------------------------------------#healthcare #healthtech #healthcaretech #healthtechnology #medgadget #medicine #doctor
-
DATE: May 20, 2026 at 04:30AM
SOURCE: STAT HEALTH TECHTITLE: STAT+: OpenEvidence makes its pitch to hospitals. ‘We’re not crazy monsters’
SAN FRANCISCO — OpenEvidence rode the wave of early enthusiasm for large language models by building a free chatbot for doctors. Physicians, especially trainees, have flocked to the platform to help make patient care decisions; the company claims that about 650,000 U.S. physicians use it actively.
In just four years, OpenEvidence has leveraged that popularity into a $12 billion valuation, going directly to clinicians and avoiding health tech’s traditional hospital procurement process. But the company is facing competitive pressures and questions about whether its ad-based business model can continue to propel the company forward.
At the STAT Breakthrough Summit West, OpenEvidence chief technology officer Zachary Ziegler said that while the company has grown by building for individual clinicians, it’s time to aim bigger.
Continue to STAT+ to read the full story…
-------------------------------------------------
STAT News reports "from the frontiers of health and medicine".
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Healthcare security & privacy posts not related to IT or infosec are at @HIPAABot . Even so, they mix in some infosec with the legal & regulatory information..
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-
DATE: May 20, 2026 at 04:30AM
SOURCE: STAT HEALTH TECHTITLE: STAT+: OpenEvidence makes its pitch to hospitals. ‘We’re not crazy monsters’
SAN FRANCISCO — OpenEvidence rode the wave of early enthusiasm for large language models by building a free chatbot for doctors. Physicians, especially trainees, have flocked to the platform to help make patient care decisions; the company claims that about 650,000 U.S. physicians use it actively.
In just four years, OpenEvidence has leveraged that popularity into a $12 billion valuation, going directly to clinicians and avoiding health tech’s traditional hospital procurement process. But the company is facing competitive pressures and questions about whether its ad-based business model can continue to propel the company forward.
At the STAT Breakthrough Summit West, OpenEvidence chief technology officer Zachary Ziegler said that while the company has grown by building for individual clinicians, it’s time to aim bigger.
Continue to STAT+ to read the full story…
-------------------------------------------------
STAT News reports "from the frontiers of health and medicine".
Learn more at https://www.statnews.com/category/health-tech/ .
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DATE: May 19, 2026 at 10:10AM
SOURCE: STAT HEALTH TECHTITLE: STAT+: Dexcom CEO hints at future uses for CGM
URL: https://www.statnews.com/2026/05/19/dexcom-cgm-sensors-new-uses-health-tech/?utm_campaign=rss
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I’m in San Francisco for our STAT Breakthrough Summit West, where I’ll be interviewing OpenEvidence CTO Zachary Ziegler. Look forward to seeing some of you out here. Hope the rest of you will tune in online and follow coverage on Statnews.com and social channels like X and Instagram.
Continue to STAT+ to read the full story…
URL: https://www.statnews.com/2026/05/19/dexcom-cgm-sensors-new-uses-health-tech/?utm_campaign=rss
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STAT News reports "from the frontiers of health and medicine".
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DATE: May 19, 2026 at 10:10AM
SOURCE: STAT HEALTH TECHTITLE: STAT+: Dexcom CEO hints at future uses for CGM
URL: https://www.statnews.com/2026/05/19/dexcom-cgm-sensors-new-uses-health-tech/?utm_campaign=rss
You’re reading the web edition of STAT’s Health Tech newsletter, our guide to how technology is transforming the life sciences. Sign up to get it delivered in your inbox every Tuesday and Thursday.
Good morning health tech readers!
I’m in San Francisco for our STAT Breakthrough Summit West, where I’ll be interviewing OpenEvidence CTO Zachary Ziegler. Look forward to seeing some of you out here. Hope the rest of you will tune in online and follow coverage on Statnews.com and social channels like X and Instagram.
Continue to STAT+ to read the full story…
URL: https://www.statnews.com/2026/05/19/dexcom-cgm-sensors-new-uses-health-tech/?utm_campaign=rss
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STAT News reports "from the frontiers of health and medicine".
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DATE: May 19, 2026 at 10:10AM
SOURCE: STAT HEALTH TECHTITLE: STAT+: Dexcom CEO hints at future uses for CGM
URL: https://www.statnews.com/2026/05/19/dexcom-cgm-sensors-new-uses-health-tech/?utm_campaign=rss
You’re reading the web edition of STAT’s Health Tech newsletter, our guide to how technology is transforming the life sciences. Sign up to get it delivered in your inbox every Tuesday and Thursday.
Good morning health tech readers!
I’m in San Francisco for our STAT Breakthrough Summit West, where I’ll be interviewing OpenEvidence CTO Zachary Ziegler. Look forward to seeing some of you out here. Hope the rest of you will tune in online and follow coverage on Statnews.com and social channels like X and Instagram.
