Clinical Integration of Digital Solutions in Health Care: An Overview of the Current Landscape of Digital Technologies in Cancer Care
Digital health constitutes a merger of both software and hardware technology with health care delivery and management, and encompasses a number of domains, from wearable devices to artificial intelligence, each associated with widely disparate interaction and data collection models. In this review, we focus on the landscape of the current integration of digital health technology in cancer care by subdividing digital health technologies into the following sections: connected devices, digital patient information collection, telehealth, and digital assistants. In these sections, we give an overview of the potential clinical impact of such technologies as they pertain to key domains, including patient education, patient outcomes, quality of life, and health care value. We performed a search of PubMed (www.ncbi.nlm.nih.gov/pubmed) and www.ClinicalTrials.gov for numerous terms related to digital health technologies, including digital health, connected devices, smart devices, wearables, activity trackers, connected sensors, remote monitoring, electronic surveys, electronic patient-reported outcomes, telehealth, telemedicine, artificial intelligence, chatbot, and digital assistants. The terms health care and cancer were appended to the previously mentioned terms to filter results for cancer-specific applications. From these results, studies were included that exemplified use of the various domains of digital health technologies in oncologic care. Digital health encompasses the integration of a vast array of technologies with health care, each associated with varied methods of data collection and information flow. Integration of these technologies into clinical practice has seen applications throughout the spectrum of care, including cancer screening, on-treatment patient management, acute post-treatment follow-up, and survivorship. Implementation of these systems may serve to reduce costs and workflow inefficiencies, as well as to improve overall health care value, patient outcomes, and quality of life.
At its core, digital health constitutes a merger of both software and hardware technology with health care delivery and management. This broad definition encompasses a number of domains, from wearable devices to artificial intelligence, each associated with widely disparate interaction and data collection models. The digital revolution, defined as the transition from mechanical to electronic analog technology and subsequently digital electronics, truly began as early as the 1950s.1 This revolution continues to this day and is having a growing impact on health care. Initially, interest in digital health integration focused on process inefficiencies and health care costs, increasing health care quality, improving access to care, and supporting personalized care.2 In more recent years, there has been a focus on patient data collection that may directly interface with the clinician to guide further management. In this review, we concentrate on the landscape of current use and integration of digital health technology in cancer care by subdividing digital health technologies into the following sections: connected devices (wearables and nonwearables), digital patient information collection, telehealth, and digital assistants. We acknowledge that these divisions are somewhat artificial in that boundaries among these technologies are blurred with their integration. However, for the purposes of discussion, we use these categories to facilitate subsequent discussion. For these various modalities, we give an overview of the potential clinical impact such technologies have on clinical care as it pertains to key clinical domains, including patient education, objective patient outcomes, quality of life (QOL), and health care value (Table 1).
We performed a search of PubMed (www.ncbi.nlm.nih.gov/pubmed) and www.ClinicalTrials.gov for numerous terms related to digital health technologies, including digital health, connected devices, smart devices, wearables, activity trackers, connected sensors, remote monitoring, electronic surveys, electronic patient-reported outcomes, telehealth, telemedicine, artificial intelligence, chatbot, and digital assistants. The terms health care and cancer were appended to the previously mentioned terms to filter results for cancer-specific applications. For a full list of search parameters, please see the Data Supplement. From these results, studies were included that exemplified use in the various domains of digital health technologies in oncologic care.
The concept of connected devices, or the Internet of Things, applies to devices that have the ability to communicate information remotely over an intranet or the Internet. The number of Internet-connected devices has been rapidly increasing, having reached nearly 23 billion devices in 2016, with expectations to double to 50 billion devices with a corresponding market value of nearly $163 billion by 2020.3,4 Connected devices include a diverse range of products, from Internet-connected blood pressure cuffs and weighing scales to wearable activity trackers and biometric monitoring devices. Depending on the specific application, connected devices may use active, passive, or hybrid data collection.
Within the past decade, there has been increasing interest in integrating wearable technology with cancer care, particularly given its ability to incorporate passive data collection and minimize patient burden. In our investigation using the aforementioned key words on ClinicalTrials.gov, there were approximately 35 ongoing or recently closed or registered clinical trials investigating the use of activity trackers in conjunction with cancer care. Inherent to wearable technology is the potential for longitudinal data collection without significant patient burden.
One recent study prospectively investigated the feasibility of using wrist-mounted activity trackers, worn continuously from the start of radiotherapy until 4 weeks postcompletion, to monitor patients undergoing concurrent chemoradiotherapy.5 Results demonstrated feasibility, with a 94% device compliance rate compared with a feasibility threshold of 80%. Interestingly, on exploratory analysis, step counts correlated with short-term hospitalization risk during therapy, whereas physician-scored performance-status decline did not. This is suggestive of a role for continuous monitoring via wearable devices as a potential early-warning indicator for adverse outcomes, independent of traditional physician-reported metrics.
