Volume 121, Issue 6 p. 927-934
Original Article
Free Access

Bringing PROMIS to practice: Brief and precise symptom screening in ambulatory cancer care

Lynne I. Wagner PhD

Corresponding Author

Lynne I. Wagner PhD

Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, Illinois

Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, Illinois

Corresponding author: Lynne I. Wagner, PhD, Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, 633 North St. Clair, 19th Floor, Chicago, IL 60611; Fax: (312) 503-4800; [email protected]Search for more papers by this author
Julian Schink MD

Julian Schink MD

Spectrum Health, Grand Rapids, Michigan

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Michael Bass MS

Michael Bass MS

Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, Illinois

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Shalini Patel BS

Shalini Patel BS

Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, Illinois

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Maria Varela Diaz

Maria Varela Diaz

Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, Illinois

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Nan Rothrock PhD

Nan Rothrock PhD

Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, Illinois

Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, Illinois

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Timothy Pearman PhD

Timothy Pearman PhD

Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, Illinois

Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, Illinois

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Richard Gershon PhD

Richard Gershon PhD

Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, Illinois

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Frank J. Penedo PhD

Frank J. Penedo PhD

Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, Illinois

Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, Illinois

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Steven Rosen MD

Steven Rosen MD

City of Hope, Duarte, California

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David Cella PhD

David Cella PhD

Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, Illinois

Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, Illinois

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First published: 06 November 2014
Citations: 149

We acknowledge the following colleagues for their contributions to this project: Mary Jo Graden, Mary O'Connor, Virginia Nothnagel, Rebecca Caires, Karen Giammiccho, Vicki Diep, Darren Kaiser, and Robert Milfajt.

Abstract

BACKGROUND

Supportive oncology practice can be enhanced by the integration of a brief and validated electronic patient-reported outcome assessment into the electronic health record (EHR) and clinical workflow.

METHODS

Six hundred thirty-six women receiving gynecologic oncology outpatient care received instructions to complete clinical assessments through Epic MyChart, an EHR patient communication portal. Patient Reported Outcomes Measurement Information System (PROMIS) computer adaptive tests (CATs) were administered to assess fatigue, pain interference, physical function, depression, and anxiety. Checklists identified psychosocial concerns, informational and nutritional needs, and risk factors for inadequate nutrition. Assessment results, including PROMIS T scores with documented severity thresholds, were immediately populated in the EHR. Clinicians were notified of clinically elevated symptoms through EHR messages. EHR integration was designed to provide automated triage to social work providers for psychosocial concerns, to health educators for information, and to dietitians for nutrition-related concerns.

RESULTS

Four thousand forty-two MyChart messages sent, and 3203 (79%) were reviewed by patients. The assessment was started by 1493 patients (37%), and once they started, 93% (1386 patients) completed the assessment. According to first assessments only, 49.8% of the patients who reviewed the MyChart message completed the assessment. Mean PROMIS CAT T scores indicated a lower level of physical function and elevated anxiety in comparison with the general population. Fatigue, pain, and depression scores were comparable to those of the general population. Impaired physical functioning was the most common basis for clinical alerts and occurred in 4% of the patients.

CONCLUSIONS

PROMIS CATs were used to measure common cancer symptoms in routine oncology outpatient care. Immediate EHR integration facilitated the use of symptom reporting as the basis for referral to psychosocial and supportive care. Cancer 2015;121:927–934. © 2014 American Cancer Society.

INTRODUCTION

Cancer patients commonly experience disease and treatment-related chronic and debilitating symptoms.1, 2 Many of the most prevalent symptoms, such as fatigue and pain, are best evaluated through direct patient reporting. Routine symptom assessment is recognized as an essential component of quality cancer care3 and is increasingly required for accreditation.4 Among patients receiving active treatment, symptom information is critically important to treatment decision making.5 Evidence of an improvement in disease-related symptoms can signify a response to treatment due to a reduced tumor burden. Treatment-related symptoms may be predictive of greater benefits from treatment.6 However, an increased symptom burden can indicate disease progression. Furthermore, a symptom burden due to treatment toxicities often helps to guide decision making with respect to dose reductions or delays.

