Our Mission & Values
Our Mission: To advance patient care, improve outcomes, and drive down the
cost curve by lending a helping hand to clinicians.
cost curve by lending a helping hand to clinicians.
"Data analytics is of deep interest to every stakeholder in health care."
(New England Journal of Medicine Catalyst, 2018)
The national healthcare landscape is becoming increasingly challenging due to complex conditions, an intricate regulatory framework and growing patient needs. Facilities facing these hurdles incur both patient health and economic costs at staggering rates, as clinicians and administrators pool resources to respond in kind.
We aim to harness one cornerstone resource - data and its analytics - to arm care providers with analysis, explanations and foresight to enhance their continuing efforts to provide effective, patient-centric care. Indeed, as facility data systems become increasingly available, accurate, complete and accessible, we see an opportunity to combine the computational power of machine learning and the thoughtfulness of clinical domain knowledge to accelerate practices for allocating human and monetary resources in high-risk patient care.
(New England Journal of Medicine Catalyst, 2018)
The national healthcare landscape is becoming increasingly challenging due to complex conditions, an intricate regulatory framework and growing patient needs. Facilities facing these hurdles incur both patient health and economic costs at staggering rates, as clinicians and administrators pool resources to respond in kind.
We aim to harness one cornerstone resource - data and its analytics - to arm care providers with analysis, explanations and foresight to enhance their continuing efforts to provide effective, patient-centric care. Indeed, as facility data systems become increasingly available, accurate, complete and accessible, we see an opportunity to combine the computational power of machine learning and the thoughtfulness of clinical domain knowledge to accelerate practices for allocating human and monetary resources in high-risk patient care.
OUR VALUES
1
Patients First: the patient journey and provider experience are the source of
our motivation.
2
We focus on building technology that advances the delivery, quality and efficacy of care.
3
We do not believe in the “black box” model of machine learning. Our products emphasize explanations of each result and usability in a clinical setting.
4
Data science is an ever-changing and growing field, and we aim to continue developing products that push the boundaries of clinical science and technology.
5
We view every business opportunity as a partnership.
Patients First: the patient journey and provider experience are the source of
our motivation.
2
We focus on building technology that advances the delivery, quality and efficacy of care.
3
We do not believe in the “black box” model of machine learning. Our products emphasize explanations of each result and usability in a clinical setting.
4
Data science is an ever-changing and growing field, and we aim to continue developing products that push the boundaries of clinical science and technology.
5
We view every business opportunity as a partnership.
PUBLICATIONS
Mahajan AS & Mahajan SM. Deep Learning Methods and Their Application to Nursing Workflows. CIN: Computers, Informatics, Nursing. Volume 39, 2021, Issue 1, Pages 1-6. doi: 10.1097/CIN.0000000000000702.
Mahajan SM, Nguyen C, Bui J, Kunde E, Abbott BT, & Mahajan AS. Risk Factors for Readmission After Knee Arthroplasty Based on Predictive Models: A Systematic Review. Arthroplasty Today, Volume 6, Issue 3, 2020, Pages 390-404, ISSN 2352-3441. doi: https://doi.org/10.1016/j.artd.2020.04.017.
Cai S, Bakerjian B, Bang H, Mahajan SM, Ota D, & Kiratli J. Data acquisition process for VA and non-VA emergency department and hospital utilization by veterans with spinal cord injury and disorders in California using VA and state data. The Journal of Spinal Cord Medicine. DOI: 10.1080/10790268.2020.1773028
Mahajan SM, Mahajan AM, Nguyen C, Bui J, Abbott BT, & Osborne TF (2020). Predictive models for identifying risk of readmission after index hospitalization for hip arthroplasty: A systematic review. Journal of Orthopaedics, Volume 22, Pages 73-85. doi:10.1016/j.jor.2020.03.045
Mahajan SM, & Ghani R. (2019a). Combining Structured and Unstructured Data for Predicting Risk of Readmission for Heart Failure Patients. Stud Health Technol Inform, 264, 238-242. doi:10.3233/shti190219
Mahajan SM, & Ghani R. (2019b). Using Ensemble Machine Learning Methods for Predicting Risk of Readmission for Heart Failure. Stud Health Technol Inform, 264, 243-247. doi:10.3233/shti190220
Liu L, Fillipucci-Arnold D, Mahajan SM. (2019). Quantitative Analyses of the Effectiveness of a Newly Designed Preceptor Workshop. Journal for Nurses in Professional Development, (35)3, 144-151, doi: 10.1097/NND.0000000000000528.