In addition to serving as early-warning indicators of toxicity, efforts have also been made to correlate data acquired from activity trackers with QOL. One such study prospectively investigated patients with advanced lung cancer who were given Fitbit activity trackers to measure daily step counts and multiple validated QOL questionnaires. The authors demonstrated that higher physical activity levels (as measured by daily step count) positively correlated with physical functioning, role functioning, emotional functioning, and global QOL. A separate study examining patients undergoing stem cell transplantation found similar correlations, where step counts were associated with the National Cancer Institute’s Patient-Reported Outcomes–Common Terminology Criteria for Adverse Events and multiple QOL items from PROMIS (Patient-Report Outcomes Measurement Information System).6 Multiple other studies have been published exploring the use and feasibility of activity trackers in various subsets, such as prostate,7 ovarian,8 and pediatric cancer populations.9
Beyond wrist-mounted activity trackers, other novel wearable devices are also entering the health care domain. One such device includes a small, chest-mounted device, called ADAMM (Automated Device for Asthma Monitoring and Management), which collects multiple parameters, such as respiration rate, heart rate, body temperature, coughing/wheezing, and step-count data. This device was originally developed and validated in patients with asthma10 as a platform to facilitate continuous symptom monitoring in adolescents and was found to be a significant predictor of overall asthma control and health care utilization after 3 months. It is now being actively investigated in an open clinical trial (ClinicalTrials.gov identifier: NCT03340714), exploring feasibility of using this device for monitoring adult patients with lung cancer receiving active radiotherapy.
As demonstrated previously, the proliferation of wearable devices has enabled clinicians to obtain activity and biometric data with minimal patient burden, with results suggesting correlation with clinically meaningful end points, such as hospitalization rate and QOL. However, at this time, there remains no current standard for activity data goals, and further clinical trials are warranted to explore this question. In addition, current published studies with wearable devices primarily focus on physician interpretation of the data, yet may benefit from a more clearly defined concentration on patient education, such as through physician-guided biometric and activity targets the patient can monitor via a companion application.
In addition to wearable devices, a number of Internet-connected smart devices exist in the commercial space and are being investigated in the health care setting. The primary distinguishing factor between these connected devices and wearable devices lies in the method of data collection (active v passive patient interaction), as well as in the temporality of data (discrete data-acquisition periods v continuous collection). Multiple studies investigating the feasibility and efficacy of these types of devices have recently been published.
One recent study explored the use of both smart scales and activity trackers in breast cancer survivors to prevent weight gain.11 In this three-arm pilot study, 35 women who had completed breast cancer treatment within the past 10 years were randomly assigned to an intervention group, an intervention plus activity-monitoring group (intervention+), or a control group. The intervention and intervention+ groups received face-to-face sessions, Bluetooth/Wi-Fi–enabled smart weighing scales with a companion mobile application, weekly e-mailed behavioral lessons, and tailored feedback on self-weighing. The intervention+ group received all of the previously mentioned actions, plus an activity tracker. The control group received a wireless scale without intensive feedback. The proportion of intervention, intervention+, and control group participants who were at or below baseline weight was 73%, 54%, and 46%, respectively. In addition, both intervention groups perceived the daily self-weighing as positive, and an impressive 100% of women in the intervention groups responded that they would recommend the program to others. Given that breast cancer survivors who gain 5% to 10% over prediagnosis weight have poorer survival,12,13 the results of this study suggest a public health benefit of implementing smart devices in the cancer survivorship population.
Another study explored the use of a suite of sensors and mobile reporting applications, called CYCORE, to facilitate remote data collection and analysis for patients with head and neck cancer undergoing radiotherapy and at risk for dehydration.14 The suite of CYCORE sensors included a connected weighing scale and blood pressure/pulse monitoring cuff, which communicated centrally to the CYCORE database via base station hub. Patients used the devices for two 5-day intervals during the course of radiotherapy and completed electronic questionnaires via a smartphone regarding oral intake and dehydration-related signs and symptoms. The estimated time burden for these tasks was approximately 5 to 10 minutes per day. When automated prespecified triggers were met, clinicians were prompted to contact the patient within 24 hours for further management. CYCORE was successful in identifying dehydration events during both test periods, with 60% of patients experiencing at least one dehydration event and 35% experiencing two or more events. Despite the active nature of the data collection (with the patient required to engage the sensors), the study reported > 90% satisfaction and ease of use for the CYCORE system. These results are suggestive of the role of remote data collection in improving the quality of care in patients with cancer.
Despite the active nature of data collection, requiring patients to explicitly interface with the connected device and the corresponding potential concern of patient burden, the previously cited studies demonstrate the high patient-reported acceptability of these interventions. There is significant potential for improving value, patient education, patient outcomes, and QOL with both active and passive devices. Clinical integration of these technologies seems feasible for both categories of devices, and preliminary outcome results are encouraging.
