Clinical Decision Support Functions and Digitalization of Clinical Documents of Electronic Medical Record Systems

Exploring maturity of electronic medical record use among allied health professionals

BACKGROUND

Electronic medical records (EMRs) have the potential to improve and streamline the quality and safety of patient care. Harnessing the full benefits of EMR implementation depends on the utilisation of advanced features, defined as "mature usage." At present, little is known about the maturity of EMR usage by allied health professionals (AHPs).

OBJECTIVE

To examine current maturity of EMR use by AHPs and explore perceived barriers to mature EMR utilisation and optimisation.

METHOD

AHPs were recruited from three health services. Participants completed a 27-question electronic questionnaire based on the EMR Adoption Framework, which measures clinician EMR utilisation (0 = paper chart, 5 = theoretical maximum) across 10 EMR feature categories. Interviews were conducted with both clinicians and managers to explore the nature of current EMR utilisation and perceived facilitators and barriers to mature usage.

RESULTS

Questionnaire responses were obtained from 193 participants AHPs. The majority of questions (74%) showed a mean score of <3, indicating a lack of mature EMR use. Pockets of mature usage were identified in the categories of health information, referrals and administration processes. Interviews with 21 clinicians and managers revealed barriers to optimisation across three themes: (1) limited understanding of EMR opportunities; (2) complexity of the EMR change process and (3) end-user and environmental factors.

CONCLUSION

Mature usage across EMR feature categories of the EMR Adoption Framework was low. However, questionnaire and qualitative interview data suggested pockets of mature utilisation.

IMPLICATIONS

Achieving mature allied health EMR use will require strategies implemented at the clinician, EMR support, and service levels.

Digitization in Everyday Nursing Care: A Vignette Study in German Hospitals

(1) Background: Digitization in hospital nursing promises to transform the organization of care processes and, therefore, provide relief to nurse staffing shortages. While technological solutions are advanced and application fields numerous, comprehensive implementation remains challenging. Nursing leadership is crucial to digital change processes. This vignette study examined the effects of the motives and values on nurses' motivation to use innovative technologies. (2) Methods: We asked hospital nurses in an online vignette study to assess a fictitious situation about the introduction of digital technology. We varied the devices on the degree of novelty (tablet/smart glasses), addressed motives (intrinsic/extrinsic), and values (efficiency/patient orientation). (3) Results: The analysis included 299 responses. The tablet vignettes caused more motivation than those of the smart glasses (Z = -6.653, p < 0.001). The dataset did not show significant differences between intrinsic and extrinsic motives. The nursing leader was more motivating when emphasizing efficiency rather than patient orientation (Z = -2.995, p = 0.003). (4) Conclusions: The results suggest efficiency as a motive for using known digital technologies. The nursing staff's willingness to use digital technology is generally high. Management actions can provide a structural framework and training so that nursing leaders can ensure their staff's engagement in using also unknown devices.

Protected Health Information Recognition by Fine-Tuning a Pre-training Transformer Model

Objectives: De-identifying protected health information (PHI) in medical documents is important, and a prerequisite to deidentification is the identification of PHI entity names in clinical documents. This study aimed to compare the performance of three pre-training models that have recently attracted significant attention and to determine which model is more suitable for PHI recognition. Methods: We compared the PHI recognition performance of deep learning models using the i2b2 2014 dataset. We used the three pre-training models—namely, bidirectional encoder representations from transformers (BERT), robustly optimized BERT pre-training approach (RoBERTa), and XLNet (model built based on Transformer-XL)—to detect PHI. After the dataset was tokenized, it was processed using an inside-outside-beginning tagging scheme and WordPiecetokenized to place it into these models. Further, the PHI recognition performance was investigated using BERT, RoBERTa, and XLNet. Results: Comparing the PHI recognition performance of the three models, it was confirmed that XLNet had a superior F1-score of 96.29%. In addition, when checking PHI entity performance evaluation, RoBERTa and XLNet showed a 30% improvement in performance compared to BERT. Conclusions: Among the pre-training models used in this study, XLNet exhibited superior performance because word embedding was well constructed using the two-stream self-attention method. In addition, compared to BERT, RoBERTa and XLNet showed superior performance, indicating that they were more effective in grasping the context.

