What does social isolation have to do with my arthritis? Patient and provider views of the implementation of an electronic Social Determinants of Health risk assessment tool

2020 ◽  
Vol 64 (1) ◽  
pp. 707-710
Keyword(s):  
Electronic Health Record ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Assessment Tool ◽  
Determinants Of Health ◽  
Health Record ◽  
Institute Of Medicine ◽  
Patient Reported ◽  
Item Responses ◽  
Electronic Health

Social Determinants of Health (SDoH) are the conditions in which people are born, live, learn, work, and play that can affect health, functioning, and quality-of-life outcomes. The Institute of Medicine charged healthcare institutions with capturing and measuring patient SDoH risk factors through the electronic health record. Following the implementation of a social determinants of health electronic module across a major health institution, the response to institutional implementation was evaluated. To assess the response, a multidisciplinary team interviewed patients and providers, mapped the workflow, and performed simulated tests to trace the flow of SDoH data from survey item responses to visualization in EHR output for clinicians. Major results of this investigation were: 1) the lack of patient consensus about value of collecting SDOH data, and 2) the disjointed view of patient reported SDoH risks across patients, providers, and the electronic health record due to the way data was collected and visualized.

scholarly journals Identification of social determinants of health using multi-label classification of electronic health record clinical notes

JAMIA Open ◽  
2021 ◽  
Author(s):  
Rachel Stemerman ◽  
Jaime Arguello ◽  
Jane Brice ◽  
Ashok Krishnamurthy ◽  
Mary Houston ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Determinants Of Health ◽  
Support Vector ◽  
Health Record ◽  
Classification Models ◽  
Clinical Note ◽  
Clinical Notes ◽  
Electronic Health

Abstract Objectives Social determinants of health (SDH), key contributors to health, are rarely systematically measured and collected in the electronic health record (EHR). We investigate how to leverage clinical notes using novel applications of multi-label learning (MLL) to classify SDH in mental health and substance use disorder patients who frequent the emergency department. Methods and Materials We labeled a gold-standard corpus of EHR clinical note sentences (N = 4063) with 6 identified SDH-related domains recommended by the Institute of Medicine for inclusion in the EHR. We then trained 5 classification models: linear-Support Vector Machine, K-Nearest Neighbors, Random Forest, XGBoost, and bidirectional Long Short-Term Memory (BI-LSTM). We adopted 5 common evaluation measures: accuracy, average precision–recall (AP), area under the curve receiver operating characteristic (AUC-ROC), Hamming loss, and log loss to compare the performance of different methods for MLL classification using the F1 score as the primary evaluation metric. Results Our results suggested that, overall, BI-LSTM outperformed the other classification models in terms of AUC-ROC (93.9), AP (0.76), and Hamming loss (0.12). The AUC-ROC values of MLL models of SDH related domains varied between (0.59–1.0). We found that 44.6% of our study population (N = 1119) had at least one positive documentation of SDH. Discussion and Conclusion The proposed approach of training an MLL model on an SDH rich data source can produce a high performing classifier using only unstructured clinical notes. We also provide evidence that model performance is associated with lexical diversity by health professionals and the auto-generation of clinical note sentences to document SDH.

Uji Validitas Dan Reliabilitas Kuesioner Pengukuran Kompetensi Sistem Informasi Keperawatan: NICAT Dalam Bahasa Indonesia

2019 ◽  
Vol 8 (1) ◽  
pp. 39-43
Author(s):  
Stephanie Dwi Guna ◽  
Yureya Nita
Keyword(s):  
Patient Safety ◽  
Electronic Health Record ◽  
Assessment Tool ◽  
Competency Assessment ◽  
Health Record ◽  
Nursing Informatics ◽  
Cronbach’S Alpha ◽  
Cronbach's Alpha ◽  
Electronic Health ◽  
Bahasa Indonesia

Integrasi Teknologi Informasi (TI) di bidang kesehatan terbukti meningkatkan kualitas pelayanan kesehatan dengan meningkatkan patient safety serta mempercepat waktu layanan. Salah satu inovasi TI di bidang kesehatan yaitu rekam medik elektronik (electronic health record). Rekam medik jenis ini sudah umum digunakan di negara maju namun masih jarang digunakan di negara berkembang termasuk Indonesia. Sebelum pengimplementasian suatu sistem informasi baru di pelayanan kesehatan, perlu dipastikan bahwa user dapat mengoperasikannya dengan baik sehingga hasil dari sistem tersebut optimal. Perawat sebagai tenaga kesehatan dengan jumlah paling banyak di suatu pelayanan kesehatan seperti Rumah Sakit merupakan user terbesar bila rekam medik elektronik ini diterapkan.  Oleh karena itu diperlukan suatu alat untuk mengukur kemampuan atau literasi sistem informasi keperawatan (SIK). Salah satu alat ukur kompetensi SIK yaitu NICAT (Nursing Informatics Competency Assessment Tool) yang memiliki 3 bagian serta 30 item pertanyaan. Penulis melakukan alih bahasa pada kuesioner ini, kemudian melakukan uji validitas dan reliabilitas. Jumlah sampel pada penelitian ini yaitu 233 perawat di salah satu Rumah Sakit Pemerintah di Pekanbaru, Indonesia. Hasil uji validitas pada 30 item dengan r tabel 0.128 menunjukkan r hitung diatas nilai tersebut dengan Cronbach’s Alpha 0,975. Dapat disimpulkan kuesioner pengukuran kemampuan SIK (NICAT versi Bahasa Indonesia) telah valid dan reliabel sehingga dapat digunakan mengukur kemampuan SIK perawat Indonesia.

scholarly journals Frequency and Types of Patient-Reported Errors in Electronic Health Record Ambulatory Care Notes

2020 ◽  
Vol 3 (6) ◽  
pp. e205867 ◽  
Author(s):  
Sigall K. Bell ◽  
Tom Delbanco ◽  
Joann G. Elmore ◽  
Patricia S. Fitzgerald ◽  
Alan Fossa ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Ambulatory Care ◽  
Health Record ◽  
Patient Reported ◽  
Electronic Health
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