Development of a Predictive Model for Hospital-Acquired Pressure Injuries

Hospital-acquired pressure injuries are a challenge for healthcare systems, and the nurse's role is essential in their prevention. The first step is risk assessment. The development of advanced data-driven methods based on machine learning techniques can improve risk assessment through the use of routinely collected data. We studied 24 227 records from 15 937 distinct patients admitted to medical and surgical units between April 1, 2019, and March 31, 2020. Two predictive models were developed: random forest and long short-term memory neural network. Model performance was then evaluated and compared with the Braden score. The areas under the receiver operating characteristic curve, the specificity, and the accuracy of the long short-term memory neural network model (0.87, 0.82, and 0.82, respectively) were higher than those of the random forest model (0.80, 0.72, and 0.72, respectively) and the Braden score (0.72, 0.61, and 0.61, respectively). The sensitivity of the Braden score (0.88) was higher than that of long short-term memory neural network model (0.74) and the random forest model (0.73). The long short-term memory neural network model has the potential to support nurses in clinical decision-making. Implementation of this model in the electronic health record could improve assessment and allow nurses to focus on higher-priority interventions.

SCOR: A secure international informatics infrastructure to investigate COVID-19

Global pandemics call for large and diverse healthcare data to study various risk factors, treatment options, and disease progression patterns. Despite the enormous efforts of many large data consortium initiatives, scientific community still lacks a secure and privacy-preserving infrastructure to support auditable data sharing and facilitate automated and legally compliant federated analysis on an international scale. Existing health informatics systems do not incorporate the latest progress in modern security and federated machine learning algorithms, which are poised to offer solutions. An international group of passionate researchers came together with a joint mission to solve the problem with our finest models and tools. The SCOR Consortium has developed a ready-to-deploy secure infrastructure using world-class privacy and security technologies to reconcile the privacy/utility conflicts. We hope our effort will make a change and accelerate research in future pandemics with broad and diverse samples on an international scale.

Are privacy-enhancing technologies for genomic data ready for the clinic? A survey of medical experts of the Swiss HIV Cohort Study

PURPOSE

Protecting patient privacy is a major obstacle for the implementation of genomic-based medicine. Emerging privacy-enhancing technologies can become key enablers for managing sensitive genetic data. We studied physicians' attitude toward this kind of technology in order to derive insights that might foster their future adoption for clinical care.

METHODS

We conducted a questionnaire-based survey among 55 physicians of the Swiss HIV Cohort Study who tested the first implementation of a privacy-preserving model for delivering genomic test results. We evaluated their feedback on three different aspects of our model: clinical utility, ability to address privacy concerns and system usability.

RESULTS

38/55 (69%) physicians participated in the study. Two thirds of them acknowledged genetic privacy as a key aspect that needs to be protected to help building patient trust and deploy new-generation medical information systems. All of them successfully used the tool for evaluating their patients' pharmacogenomics risk and 90% were happy with the user experience and the efficiency of the tool. Only 8% of physicians were unsatisfied with the level of information and wanted to have access to the patient's actual DNA sequence.

CONCLUSION

This survey, although limited in size, represents the first evaluation of privacy-preserving models for genomic-based medicine. It has allowed us to derive unique insights that will improve the design of these new systems in the future. In particular, we have observed that a clinical information system that uses homomorphic encryption to provide clinicians with risk information based on sensitive genetic test results can offer information that clinicians feel sufficient for their needs and appropriately respectful of patients' privacy. The ability of this kind of systems to ensure strong security and privacy guarantees and to provide some analytics on encrypted data has been assessed as a key enabler for the management of sensitive medical information in the near future. Providing clinically relevant information to physicians while protecting patients' privacy in order to comply with regulations is crucial for the widespread use of these new technologies.