Reducing severe cutaneous adverse and type B adverse drug reactions using pre-stored human leukocyte antigen genotypes

BACKGROUND

Several type B adverse drug reactions (ADRs), especially severe cutaneous adverse reactions (SCARs), are associated with particular human leukocyte antigen (HLA) genotypes. However, pre-stored HLA information obtained from other clinical workups has not been used to prevent ADRs. We aimed to simulate the preemptive use of pre-stored HLA information in electronic medical records to evaluate whether this information can prevent ADRs.

METHODS

We analyzed the incidence and the risk of ADRs for selected HLA alleles (HLA-B*57:01, HLA-B*58:01, HLA-A*31:01, HLA-B*15:02, HLA-B*15:11, HLA-B*13:01, HLA-B*59:01, and HLA-A*32:01) and seven drugs (abacavir, allopurinol, carbamazepine, oxcarbazepine, dapsone, methazolamide, and vancomycin) using pre-stored HLA information of transplant patients based on the Pharmacogenomics Knowledge Base guidelines and experts' consensus.

RESULTS

Among 11,988 HLA-tested transplant patients, 4092 (34.1%) had high-risk HLA alleles, 4583 (38.2%) were prescribed risk drugs, and 580 (4.8%) experienced type B ADRs. Patients with HLA-B*58:01 had a significantly higher incidence of type B ADR and SCARs associated with allopurinol use than that of patients without HLA-B*58:01 (17.2% vs. 11.9%, odds ratio [OR] 1.53 [95% confidence interval {CI} 1.09-2.13], p = 0.001, 2.3% versus 0.3%, OR 7.13 [95% CI 2.19-22.69], p < 0.001). Higher risks of type B ADR and SCARs were observed in patients taking carbamazepine or oxcarbazepine if they had one of HLA-A*31:01, HLA-B*15:02, or HLA-B*15:11 alleles. Vancomycin and dapsone use in HLA-A*32:01 and HLA-B*13:01 carriers, respectively, showed trends toward increased risk of type B ADRs.

CONCLUSION

Utilization of pre-stored HLA data can prevent type B ADRs including SCARs by screening high-risk patients.

Physician Knowledge Base: Clinical Decision Support Systems

With the introduction of electronic medical records (EMRs), it has become possible to accumulate massive amounts of qualitative medical data. As such, EMRs have become increasingly used in clinical decision support systems (CDSSs). While CDSSs aim to reduce medical errors normally occurring in the process of treating patients by physicians, technical maturity and the completeness of CDSSs do not meet standards for medical use yet. As data further accumulates, CDSS algorithms must be continuously updated to allow CDSSs to perform their core functions. Doing so, however, requires extensive time and manpower investments. In current practice, computational systems already perform a wide variety of functions in medical settings to allow medical staff to focus on other tasks. However, no prior research has evaluated the potential effectiveness of future CDSSs nor analyzed possibilities for their further development. In this article, we evaluate CDSS technology with the consideration that medical staff also understand the core functions of such systems.

Filtered BERT: Similarity Filter-Based Augmentation with Bidirectional Transfer Learning for Protected Health Information Prediction in Clinical Documents

For the secondary use of clinical documents, it is necessary to de-identify protected health information (PHI) in documents. However, the difficulty lies in the fact that there are few publicly annotated PHI documents. To solve this problem, in this study, we propose a filtered bidirectional encoder representation from transformers (BERT)-based method that predicts a masked word and validates the word again through a similarity filter to construct augmented sentences. The proposed method effectively performs data augmentation. The results show that the augmentation method based on filtered BERT improved the performance of the model. This suggests that our method can effectively improve the performance of the model in the limited data environment.

