SHAARPEC approach in research

Real-world evidence research made possible through SHAARPEC.

  • With all clinical data gathered in one platform, SHAARPEC enables real-world evidence studies across a population rather than a sample of patients. 
  • Research and benchmarking are made feasible by applying several techniques compliant with GDPR and HIPAA. The data never leaves the care premises, while the query moves from one SHAARPEC-platform to the next. Hence, from one population to the next. 

Articles of interest within information-driven care

Nikolentzos, G., Vazirgiannis, M., Xypolopoulos, C., Lingman, M., Brandt, E. (2023). Synthetic electronic health records generated with variational graph autoencoders. npj Digit. Med. 6, 83 (2023).

Jendle J, Agvall B, Galozy A, Adolfsson P. (2022). Patterns and Predictors Associated With Long-Term Glycemic Control in Pediatric and Young Adult Patients with Type 1 Diabetes. J Diabetes Sci Technol. 2022 May 12:19322968221096423.

Wibring, K., Lingman, M., Herlitz, J., Ashfaq, A., Bång, A. (2022). Development of a prehospital prediction model for risk stratification of patients with chest pain. The American Journal of Emergency Medicine, 51.

Petersson, L., Larsson, I., Nygren J., Nilsen, P., Neher, M., Reed, J., Tyskbo, D., Svedberg, P. (2022). Challenges to implementing artificial intelligence in healthcare: a qualitative interview study with healthcare leaders in Sweden. BMC Health Services Research.

Svedberg, P., Reed, J., Nilsen, P., Barlow, J., Macrae, C., Nygren J. (2022). Toward Successful Implementation of Artificial Intelligence in Health Care Practice: Protocol for a Research Program. JMIR Research Protocols

Etminani, K., Göransson, C., Galozy, A., Pejner, M. N., & Nowaczyk, S. (2021). Improving Medication Adherence Through Adaptive Digital Interventions (iMedA) in Patients With Hypertension: Protocol for an Interrupted Time Series Study. JMIR Research Protocols, 10(5), e24494.

Tolestam Heyman, E., Ashfaq, A., Khoshnood, A., Ohlsson, M., Ekelund U., Dahlén Holmqvist, L., Lingman, M. (2021). Improving Machine Learning 30-Day Mortality Prediction by Discounting Surprising Deaths. The Journal of Emergency Medicine, 61 (6). 

Wibring, K., Lingman, M., Herlitz, J., Blom, L., Serholt Gripestam, O., Bång, A. (2021). Guideline adherence among prehospital emergency nurses when caring for patients with chest pain: a prospective cohort study. Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine.

Galozy, A., Nowacyk, S., Sant’Anna, A., Ohlsson, M., Lingman, M. (2020). Pitfalls of medication adherence approximation through EHR and pharmacy records: Definitions, data and computation. International Journal of Medical Informatics, 136.

Tolestam Heyman, E., Engström, M., Baigi, A., Dahlén Holmqvist, L., Lingman, M. (2020). Likelihood of admission to hospital from the emergency department is not universally associated with hospital bed occupancy at the time of admission. Int J Health Plann Manage.

Yasin, Z., Anderson, P., Lingman, M., Kwatra, J., Ashfaq, A., Slutzman, J., Agvall, B. (2020). Receiving care according to national heart failure guidelines is associated with lower total costs: an observational study in Region Halland, Sweden. European Heart Journal – Quality of Care and Clinical Outcomes, 7 (3).

Galozy, A., Nowacyk, S. (2020). Prediction and pattern analysis of medication refill adherence through electronic health records and dispensation data. Journal of Biomedical Informatics.

Blom, M, Ashfaq, A., Sant’Anna, A., Anderson, P., Lingman, M. (2019). Training machine learning models to predict 30-day mortality in patients discharged from the emergency department: a retrospective, population-based registry study. BMJ Open, 9 (8).

Ashfaq, A., Sant’Anna, A., Lingman, M., & Nowaczyk, S. (2019). Readmission prediction using deep learning on electronic health records. Journal of biomedical informatics, 97, 103256.

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