Ehr system

Manufactured by Epic Systems

The EHR system is a comprehensive electronic health record platform that allows healthcare providers to manage patient data, including medical history, treatments, and test results. The system integrates various functionalities to streamline clinical workflows and facilitate efficient patient care.

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Epic EHR system (8 citations) EpicCare EHR system (3 citations) EHRs) system (2 citations) Epic EHR system expertise (2 citations)

19 protocols using ehr system

Comparative Clinical Decision Support Evaluation

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The study is being conducted at primary care clinics associated with the University of Wisconsin and University of Utah medical centers. All General Internal Medicine (GIM) and Family Medicine (FM) primary care clinics at the two institutions were invited to participate. A total of 33 individual clinics (12 GIM clinics, 16 FM clinics, and 5 combined clinics) are participating in the study. Table 1 illustrates the clinic characteristics. Clinics were enrolled by site leads at each medical center. All physicians, nurse practitioners, physician assistants, and residents at participating clinics were eligible to participate. Both sites use the same EHR system (Epic Systems, Verona, WI) and had off-the-shelf capabilities to develop CDS tools in their EHR. Each site was supported by an information technology department that was able to develop and test the components of the iCPR before deployment.Table 1

Description of study clinics

	University of Wisconsin	University of Utah
Total no. of clinics	22	11
No. of intervention clinics	12	6
Total no. of providers	268	111
GIM clinics	10	2
FM clinics	12	4
Combined GIM and FM clinics	0	5
No. of providers per clinic	2–29	3–23

GIM General Internal Medicine, FM Family Medicine

Feldstein D.A., Hess R., McGinn T., Mishuris R.G., McCullagh L., Smith P.D., Flynn M., Palmisano J., Doros G, & Mann D. (2017). Design and implementation of electronic health record integrated clinical prediction rules (iCPR): a randomized trial in diverse primary care settings. Implementation Science : IS, 12, 37.

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Epic EHR System at Stanford Health

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The study was based at Stanford Health Care (SHC), a vertically-integrated healthcare system comprising three hospitals and multiple clinics and health centers located throughout Northern California. SHC utilizes an EHR system (Epic Systems, Verona, WI) for all outpatient and inpatient clinical encounters.

Wu A., Huang R.J., Colón G.R., Zembrzuski C, & Patel C.B. (2022). Low rates of structured advance care planning documentation in electronic health records: results of a single-center observational study. BMC Palliative Care, 21, 203.

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Predictive Modeling for Hospital Wards

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We drew data from the Duke University Hospital (DUH) EHR system, an Epic Systems Corporation¹¹ (EPIC) based health system, installed in late 2013. We extracted data on patient hospital stays from January 1, 2014, to December 30, 2016. We focus on patients admitted to general medical-surgical wards, that is, an environment where patients are not receiving constant monitoring, as is the case in an ICU. We planned to implement the model into the EPIC Acuity Scoring module.¹¹ This environment allows one to generate risk models using data generated in real time. One designates the clinical feature to be used and assigns a weight (i.e., a beta coefficient) to that value. Therefore, this environment is designed to handle regression-based models as opposed to more complicated machine learning models. However, since the calculation is embedded in the EPIC environment the results can be directly fed back to providers.

O’Brien C., Goldstein B.A., Shen Y., Phelan M., Lambert C., Bedoya A.D, & Steorts R.C. (2020). Development, Implementation, and Evaluation of an In-Hospital Optimized Early Warning Score for Patient Deterioration. MDM Policy & Practice, 5(1), 2381468319899663.

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Linking Prostate Cancer Data Across EHRs and Registries

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We assessed patient records from a tertiary academic medical center accessed under an Institutional Review Board (IRB) approved protocol. EPIC surveys were collected under a separate IRB approved protocol and with informed consent. This health care system has implemented a fully functional EHR system in 2008 (Epic Systems Corporation, Verona Wisconsin). Surveys were linked to patients’ EHRs via unique patient features (first name, last name, date of birth, date of surgery, and surgeon). We linked data from the EHRs with the California Cancer Registry to create a research data warehouse for prostate cancer, described previously [12 (link)].