Mahajan SM, Mahajan A, Burman P, Heidenreich P. (2019). Can We Do More with Less While Building Predictive Models? A Study in Parsimony of Risk Models for Predicting Heart Failure Readmissions. CIN: Computers, Informatics, Nursing, (37)6, 306-314, doi: 10.1097/CIN.0000000000000499.
Mahajan SM, Heidenreich P, Abbott B, Newton A, Ward D. (2018). Predictive models for identifying risk of readmission after index hospitalization for heart failure: A systematic review. European Journal of Cardiovascular Nursing, (17)8, 675-689, doi:10.1177/1474515118799059.
Mahajan SM, Mahajan A, Negahban S. (2018). Regional Differences in Predicting Risk of 30-day Readmissions for Heart Failure. Studies in Health Informatics and Technology, 250: 245-249, doi: 10.3233/978-1-61499-872-3-245.
Mahajan SM, Mahajan A, King R, Negahban S. (2018). Predicting Risk of 30-day Readmissions Using Two Emerging Machine Learning Methods. Studies in Health Informatics and Technology, 250: 250-255, doi: 10.3233/978-1-61499-872-3-250.
Mahajan SM, Burman P, Newton A, Heidenreich P. (2017). A Validated Risk Model for 30-day Readmission for Heart Failure. Studies in Health Informatics and Technology, 245: 506-510, doi: 10.3233/978-1-61499-830-3-506.
Kim KK, Mahajan SM, Miller J, Selby JV. (2017). Answering Research Questions with National Clinical
Research Networks. In: Delaney CW, Weaver CA, Warren JJ, Clancy TR, Simpson RL, eds. Big Data-Enabled Nursing Education, Research and Practice (Health Informatics). Cham, Switzerland: Springer, 211-226, doi: 10.1007/978-3-319-53300-1_11.
Mahajan SM, Burman P, Hogarth MA. (2016). Analyzing 30-day Readmission Rate for Heart Failure Using Different Predictive Models. Studies in Health Informatics and Technology, 225: 143-147, doi: 10.3233/978-1-61499-658-3-143.
Mudumbai SC, Wagner T, Mahajan SM, King R, Heidenreich PA, Hlatky M, Wallace A, Mariano ER. (2014). Effectiveness of preoperative beta-blockade on intra-operative heart rate in vascular surgery cases conducted under regional or local anesthesia. Springerplus, 3:227, doi: 10.1186/2193-1801-3-227.
Mudumbai S, Wagner T, Mahajan SM, King R, Heidenreich P, Hlatky M, Wallace A, Mariano ER. (2012). Vascular Surgery Patients Prescribed Preoperative β-blockers Experienced a Decrease in the Maximal Heart Rate Observed during Induction of General Anesthesia. Journal of Cardiothoracic and Vascular Anesthesia, (26)3, 414-419, doi: 10.1053/j.jvca.2011.09.027.
Mahajan SM, Nguyen C, Bui J, Kunde E, Abbott BT, & Mahajan AS. Risk Factors for Readmission After Knee Arthroplasty Based on Predictive Models: A Systematic Review. Arthroplasty Today, Volume 6, Issue 3, 2020, Pages 390-404, ISSN 2352-3441. doi: https://doi.org/10.1016/j.artd.2020.04.017.
