Do data security measures, privacy regulations, and communication standards impact the interoperability of patient health information? A cross-country investigation.

BACKGROUND: The lack of interoperability is one of the biggest obstacles to the complete digitalization of patient health information in electronic medical records (EMR). The high volume of data breaches has put pressure on care providers to adopt data protection measures to remain compliant with legal requirements. Extreme data protection measures can impede information flow, but they also instill confidence in secure information sharing. This study investigates how the adoption of security measures, privacy regulations, and communication standards has impacted patient health information interoperability at technical (TI), semantic (SI), and organizational (OI) levels within the hospitals. METHODS: The study utilizes a quasi-experimental research design to probe the relationships of interest. Secondary data from a survey of randomly selected 773 hospitals conducted by the European Commission in over 30 countries in Europe is used to understand the relationships. The study counters selection bias and accounts for systematic differences in adopting treatments of interest in the hospitals using the propensity score-based approaches for the observational data. RESULTS: The empirical models that account for selection bias explain more observational data variations than those that did not. Access control measures on workstations are linked to 44 % lesser odds of experiencing TI problems. However, hospitals with regional and organizational level privacy regulations have 85 % and 76 % higher odds of experiencing SI and OI problems, respectively. On the other hand, hospitals with a single hospital-wide EMR are 53 % and 43 % less likely to experience TI and SI problems, respectively, in comparison to those with multiple EMR systems. CONCLUSION: The study highlights the differential impacts of data protection measures on the hospitals' three key types of interoperability problems (i.e., TI, SI, and OI). Homogenous EMR systems type and substantial investment in technology are critical to supporting health information interoperability within the hospitals. The study findings inform policy considerations for improving specific aspects of health information's interoperability while preserving patient data privacy and security.

Machine learning for psychiatric patient triaging: an investigation of cascading classifiers.

Objective: Develop an approach, One-class-at-a-time, for triaging psychiatric patients using machine learning on textual patient records. Our approach aims to automate the triaging process and reduce expert effort while providing high classification reliability. Materials and Methods: The One-class-at-a-time approach is a multistage cascading classification technique that achieves higher triage classification accuracy compared to traditional multiclass classifiers through 1) classifying one class at a time (or stage), and 2) identification and application of the highest accuracy classifier at each stage. The approach was evaluated using a unique dataset of 433 psychiatric patient records with a triage class label provided by "I2B2 challenge," a recent competition in the medical informatics community. Results: The One-class-at-a-time cascading classifier outperformed state-of-the-art classification techniques with overall classification accuracy of 77% among 4 classes, exceeding accuracies of existing multiclass classifiers. The approach also enabled highly accurate classification of individual classes-the severe and mild with 85% accuracy, moderate with 64% accuracy, and absent with 60% accuracy. Discussion: The triaging of psychiatric cases is a challenging problem due to the lack of clear guidelines and protocols. Our work presents a machine learning approach using psychiatric records for triaging patients based on their severity condition. Conclusion: The One-class-at-a-time cascading classifier can be used as a decision aid to reduce triaging effort of physicians and nurses, while providing a unique opportunity to involve experts at each stage to reduce false positive and further improve the system's accuracy.