Despite their clinical significance, many symptoms go unrecognized and untreated.7 It has been well documented that clinician symptom severity ratings are lower than patient-reported ratings.8, 9 The discrepancy between clinician and patient ratings is greatest for more subjective symptoms such as fatigue and distress.10 Complicated patient-provider communication dynamics contribute to discrepancies in symptom reporting. Clinicians may assume that patients will initiate conversations regarding troublesome symptoms. This is of concern as patients often rely on their physicians to inquire about symptoms and may face disincentives to reporting symptoms,11 including a fear of treatment changes or jeopardized rapport. These patient-provider dynamics can lead to suboptimal symptom management and, therefore, missed opportunities to reduce the burden of cancer and to improve the quality of life for cancer survivors.

Patient-reported outcome (PRO) measures are considered to be the gold standard for quantifying a patient's experience with a particular symptom or subjective concern.10, 12 Advances in health informatics have led to the development of systems for the electronic administration and scoring of PROs (electronic patient-reported outcomes [ePROs]). Several examples have been developed and implemented at academic and community-based cancer centers.13-15 A comprehensive review of electronic patient-reported symptom assessment systems in oncology has been recently published.16

These systems have been demonstrated to be feasible and efficient and to provide valuable information to patients and providers.17 However, many use single items to assess symptoms and may lack robust psychometric properties needed to detect small but meaningful symptom fluctuations over time. Alternatively, the tradeoff is to administer lengthier assessments to obtain greater psychometric precision, although this is not feasible in most clinical settings. The meaningful use of PROs in the delivery of cancer care requires a system that provides precise, accurate, and robust symptom assessment that is brief and maximizes feasibility for clinical use. Ideally, this system should integrate PRO administration with the electronic health record (EHR). EHR integration offers many potential but as yet untested advantages. EHR integration allows the coordination of PRO administration with meaningful clinic events, such as scheduled medical visits, to maximize clinical utility. Optimally designed EHR-integrated systems can provide the real-time delivery of PRO results and can enhance clinical operations by alerting clinicians to clinically significant symptoms. Automated triage to specialty providers when this is appropriate (eg, psychosocial care providers) through the EHR enhances the coordination of care and can support sites in meeting emerging accreditation standards.4

National Institutes of Health–supported advances in measurement science through the Patient Reported Outcomes Measurement Information System (PROMIS) network (http://www.nihpromis.org/)18, 19 provide an unprecedented opportunity to rapidly and precisely assess cancer-related symptoms with the most psychometrically robust tools available. PROMIS is a National Institutes of Health Common Fund (formerly Roadmap) initiative to measure patient-reported symptoms and health-related quality of life across various conditions and disease populations. PROMIS computer adaptive tests (CATs) merge computer technology with measurement science and, by doing so, rapidly assess target symptoms with as few as 4 items per symptom.20 We implemented the use of National Institutes of Health PROMIS CATs to assess cancer-related symptoms with EHR integration to communicate assessment results to the clinical team in real time. In this article, we describe our model for implementing PROMIS ePROs into routine cancer care.

MATERIALS AND METHODS

We created an ePRO assessment that included PROMIS CATs and checklists to assess psychosocial and nutritional concerns. We used Assessment Center,21 a Web-based platform for the administration of PROMIS CATs, to create our assessment. PROMIS CAT development and validation and Assessment Center have been previously described.18, 20-22 Through custom programming, we integrated assessment administration, scoring, and clinician notification with Epic, the EHR system used at our institution. We summarized assessment metrics as part of a clinical quality improvement initiative. The analysis of assessment metrics was part of a performance improvement project and thus was exempt from institutional review board review according to institutional review board guidance.23

Patients

Women with scheduled physician visits at the gynecologic oncology outpatient clinic of the Robert H. Lurie Comprehensive Cancer Center were asked to participate. Patients were required to have an EHR patient communication portal account (Epic MyChart). A total of 636 women completed 1493 assessments from November 2011 to February 2014. Patient demographic and medical characteristics were extracted from the EHR. The average age of the patients was 55.1 years (standard deviation, 12.8 years; range, 21-90 years). The majority (78%) were non-Hispanic white (Black, 5%; Asian, 4%; and Hispanic, 4%), and this is comparable to the clinical population treated at the Robert H. Lurie Comprehensive Cancer Center. The majority of patients had ovarian (35%), uterine (28%), or cervical malignancies (7%; Table 1). Some (9%) had a nongynecologic malignancy entered as the primary diagnosis for the clinical encounter (eg, breast cancer).