Gori D., Banerjee I., Chung B.I., Ferrari M., Rucci P., Blayney D.W., Brooks J.D, & Hernandez-Boussard T. (2019). Extracting Patient-Centered Outcomes from Clinical Notes in Electronic Health Records: Assessment of Urinary Incontinence After Radical Prostatectomy. eGEMs, 7(1), 43.

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Emergency Medicine Specimen Biobank

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The EMSB was initiated in the UC-AMC ED on February 5, 2018. Patients eligible for the EMSB include those presenting to UC-AMC who are >17 years of age, speak English or Spanish, and are medically stable to consent or have a medical durable power of attorney (MDPOA). The EMSB researchers and trained clinical staff approach all eligible patients for consent to participate in this biobank program. All patients who have an intravenous line (IV) placed as part of their routine care have a blood sample collected, and the EMSB keeps samples from consented participants. Consent, sample collection, sample sorting, and sample processing occur in the ED.
The inclusion and exclusion criteria are outlined in the electronic health record (EHR) system used by UCHealth (Epic Systems Corporation, Verona, WI). Patients are excluded if their clinical condition precludes the ability to consent, and there is no MDPOA available. The consent lasts for a year after signing, allowing for collection of samples and clinical data from subsequent ED visits without additional consent.

Vest A., Sonn B., Puls R., Arnold C., Devney Z., Ahmed A., Pallisard O, & Monte A.A. (2023). Characteristics of Emergency Medicine Specimen Bank Participants Compared to the Overall Emergency Department Population. Western Journal of Emergency Medicine, 24(2), 312-321.

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Evaluating Pediatric Residents' EHR Prerounding

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This study was designed as a mixed methods approach combining quantitative and qualitative analyses to evaluate residents’ prerounding performance using the Epic EHR system. We invited pediatric residents at a large teaching hospital in the South Atlantic Region to participate in the study as part of an optional professional development event. A convenience sample of all 30 pediatric residents voluntarily participated are reflecting the entire population of pediatric residents in the hospital. The residents’ level of training ranged from 1 to 3 years of postgraduate medical education, and all residents had more than a month of direct patient care in the pediatric wards. All residents had prior experience using the EHR system (Epic Systems) for prerounding as part of their work routine. To ensure completeness and transparency of reporting, we followed the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) reporting guidelines [33 (link)].

Alami J., Hammonds C., Hensien E., Khraibani J., Borowitz S., Hellems M, & Riggs S. (2023). Examining Pediatric Resident Electronic Health Records Use During Prerounding: Mixed Methods Observational Study. JMIR Medical Education, 9, e38079.

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Analyzing Prostate Cancer Surgical Outcomes

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We obtained data from a large, tertiary academic medical center that provides inpatient-, outpatient-, and primary care. During the time of our analysis, the center used the Epic (Epic Systems, Verona Wisconsin) EHR system. The access to de identified EHR data was obtained through an innovative research data warehouse that facilitates research.26 (link) This translational research platform allows the capture of both structured data (e.g., ICD-9-CM codes, laboratory values, etc.) as well as unstructured data (e.g., clinicians’ narrative text, preoperative notes, etc.) on all patients receiving care at the institute.
We identified patients in our research platform with localized prostate cancer based on ICD-9-CM code 185. (Figure 1). Patients were categorized into prostatectomy surgical groups according to ICD 9 procedure codes: open prostatectomy, ICD 9 60.5 and CPT 55845; robotic prostatectomy, ICD 9 60.5 plus 17.42 and CPT 55866; laparoscopic prostatectomy, 60.5 plus 54.21; and other prostatectomies, which included CPT codes that were not distinguishable between robotic and laparoscopic procedures, e.g., CPT 55840. In our data mining analysis, we exclude patients without a clinical note and without a follow-up visit within 90 days postoperatively because they have no text notes to process for postoperative urinary incontinence.

