Electronic Health Records

Hoerbst, Alexander; Ammenwerth, Elske

doi:10.3414/me10-01-0038

Cited by 157 publications

(17 citation statements)

References 94 publications

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“…Furthermore, in this study we have not considered any information from the patients' electronic health records (EHRs) [32], such as diagnoses or additional history. Considering the multi-factorial aetiology of falls [33], our sensor-based information may well be used in combination with resp.…”

Section: Discussionmentioning

confidence: 99%

Sensors vs. experts - A performance comparison of sensor-based fall risk assessment vs. conventional assessment in a sample of geriatric patients

Marschollek

Rehwald

Wolf

et al. 2011

BMC Med Inform Decis Mak

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BackgroundFall events contribute significantly to mortality, morbidity and costs in our ageing population. In order to identify persons at risk and to target preventive measures, many scores and assessment tools have been developed. These often require expertise and are costly to implement. Recent research investigates the use of wearable inertial sensors to provide objective data on motion features which can be used to assess individual fall risk automatically. So far it is unknown how well this new method performs in comparison with conventional fall risk assessment tools. The aim of our research is to compare the predictive performance of our new sensor-based method with conventional and established methods, based on prospective data.MethodsIn a first study phase, 119 inpatients of a geriatric clinic took part in motion measurements using a wireless triaxial accelerometer during a Timed Up&Go (TUG) test and a 20 m walk. Furthermore, the St. Thomas Risk Assessment Tool in Falling Elderly Inpatients (STRATIFY) was performed, and the multidisciplinary geriatric care team estimated the patients' fall risk. In a second follow-up phase of the study, 46 of the participants were interviewed after one year, including a fall and activity assessment. The predictive performances of the TUG, the STRATIFY and team scores are compared. Furthermore, two automatically induced logistic regression models based on conventional clinical and assessment data (CONV) as well as sensor data (SENSOR) are matched.ResultsAmong the risk assessment scores, the geriatric team score (sensitivity 56%, specificity 80%) outperforms STRATIFY and TUG. The induced logistic regression models CONV and SENSOR achieve similar performance values (sensitivity 68%/58%, specificity 74%/78%, AUC 0.74/0.72, +LR 2.64/2.61). Both models are able to identify more persons at risk than the simple scores.ConclusionsSensor-based objective measurements of motion parameters in geriatric patients can be used to assess individual fall risk, and our prediction model's performance matches that of a model based on conventional clinical and assessment data. Sensor-based measurements using a small wearable device may contribute significant information to conventional methods and are feasible in an unsupervised setting. More prospective research is needed to assess the cost-benefit relation of our approach.

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Section: Discussionmentioning

confidence: 99%

Sensors vs. experts - A performance comparison of sensor-based fall risk assessment vs. conventional assessment in a sample of geriatric patients

Marschollek

Rehwald

Wolf

et al. 2011

BMC Med Inform Decis Mak

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“…4 There is evidence of significant potential for better quality and safety in health care deriving from the adoption of EHRs. 1,5,6 For example, in a study of 58 US hospitals over a 3 year period (2008-2011), EHR use was associated with 1.6% increase in overall health-care service productivity. 7 Another, nationally representative, study of physicians (n = 1727) in the USA reported great clinical benefits from EHRs.…”

Section: Introductionmentioning

confidence: 99%

Satisfaction of health‐care providers with electronic health records and perceived barriers to its implementation in the United Arab Emirates

Bani‐Issa

Al‐Yateem

Makhzoomy

et al. 2016

Int J of Nursing Practice

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The integration of electronic health records (EHRs) has shown promise in improving health-care quality. In the United Arab Emirates, EHRs have been recently adopted to improve the quality and safety of patient care. A cross-sectional survey of 680 health-care providers (HCPs) was conducted to assess the satisfaction of HCPs in the United Arab Emirates with EHRs' impact on access/viewing, documentation and medication administration and to explore the barriers encountered in their use. Data were collected over 6 months from April to September 2014. High overall satisfaction with EHRs was reported by HCPs, suggesting their acceptance. Physicians reported the greatest overall satisfaction with EHRs, although nurses showed significantly higher satisfaction with the impact on medication administration compared with other HCPs. The most significant barriers reported by nurses were lack of belief in the value of EHRs for patients and lack of adequate computer skills. Given the large investment in technology, additional research is necessary to promote the full utilization of EHRs. Nurses need to be aware of the value of EHRs for patient care and be involved in all stages of EHR implementations to maximize its meaningful use for better clinical outcomes.

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“…These aspects will have direct impact in data redundancy, granularity, specification of mandatory fields and the existence of multiple feature values, which are directly linked with challenges 3, 4 and 5 of this article. Secondly, training of health professionals in using EHR systems is crucial to determine the format and quality of routinely collected data, coupled with in embedded validation mechanisms [77]. As such, we call attention to the importance of creating awareness of the implications of EHR design and use to the reuse of clinical data, namely by providing proper training and feedback, ultimately aiming for continuous improvement [78].…”

Section: Discussionmentioning

confidence: 99%

Preprocessing structured clinical data for predictive modeling and decision support

et al. 2016

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KeywordsData mining, data access, integration and analysis, electronic health records and systems, structured data, clinical decision support SummaryBackground: EHR systems have high potential to improve healthcare delivery and management. Although structured EHR data generates information in machine-readable formats, their use for decision support still poses technical challenges for researchers due to the need to preprocess and convert data into a matrix format. During our research, we observed that clinical informatics literature does not provide guidance for researchers on how to build this matrix while avoiding potential pitfalls. Objectives: This article aims to provide researchers a roadmap of the main technical challenges of preprocessing structured EHR data and possible strategies to overcome them. Methods: Along standard data processing stages -extracting database entries, defining features, processing data, assessing feature values and integrating data elements, within an EDPAI framework -, we identified the main challenges faced by researchers and reflect on how to address those challenges based on lessons learned from our research experience and on best practices from related literature. We highlight the main potential sources of error, present strategies to approach those challenges and discuss implications of these strategies. Results: Following the EDPAI framework, researchers face five key challenges: (1) gathering and integrating data, (2) identifying and handling different feature types, (3) combining features to handle redundancy and granularity, (4) addressing data missingness, and (5) handling multiple feature values. Strategies to address these challenges include: cross-checking identifiers for robust data retrieval and integration; applying clinical knowledge in identifying feature types, in addressing redundancy and granularity, and in accommodating multiple feature values; and investigating missing patterns adequately. Conclusions: This article contributes to literature by providing a roadmap to inform structured EHR data preprocessing. It may advise researchers on potential pitfalls and implications of methodological decisions in handling structured data, so as to avoid biases and help realize the benefits of the secondary use of EHR data.

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Electronic Health Records

Cited by 157 publications

References 94 publications

Sensors vs. experts - A performance comparison of sensor-based fall risk assessment vs. conventional assessment in a sample of geriatric patients

Sensors vs. experts - A performance comparison of sensor-based fall risk assessment vs. conventional assessment in a sample of geriatric patients

Satisfaction of health‐care providers with electronic health records and perceived barriers to its implementation in the United Arab Emirates

Preprocessing structured clinical data for predictive modeling and decision support

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