Table 1. Demographic and Medical Characteristics (n = 636)
Patient Characteristic n %
Race
White 494 77.7
Black 33 5.2
Asian 26 4.1
Other 23 3.6
Patient declined per medical record 41 6.4
Unknown per medical record 17 2.7
Missing 2 0.3
Ethnicity
Not Hispanic or Latino 547 86.1
Hispanic or Latino 25 3.9
Patient declined per medical record 62 9.7
Missing 2 0.3
Cancer type
Ovarian 225 35.4
Uterine 179 28.1
Cervical 44 6.9
Other female genital malignancy 28 4.4
Other malignancy coded for clinical encounter 55 8.6
Missing 105 16.5

Measures

PROMIS CATs use a computer algorithm developed with item response theory to administer PRO items selected from an item bank that are tailored to the patient's symptom severity. An item bank comprises items calibrated by item response theory–driven analysis to establish target symptom item banks.22, 24, 25 Precise, reliable, and valid symptom scores are generated with typically 4 items per symptom-specific item pool. Items are computer-scored and benchmarked on the basis of normative data from cancer patients and the general population. PROMIS CATs can be administered through Assessment Center.21 PROMIS T score clinical severity thresholds were previously determined with a standard setting exercise that converged clinician expert ratings and patient self-reported severity scores.26

The gynecologic oncology assessment consisted of PROMIS CATs that measured pain interference, fatigue, physical function, depression, and anxiety. The assessment included items assessing psychosocial and nutritional concerns for triage to supportive oncology services. The psychosocial assessment was adapted from the National Comprehensive Cancer Network Distress Thermometer and Problem Checklist27 and consisted of a checklist of emotional, practical, and informational concerns developed collaboratively with our social workers and health educator (eg, coping with cancer diagnosis, communicating with the medical team, and financial resources). The nutritional assessment included new items and items adapted from the Patient-Generated Subjective Global Assessment28 that assessed significant weight loss within the last 2 weeks and concerns about weight loss or weight gain as well as a checklist of symptoms interfering with maintaining nutrition that was developed collaboratively with cancer center dietitians. The total assessment length for the PROMIS CATs and psychosocial and nutrition assessments was approximately 40 items, and they required an average of slightly less than 10 minutes to complete.

Procedures

We implemented this ePRO assessment at our gynecologic oncology outpatient clinics. Figure 1 illustrates the EHR integration process. We created the assessment with Assessment Center and downloaded it within the hospital firewall for EHR integration. Patients with an Epic MyChart account received an email message 72 hours before scheduled physician visits alerting them to a new MyChart message. The MyChart message instructed patients to complete the ePRO assessment before their upcoming medical visit and provided a hyperlink that connected patients to the ePRO assessment in Assessment Center. Clinic staff generated a daily list of patients who had scheduled medical appointments and had not completed the ePRO assessment. When these patients checked into the clinic, clinic staff provided an iPad for patients to access their MyChart account and complete the assessment.

Details are in the caption following the image

Diagram illustrating the electronic medical record integration of a Patient Reported Outcomes Measurement Information System assessment. PRO indicates patient-reported outcome.

Assessment results were immediately documented in the EHR in 3 ways. First, all assessment results, including the date completed, PROMIS T scores, severity interpretation (normal, mild, moderate, or severe), items administered, and patient responses, were documented in the EHR in a designated section. Second, providers could copy assessment results into the typed progress note in the EHR. Third, clinicians were alerted to severe symptoms and patient concerns through messages sent to the messaging area (in-basket) of the EHR.