Cai S, Bakerjian B, Bang H, Mahajan SM, Ota D, & Kiratli J. Data acquisition process for VA and non-VA emergency department and hospital utilization by veterans with spinal cord injury and disorders in California using VA and state data. The Journal of Spinal Cord Medicine. DOI: 10.1080/10790268.2020.1773028
Mahajan SM, Mahajan AM, Nguyen C, Bui J, Abbott BT, & Osborne TF (2020). Predictive models for identifying risk of readmission after index hospitalization for hip arthroplasty: A systematic review. Journal of Orthopaedics, Volume 22, Pages 73-85. doi:10.1016/j.jor.2020.03.045
Mahajan SM, & Ghani R. (2019a). Combining Structured and Unstructured Data for Predicting Risk of Readmission for Heart Failure Patients. Stud Health Technol Inform, 264, 238-242. doi:10.3233/shti190219
Mahajan SM, & Ghani R. (2019b). Using Ensemble Machine Learning Methods for Predicting Risk of Readmission for Heart Failure. Stud Health Technol Inform, 264, 243-247. doi:10.3233/shti190220
Liu L, Fillipucci-Arnold D, Mahajan SM. (2019). Quantitative Analyses of the Effectiveness of a Newly Designed Preceptor Workshop. Journal for Nurses in Professional Development, (35)3, 144-151, doi: 10.1097/NND.0000000000000528.
Mahajan SM, Mahajan A, Burman P, Heidenreich P. (2019). Can We Do More with Less While Building Predictive Models? A Study in Parsimony of Risk Models for Predicting Heart Failure Readmissions. CIN: Computers, Informatics, Nursing, (37)6, 306-314, doi: 10.1097/CIN.0000000000000499.
Mahajan SM, Heidenreich P, Abbott B, Newton A, Ward D. (2018). Predictive models for identifying risk of readmission after index hospitalization for heart failure: A systematic review. European Journal of Cardiovascular Nursing, (17)8, 675-689, doi:10.1177/1474515118799059.
Mahajan SM, Mahajan A, Negahban S. (2018). Regional Differences in Predicting Risk of 30-day Readmissions for Heart Failure. Studies in Health Informatics and Technology, 250: 245-249, doi: 10.3233/978-1-61499-872-3-245.
Mahajan SM, Mahajan A, King R, Negahban S. (2018). Predicting Risk of 30-day Readmissions Using Two Emerging Machine Learning Methods. Studies in Health Informatics and Technology, 250: 250-255, doi: 10.3233/978-1-61499-872-3-250.
Mahajan SM, Burman P, Newton A, Heidenreich P. (2017). A Validated Risk Model for 30-day Readmission for Heart Failure. Studies in Health Informatics and Technology, 245: 506-510, doi: 10.3233/978-1-61499-830-3-506.
Kim KK, Mahajan SM, Miller J, Selby JV. (2017). Answering Research Questions with National Clinical
Research Networks. In: Delaney CW, Weaver CA, Warren JJ, Clancy TR, Simpson RL, eds. Big Data-Enabled Nursing Education, Research and Practice (Health Informatics). Cham, Switzerland: Springer, 211-226, doi: 10.1007/978-3-319-53300-1_11.
Mahajan SM, Burman P, Hogarth MA. (2016). Analyzing 30-day Readmission Rate for Heart Failure Using Different Predictive Models. Studies in Health Informatics and Technology, 225: 143-147, doi: 10.3233/978-1-61499-658-3-143.
Mudumbai SC, Wagner T, Mahajan SM, King R, Heidenreich PA, Hlatky M, Wallace A, Mariano ER. (2014). Effectiveness of preoperative beta-blockade on intra-operative heart rate in vascular surgery cases conducted under regional or local anesthesia. Springerplus, 3:227, doi: 10.1186/2193-1801-3-227.
Mudumbai S, Wagner T, Mahajan SM, King R, Heidenreich P, Hlatky M, Wallace A, Mariano ER. (2012). Vascular Surgery Patients Prescribed Preoperative β-blockers Experienced a Decrease in the Maximal Heart Rate Observed during Induction of General Anesthesia. Journal of Cardiothoracic and Vascular Anesthesia, (26)3, 414-419, doi: 10.1053/j.jvca.2011.09.027.
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