Big data simulations for capacity improvement in a general ophthalmology clinic

Purpose

Long total waiting times (TWT) experienced by patients during a clinic visit have a significant adverse effect on patient’s satisfaction. Our aim was to use big data simulations of a patient scheduling calendar and its effect on TWT in a general ophthalmology clinic. Based on the simulation, we implemented changes to the calendar and verified their effect on TWT in clinical practice.

Design and methods

For this retrospective simulation study, we generated a discrete event simulation (DES) model based on clinical timepoints of 4.401 visits to our clinic. All data points were exported from our clinical warehouse for further processing. If not available from the electronic health record, manual time measurements of the process were used. Various patient scheduling models were simulated and evaluated based on their reduction of TWT. The most promising model was implemented into clinical practice in 2017.

Results

During validation of our simulation model, we achieved a high agreement of mean TWT between the real data (229 ± 100 min) and the corresponding simulated data (225 ± 112 min). This indicates a high quality of the simulation model. Following the simulations, a patient scheduling calendar was introduced, which, compared with the old calendar, provided block intervals and extended time windows for patients. The simulated TWT of this model was 153 min. After implementation in clinical practice, TWT per patient in our general ophthalmology clinic has been reduced from 229 ± 100 to 183 ± 89 min.

Conclusion

By implementing a big data simulation model, we have achieved a cost-neutral reduction of the mean TWT by 21%. Big data simulation enables users to evaluate variations to an existing system before implementation into clinical practice. Various models for improving patient flow or reducing capacity loads can be evaluated cost-effectively.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00417-020-05040-9.

Association of the Magnitude of Nurses With the Use of Health Information Exchanges: Analyzing the National Health Insurance Claim Data of Hospitals and Clinics in Korea

BACKGROUND

Many features of health care organizations (HCOs) have been identified to be associated with health information exchange (HIE), but subcategories of organizational factors focusing on nurse workforces still need to be identified. The objective of this study is to investigate the association of number of nurses with HIE use in Korea.

METHODS

This study had a retrospective study design and used health insurance claim data from June 1, 2016 to June 30, 2018. The unit of analysis was the HCO, and any health insurance claims having HIE were counted by HCO. There were a total of 1490 HCOs having any HIE and 24 026 HCOs not having HIE. For statistical analysis, two-part model was used: logistic regression for HIE participation and the generalized linear model for the volume of HIE use.

RESULTS

HIE was used by 44.6% of general hospitals, and 8.6% and 5.3% of small hospitals and clinics, respectively. Both HIE use and its volume were significantly positively associated with nurse variables. The use of HIE was significantly positively associated with nurse-to-bed ratio in general hospitals (OR 1.028; 1.016 to 1.041) and in small hospitals (OR 1.021; 1.016 to 1.027), and with the number of nurses (OR 1.041; 1.028 to 1.054) in clinics (P<.001). The volume of HIE use was also positively associated with nurse-to-bed ratio in general hospitals (OR 1.010; 1.004 to 1.017) and in small hospitals (OR 1.014; 1.006 to 1.022), and with the number of nurses (OR 1.055; 1.037 to 1.073) in clinics (P<.01).

CONCLUSION

This study found that there was a low rate of HIE use in small hospitals and clinics. The number of nurses was critically associated with the use of HIE and the volume of HIE claims. HIE policy makers need to be aware of this factor in seeking to accelerate HIE.

Using active information and communication technology for elderly homecare services: A scoping review

Nowadays, as life expectancy grows, the healthcare industry faces growing challenges related to corresponding increases in chronic diseases. Home care services (HCS) are the solution to this growing problem. It's a general premise that information and communication technology (ICT) can address these health issues and enhances HCS. The scope of our study was the active managerial and supervisory roles of these technologies within HCS. The study aimed to extract, accumulate, and classify the challenges of using active ICT for elderly HCS. We employed the keywords, their synonyms, and their combinations into the searching areas of title, keywords, and abstract. More than 300 resources were collected, and found those 33 articles of those 33 articles were eligible for our study. Later, a team of experts provided their opinions on our gatherings, which were collected individually. According to the expert team's opinions, researchers classified challenges into; technology, human factors, and management.