Processes for clinician notification within the EHR messing system were developed in collaboration with gynecologic oncology and supportive oncology clinical providers. PROMIS T scores in the severe range26 (≥70 or ≥75 according to the symptom) triggered electronic provider notification through EHR messages to the primary oncologist and a nurse message pool. The nurse message pool is an EHR in-basket with multiple recipients; for our purposes, the nurse pool included gynecologic oncology nurses. Table 2 details the clinical messaging algorithms for each domain assessed. Medical and psychosocial providers followed up with patients during clinic visits, by telephone, or through MyChart messages to the patients. Patients were asked for their preferred method of follow-up (MyChart message or telephone call) within the ePRO assessment. Standard clinical procedures were followed to manage patients who reported severe symptoms. Therefore, by design, the assessment did not ask patients to consent to follow-up contact by an oncology provider because consent was provided by virtue of the signed patient consent to treatment.

Table 2. Electronic Patient-Reported Outcome Assessment Domains and Clinician Notification Algorithms
Symptom/Concern Notification Algorithm
Pain, fatigue, physical function Treating oncologist and nurse pool messaged when symptoms severe
Depression, anxiety 1. Treating oncologist and nurse pool messaged when symptoms severe 2. Social work pool messaged
Psychosocial concerns Social work pool messaged with list of items endorsed by patient
Informational needs 1. Health educator pool messaged with information request 2. Patient provided Web link to online resource library at end of assessment
Nutrition status Dietitian messaged if weight loss thresholds met, key symptoms endorsed, or consult requested by patient

RESULTS

From November 2011 to February 2014, 636 patients completed a total of 1493 assessments, with 636 patients completing the assessment at least once. Most patients (90.1%) completed the assessment at home rather than at the clinic (9.3%). The majority of patients asked to be contacted through MyChart (84%) versus the telephone (16%) for any follow-up.

Health Record Integration

We demonstrated our ability to integrate the administration and scoring of an ePRO assessment within the EHR (Fig. 1). Through integration, a clinical event (eg, scheduled medical visit) triggered ePRO assessment administration to patients, and assessment results were immediately documented. Quality assurance testing was conducted by EHR and Assessment Center programmers to identify and resolve problems. To our knowledge, between November 2011 and February 2014, 3 minor problems occurred after EHR upgrades and required updated programming.

Feasibility

Patients (n = 636) with recurring scheduled medical visits completed the assessment multiple times (301 twice, 184 three times, and 129 four times). We examined assessment completion rates for all assessment requests, and we noted that many patients completed the assessment multiple times. As shown in Table 3, 4042 assessment requests through MyChart messages were sent across multiple time points. Of these, 3203 messages (79%) were reviewed; 1493 assessments (37%) were started; and once they were started, 1386 assessments (93%) were completed. We examined these same metrics on the basis of the first assessment requests only (first MyChart messages) to evaluate acceptance and feasibility with each patient counted only once. A total of 1089 first assessment requests were sent; 874 of those messages (80%) were read, and 435 assessments (40%) were started. With only responses to the first MyChart message used, 401 assessments were completed out of 1089 MyChart messages that were generated (a 37% completion rate). We also evaluated feasibility by limiting these metrics to patients who read the MyChart message (n = 874 or 80% of messages generated) under the assumption that patients who did not read the MyChart message may not have maintained their accounts. Among the patients who read the MyChart message (n = 874), 435 (49.8%) provided assessment data. Another 201 patients began the assessment after multiple MyChart message requests. Detailed results are now presented on the basis of the first assessments only (n = 636). Five of these patients opened the assessment Web site but did not answer any items.

Table 3. Assessment Completion Rates
MyChart Messages Sent, n MyChart Messages Read, n Completion Rate, % Assessments Started, n Completion Rate, % Assessments Completed, Once Started, n Completion Rate, % Completion Rate Among All Eligible, % Patients Who Read MyChart Message and Provided Assessment Data, %
Total for all assessments 4042 3203 79 1493 37 1386 93 34.5 46.7
Total for first assessments only 1089 874 80 435 40 401 92 36.8 49.8

Symptom Burden

In Table 4, PROMIS CAT symptom score descriptive statistics show that 631 patients started the assessment and 583 patients completed the assessment (a 92% assessment completion rate once patients began). The physical-function PROMIS CAT generated the most clinician notifications, and this indicated that of the 5 domains assessed, impairment in physical functioning was the most common patient concern. The mean physical function T score of 46.6 (standard deviation, 9.6) indicated that patients reported lower physical function than the general population. The mean anxiety T score of 52.8 (standard deviation, 9.1) indicated a slightly elevated level of anxiety in comparison with the general population. The mean T scores for fatigue, pain interference, and depression were comparable to those of the general population.

