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Electronic Health Records

Capturing data today, to inform decision tomorrow.

Holmusk aims is to advance data-driven decision-making in mental health practice and research. Our speciality EHR system is designed to better capture and monitor decisions and is combined with past mental health records to continuously improve the longitudinal data necessary for advanced analytics.

Our Approach

Holmusk’s MindLinc is an Electronic Health Record (EHR) system built exclusively for mental health disorders.

Believing in the ability of data science to uncover insights that lead to better outcomes in mental health, Holmusk acquired MindLinc from Duke University in 2016, with the goal of making it a global leader and unlocking the convergence of innovative technology and healthcare.

Designed by leading researchers and scientists from Duke University Medical Center, the EHR platform was designed to allow more contact time with patients by reducing administrative burden, and enable research and clinical decision support.

For the past 20 years, MindLinc has served as the new age EHR for behavioral sciences, making it one of the world’s largest behavioral health clinical databases, not only in the amount of data collected but also in the scope of mental health issues covered. Specifically designed to advance data-driven decision-making in mental health practice and research, Holmusk is creating the largest and most comprehensive structured mental health database, and working to provide regulatory-grade and clinical research-grade real world evidence datasets.


As a specialty mental health EHR, MindLinc uses the widely accepted assessment tool, clinical global impression scale (CGI-S).

This rating scale, which is a measure of symptom severity and treatment responsiveness, is fundamental to providing insights into disease severity and determining appropriate course of treatments for patients.

MindLinc is uniquely programmed to collect CGI-S and other specific mental illness data in a robust manner that is validated, harmonized and standardized. It uses the CGI-S score to form a longitudinal patient trajectory, by making comparisons amongst patients with similar profiles, and ultimately provide a holistic view of the patient's medical history.

It also delivers enhanced value by capturing significant data related to mood and cognitive impairment. The MindLinc database is the largest RWD on Mental Health:

  • 50 MILLION

    Rows of data with patient features with over 500,000 patients and 42 million rows of patient diagnoses history

  • 68 MILLION

    Rows of medications data and over 15 million rows of medication side effects


    Qualified clinical visits collected over 17 years and 3 million rows of data featuring the Psychiatric Review of Systems (ROS)

  • 22 MILLION

    Rows of substance addiction data and 10 million rows of allergies data

  • 13 MILLION

    Rows of data with Global Assessment of Functioning (GAF) score and 13 million rows of Clinical Global Impression data

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Research Based on MindLinc


Tolerability and Effectiveness of Lamotrigine and Valproic Acid for Bipolar Disorder in a Real-World Treatment Setting.

Clinical and Economic Outcomes Comparison of Schizophrenics Treated with Oral Antipsychotics vs. INVEGA® SUSTENNA® (Paliperidone Palmitate).


Analysis of psychiatric outcomes related to schizoaffective disorder and compares them to those of schizophrenia and bipolar disorder from the MindLinc repository.

Analysis of psychiatric outcomes related to schizoaffective disorder from a long-term data repository.


Treatment patterns and clinical outcomes associated with bipolar and schizophrenic patients on Lurasidone.


Use of Augmentation Agents for Treating Depression: Analysis of a Psychiatric Electronic Medical Record Data Set.

Treatment patterns and outcomes associated with antipsychotic treatment in patients with schizophrenia.


Efficacy of Mood Stabilizers (valproate, lamotrigine) vs. Atypical Anti-Psychotics (olanzapine) in the Treatment of Bipolar I Disorder and With and Without Co-Morbid Substance Abuse.


Population based Predictors of Safety and Response to escitalopram, and other SSRI’s and SNRI’s.

Comparison of Effectiveness and Tolerability in Patients with Major Depression Receiving Venlafaxine and Escitalopram in a Naturalistic Clinical Care Setting.


Identify patients who are likely responders to the treatment of aripiprazole and distinguish them from non-responders to aripiprazole.


Improving Clinical Performance for the Care of People with Adult Major Depressive Disorder.

Analyses of Psychiatric Outcomes from a Long- Term Data Repository: Dosage and Outcomes Comparisons for Citalopram, Escitalopram, and Sertraline.


Evaluation of Growth, Sexual Maturation, and Prolactin-related Adverse Events in the Pediatric Population Exposed to Atypical Antipsychotic Drugs (comparison of Prolactin to other drugs).