Continue to STAT+ to read the full story…
URL: https://www.statnews.com/2026/05/19/dexcom-cgm-sensors-new-uses-health-tech/?utm_campaign=rss
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Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
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Healthcare security & privacy posts not related to IT or infosec are at @HIPAABot . Even so, they mix in some infosec with the legal & regulatory information..
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DATE: May 19, 2026 at 10:10AM
SOURCE: STAT HEALTH TECHTITLE: STAT+: Dexcom CEO hints at future uses for CGM
URL: https://www.statnews.com/2026/05/19/dexcom-cgm-sensors-new-uses-health-tech/?utm_campaign=rss
You’re reading the web edition of STAT’s Health Tech newsletter, our guide to how technology is transforming the life sciences. Sign up to get it delivered in your inbox every Tuesday and Thursday.
Good morning health tech readers!
I’m in San Francisco for our STAT Breakthrough Summit West, where I’ll be interviewing OpenEvidence CTO Zachary Ziegler. Look forward to seeing some of you out here. Hope the rest of you will tune in online and follow coverage on Statnews.com and social channels like X and Instagram.
Continue to STAT+ to read the full story…
URL: https://www.statnews.com/2026/05/19/dexcom-cgm-sensors-new-uses-health-tech/?utm_campaign=rss
-------------------------------------------------
STAT News reports "from the frontiers of health and medicine".
Learn more at https://www.statnews.com/category/health-tech/ .
See also their complete Mastodon account at @STAT .
This robot is NOT affiliated with STAT news and merely rebroadcasts from their site. Responses posted here are not monitored.
-------------------------------------------------
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
.
Healthcare security & privacy posts not related to IT or infosec are at @HIPAABot . Even so, they mix in some infosec with the legal & regulatory information..
.
-------------------------------------------------#healthcare #healthtech #healthcaretech #healthtechnology #medgadget #medicine #doctor
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Manual glucose tracking deserves to feel first-class.
Quick entries, clear history, optional Apple Health sync, and no pressure to turn your diabetes app into a food diary, calorie tracker, or coaching system.
New on Glu Sight Resources:
https://about.glusight.app/resources/manual-glucose-tracking-without-cgm#Diabetes #BloodSugar #GlucoseTracking #iPhoneApps #HealthTech
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Manual glucose tracking deserves to feel first-class.
Quick entries, clear history, optional Apple Health sync, and no pressure to turn your diabetes app into a food diary, calorie tracker, or coaching system.
New on Glu Sight Resources:
https://about.glusight.app/resources/manual-glucose-tracking-without-cgm#Diabetes #BloodSugar #GlucoseTracking #iPhoneApps #HealthTech
-
Manual glucose tracking deserves to feel first-class.
Quick entries, clear history, optional Apple Health sync, and no pressure to turn your diabetes app into a food diary, calorie tracker, or coaching system.
New on Glu Sight Resources:
https://about.glusight.app/resources/manual-glucose-tracking-without-cgm#Diabetes #BloodSugar #GlucoseTracking #iPhoneApps #HealthTech
-
Manual glucose tracking deserves to feel first-class.
Quick entries, clear history, optional Apple Health sync, and no pressure to turn your diabetes app into a food diary, calorie tracker, or coaching system.
New on Glu Sight Resources:
https://about.glusight.app/resources/manual-glucose-tracking-without-cgm#Diabetes #BloodSugar #GlucoseTracking #iPhoneApps #HealthTech
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The Surveillance Architecture of Discovery: From Barrels to Biopolitics
How Allan Pinkerton's 19th-century spying tactics are similar to how modern medical companies collect data and patents today in May 2026.
#PinkertonAgency, #MedicalData, #PatentLaw, #HealthTech, #CorporateEspionage
https://newsletter.tf/pinkertons-spying-methods-medical-firms-2026/
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Medical companies today are using old spying methods from the 1800s to collect data. This is a big change from how things were done before.
#PinkertonAgency, #MedicalData, #PatentLaw, #HealthTech, #CorporateEspionage
https://newsletter.tf/pinkertons-spying-methods-medical-firms-2026/ -
https://www.europesays.com/ie/487872/ Billions In Prescriptions Go Unfilled. This Startup Is Using AI To Fix That. #DrugAccess #Éire #forus #GeneralCatalyst #Health #HealthCare #HealthTech #Healthcare #HealthcareAI #IE #Ireland #Oscar #PrescriptionDrugs #SahirJaggi #tandem #ThriveCapital
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Catalyst Crew Technologies launches AI-powered healthcare analytics platform targeting Latin America. The platform integrates patient data to support clinical decision-making and expand access to medical expertise in emerging markets. #HealthTech #AI
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Catalyst Crew Technologies launches AI-powered healthcare analytics platform targeting Latin America. The platform integrates patient data to support clinical decision-making and expand access to medical expertise in emerging markets. #HealthTech #AI
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Catalyst Crew Technologies launches AI-powered healthcare analytics platform targeting Latin America. The platform integrates patient data to support clinical decision-making and expand access to medical expertise in emerging markets. #HealthTech #AI