Table 4. PROMIS CAT T Score Descriptive Statistics for First Assessments Only
CAT Scores
Fatigue Pain Interference Physical Function Anxiety Depression
Completed PROMIS CATs, n 631 623 603 591 583
Score
Mean 49.7 49.5 46.6 52.8 48.8
Range 24.3-77.7 38.6-80.1 20.0-73.3 32.9-84.9 34.2-77.4
Standard deviation 9.9 9.7 9.6 9.1 8.5
Clinician notifications generated, n 13 10 26 1 1
Proportion generating notifications, % 2.1 1.6 4.3 0.2 0.2
  • Abbreviations: CAT, computer adaptive test; PROMIS, Patient Reported Outcomes Measurement Information System.

The proportion of patients with mild, moderate, and severe PROMIS T scores are presented in Table 5. Severe PROMIS symptom scores triggered a message to the oncology team (see Table 2). A total of 26 patients (4%) scored in the severe range on the physical-function CAT. Provider notifications for fatigue (n = 13 or 2%) and pain (n = 10 or 2%) were infrequently generated. Severe anxiety or depression was rare (n = 1 or 0.2%). If we consider T scores in the mild or moderate ranges to be clinically significant, 82% of the patients (n = 492) reported clinically significant impairments in physical functioning. Approximately half of the patients reported pain interference in the mild range or higher (n = 317 or 51%), fatigue severity above normal (n = 295 or 47%), and anxiety as mild or more severe (n = 252 or 43%). Approximately 22% reported depression scores in the mild range (n = 131), and the majority (n = 439 or 75%) had depression scores in the normal range.

Table 5. PROMIS CAT T Score Proportions by Symptom Severity
Normal Mild Moderate Severe Completed CAT, n
Fatigue (T score) <50 50-59 60-69 ≥70 631
No. of patients 336 193 89 13
% of patients 53 31 14 2
Pain interference (T score) <50 50-59 60-69 ≥70 623
No. of patients 306 212 95 10
% of patients 49 34 15 2
Physical function (T score) >55 54-46 45-31 ≤30 603
No. of patients 111 232 234 26
% of patients 18 38 39 4
Anxiety (T score) <55 55-64 65-74 ≥75 591
No. of patients 339 200 51 1
% of patients 57 34 9 <1
Depression (T score) <55 55-64 65-74 ≥75 583
No. of patients 439 131 12 1
% of patients 75 22 2 <1
  • Abbreviations: CAT, computer adaptive test; PROMIS, Patient Reported Outcomes Measurement Information System.

Psychosocial Concerns

Among the 617 patients who completed the psychosocial checklist, most (n = 407 or 66%) reported no psychosocial concerns. The most common psychosocial needs were information on advance directives (16%), support with managing stress (15%), information on financial resources (11%), coping with a cancer diagnosis (10%), and information on support groups (9%). A total of 137 patients (25%) indicated that they would like to be contacted by a health educator for assistance finding health-related information.

Nutrition-Related Concerns

Among the 541 patients who completed the nutrition checklist, a total of 178 patients (33%) endorsed at least 1 item that generated a message notification to dietitians. A small proportion of patients (4%) reported concern about recent weight loss. Most patients (72%) reported stable weight; equal numbers reported weight loss (13%) or weight gain (15%) over the past 2 weeks. The most common reasons for EHR messaging to dietitians included interest in information on gaining or losing weight (35%), feeling full quickly (14%), appetite loss (13%), constipation (12%), nausea (11%), taste changes (9%), and fatigue that interfered with maintaining adequate nutrition (9%).