Hernandez-Boussard T., Tamang S., Blayney D., Brooks J, & Shah N. (2016). New Paradigms for Patient-Centered Outcomes Research in Electronic Medical Records: An Example of Detecting Urinary Incontinence Following Prostatectomy. eGEMs, 4(3), 1231.

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Implementing EHR in Tertiary Medical Center

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The study took place at Wake Forest Baptist Medical Center (WFBMC), a tertiary level 1 trauma center and level 1 pediatric trauma center with 885 beds in Winston-Salem, North Carolina. WFBMC implemented an EHR system (Epic Systems) in 2012. This study focuses on the pediatric intensive care unit (PICU) with 12 beds, the neonatal ICU (NICU) with 40 beds, and the adult medical ICU with 172 beds.

Kirkendall E., Huth H., Rauenbuehler B., Moses A., Melton K, & Ni Y. (2020). The Generalizability of a Medication Administration Discrepancy Detection System: Quantitative Comparative Analysis. JMIR Medical Informatics, 8(12), e22031.

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Extracting Cognitive Test Data from Clinical Notes

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ChatGPT (GPT-4, API version “2023-03-15-preview”) was used on these 765 notes to extract all instances of the cognitive tests—MMSE and CDR—along with the dates at which the tests were mentioned to have been administered. Two examples of our task are provided in the supplementary section S2. Inference was successful for 742 notes. The complete API call, along with the exact prompt, the temperature, and other hyper-parameters are included in Supplementary Table S3. The prompt included a request to return these results in a JSON format. ChatGPT’s response (full), as well as the JSON formatted dialogue response were recorded in one session on June 9th 2023. The notes sent to ChatGPT were text-only, stripped of the rich-text formatting (RTF) native to our EHR system (Epic Systems, Verona, WI). This reduced token count by approximately ten-fold, enabling notes to fit into the GPT4–8K input window and removing a substantial source of confusion for the LLM in prompt tuning. The date that the encounter was recorded in Epic was appended at the beginning of the note, proceeding with a column (“:”) then the note text. See Supplementary Table S3 for the API request, including the prompt.

Zhang H., Jethani N., Jones S., Genes N., Major V.J., Jaffe I.S., Cardillo A.B., Heilenbach N., Ali N.F., Bonanni L.J., Clayburn A.J., Khera Z., Sadler E.C., Prasad J., Schlacter J., Liu K., Silva B., Montgomery S., Kim E.J., Lester J., Hill T.M., Avoricani A., Chervonski E., Davydov J., Small W., Chakravartty E., Grover H., Dodson J.A., Brody A.A., Aphinyanaphongs Y., Masurkar A, & Razavian N. (2024). Evaluating Large Language Models in Extracting Cognitive Exam Dates and Scores. medRxiv.

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Integrating Clinical Prediction Rules into EHR

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The study was conducted at primary care clinics associated with two large academic health systems: one Midwestern (Health System A) and the other in the Intermountain United States (Health System B). All general internal medicine (GIM) and family medicine (FM) primary care clinics at the two institutions were invited to participate. A total of 33 individual clinics (12 GIM clinics, 16 FM clinics, and five combined clinics) participated in the study. Each site used the same EHR system (Epic Systems, Verona, WI) and its native functionality (i.e., meaning no custom software development in addition to what is standardly available in the EHR was used) to develop the iCPR tools in their EHR. Each site was supported by an information technology department that adapted and tested the components of the iCPR tool before deployment.

Mann D., Hess R., McGinn T., Mishuris R., Chokshi S., McCullagh L., Smith P.D., Palmisano J., Richardson S, & Feldstein D.A. (2019). Adaptive design of a clinical decision support tool: What the impact on utilization rates means for future CDS research. Digital Health, 5, 2055207619827716.

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