DISCUSSION

To our knowledge, this is the first program to accomplish the clinical integration of PROMIS CAT administration, scoring, and reporting within an EHR system. This preliminary work demonstrates a model for integrating PROMIS CAT administration for routine oncology care. In doing so, we have translated state-of-the-art advances in measurement science and health informatics into a clinical application aimed at improving the delivery of cancer care. Our initial metrics support the feasibility of this approach. The integration of PROs as a component of quality cancer care and the utilization of health information technology to enable real-time analysis of data from cancer patients (a learning health care system) have been increasingly emphasized, most recently in an Institute of Medicine report on quality cancer care.29 Several Web-based and computer-based assessments have been developed for the assessment of symptoms13-15; however, most approaches use single-item ratings for symptom assessment and may lack psychometric rigor or are too lengthy for repeat administration in busy clinical settings, and this limits their use for measuring outcomes. In using PROMIS CATs for symptom assessment, our program represents the most psychometrically robust approach by allowing the precise measurement of symptoms while maintaining the brevity required for clinical implementation.

EHR integration may bypass common patient-provider communication barriers by collecting previsit ePROs and delivering results in real time at the point of care. ePRO scores automatically populate the EHR, with clinicians notified of severe symptoms to ensure that clinically significant symptoms are addressed. EHR integration also facilitates automated triage for psychosocial and supportive care. Qualitative feedback from our gynecologic oncology providers is consistent with research demonstrating that PROs improve clinical workflow by allowing providers to focus the medical visit on symptoms of concern and to track symptom severity over time.30

We identified physical functioning impairments and anxiety as the most common concerns among gynecologic oncology outpatients. Pain and fatigue scores were comparable to those of the general population, with 16% of patients reporting moderate or severe fatigue and 17% reporting moderate or severe pain interference. Overall, one-third of the patients reported current psychosocial health needs.

Psychosocial triage based on patient reports of distress and related concerns improves the efficiency of our program by allowing us to provide tailored psychosocial care for patients most in need of support. Emerging accreditation standards require that patients be screened for distress and referred for psychosocial care when needed,31 although evidence documenting improved outcomes is just emerging.32 Therefore, now that we have implemented a program for assessment and triage, an important next step is to evaluate outcomes.

With respect to feasibility, approximately 8 of 10 patients read the initial MyChart message asking patients to complete the ePRO assessment, yet only one-third ultimately completed the entire assessment. This is lower than response rates from similar ePRO initiatives15; however, a key difference is that our program required patients to initiate the assessment, whereas other programs administered the ePRO assessment on site. Our program is optional and newly integrated into the clinical workflow; therefore, patients were not overtly encouraged to participate. Future efforts are required to implement strategies to encourage more widespread patient participation. Nine of 10 patients who completed the assessment did so at home before medical visits. According to anecdotal comments from patients and clinicians, the majority found it to be a positive experience. We anticipate that adherence will increase as the administration of ePROs before scheduled medical visits becomes a part of routine cancer care. The systematic evaluation of outcomes associated with this initiative and clinician perspectives on the clinical utility of assessment results and notifications are important next steps.

We note several limitations of the data summarized in this report. The implementation of this ePRO assessment was conducted as a clinical initiative; therefore, data were extracted from the EHR and were available only for the subgroup of patients who had an EHR patient electronic communication account and had accessed and completed the assessment. We were unable to estimate the percentage of patients who were excluded because they did not have an account. It is possible that patients completing the assessment may have differed from those who did not. This limits the generalizability to patients who are wired to their EHR. In this first report, we summarized metrics supporting the feasibility of this approach; however, we did not report outcomes. Our next report will focus on the clinical team's response to assessment results and resulting patient-centered outcomes.

In conclusion, we have demonstrated the successful implementation of an ePRO system for the precise, valid, and robust measurement of common cancer-related symptoms with immediate clinician notification and triage for problems identified. In doing so, we have employed state-of-the-art measurement science to bring the patient's voice to the clinical encounter with the ultimate goal of optimal symptom and psychosocial management for improved cancer care delivery.

FUNDING SUPPORT

This study was supported by the Coleman Foundation, the William W. Wirtz Cancer Innovation Fund, and the National Institutes of Health (R01 CA60068 and U5 AR057951).

CONFLICT OF INTEREST DISCLOSURES

Lynne I. Wagner reports funding from the Gollob Foundation to support the implementation of this ePRO assessment in breast cancer clinics and consulting work for the interpretation of clinical trial PRO data for Gilead, Inc. David Cella is a board member and officer of the PROMIS Health Organization, a 501(c)(3) charitable organization.