In regard to the potential impact connected devices may have on patient outcomes and QOL discussed in the previous section, digital patient information collection provides a gateway for data synchronization. With the widespread availability and decreasing costs of entry of mobile devices such as smartphones and tablets, digitization of question/survey item banks presents many potential advantages, including ease of search and access of information, automation of data entry into a central database, and remote administration. Early studies investigating patients’ self-reporting ability using Internet-connected devices have been encouraging, revealing a high level of patient satisfaction and usability.15,16
Within oncologic care, there has been increasing recognition of the importance of patient-reported outcomes (PROs),17-20 which have shown significant associations with key outcomes, such as performance status and treatment adherence,21-24 despite low concordance with corresponding physician-reported metrics.23-26 Digital platforms have the potential to facilitate PRO data capture and may afford additional benefits, such as real-time and remote monitoring with automated interventional triggers. In 2010, the National Cancer Institute issued contracts for the development of a PRO measurement system to serve as a companion to the Common Terminology Criteria for Adverse Events, which led to the creation of the Patient-Reported Outcomes–Common Terminology Criteria for Adverse Events item bank.27 This item bank has since undergone multiple validation studies, which demonstrated mode equivalence of administration techniques among paper-based, electronic, and telephone-based automated voice administration.28 Average time to complete the questionnaire was numerically, but not statistically, quickest via the electronic interface (3.4 min v 4.0 min).
Given the mode equivalence of digital collection, along with the added potential of real-time monitoring afforded by digital capture, Basch et al29 recently published a pivotal report of 766 patients undergoing chemotherapy to determine whether remote symptom reporting via computers with triggered provider-directed alerts could improve health-related QOL. Compared with the usual-care group, the intervention group experienced larger health-related QOL score improvement (34% v 18%) and fewer emergency room visits (34% v 41%), and patients were able to continue taking chemotherapy longer (mean, 8.2 v 6.3 months). Moreover, there was a notable 5-month overall survival benefit in the intervention arm.30
A randomized study by Denis et al31 examined the effect on survival of implementing a Web-based symptom self-report survey to modulate follow-up visits in patients with lung cancer. Patients were randomly assigned to a control group (standard 3-to-6-month–interval CT scan) versus an intervention group (electronic follow-up application), which entailed a weekly 12-symptom self-assessment with automated clinician triggers for predefined symptom criteria. With this system, the investigators found an improvement in overall survival (median survival, 19 v 12 months, respectively) and better performance status at first relapse (76% v 33% of patients having a performance status of 0 to 1, respectively) with the electronic follow-up application digital intervention.
Other systems aim to automate data collection and streamline data integration, including the Distress Assessment and Response Tool (DART) platform.32 DART is an electronic self-report screening tool comprising validated questions from Patient Health Questionnaire-9, the Generalized Anxiety Disorders-7, and the Social Difficulties Inventory-21. This system was initially tested with administration of the full question battery; however, in subsequent iterations, DART was tailored to ask pertinent questions dynamically on the basis of patient response, highlighting the advantage of electronic survey capture beyond simply a different mode of administration. Despite the primarily unidirectional flow of information (patient to system), the electronic platform allows for automated and adaptive data collection.
As noted in the previous examples, electronic platforms designed to capture patient symptom information have several potential advantages over conventional methods. These include increased efficiency through ease of search/access, increased efficiency sharing of authorized patient information between providers, and increased health care quality on the basis of the potential to dynamically tailor and automatically distribute electronic assessments to patients.
To maximize the potential benefit of digital health technologies, seamless integration into electronic health record systems (EHRs) is needed. Without a standardized framework to automatically transfer data between applications and EHRs, inefficient overhead is required for manually moving and re-entering data between systems. For medical informatics, Fast Healthcare Interoperability Resources (FHIR) represents a robust solution to this issue. Building and improving on prior health care information technology standards, such as Health Level 7 v1-3, Reference Information Model, and Clinical Document Architecture,33 FHIR allows for secure exchange of health care data between developers and various EHR platforms using standardized language, allowing for direct integration of external software with EHRs without requiring disparate and proprietary solutions. Multiple technology companies, including Apple, Google, Microsoft, and Samsung, have created frameworks to allow health care integration of mobile research applications. However, it was only recently34 that researchers securely integrated PROs into EHRs via FHIR protocols. Other examples include Medicare’s Blue Button (https://bluebutton.cms.gov/), which will allow 4 years of patient data to be downloaded by the patient, and the Apple Watch study, which screens for atrial fibrillation (https://www.apple.com/watch/apple-heart-study/). Another example of the utility of FHIR integration can be seen in an application that allows for complex molecular abnormality data directly obtained from the EMR to be interpreted and graphically presented to the patient via a mobile application.35 Overall, availability of the FHIR framework may significantly streamline developers’ efforts in efficiently integrating novel digital health solutions with existing EHRs.
Telemedicine, which the Health Resources and Services Administration defines as “the use of electronic information and telecommunications technologies to support long-distance clinical health care, patient and professional health-related education, public health, and health administration,”36(p1) has been explored over the past several decades in various forms, and in recent years, has seen augmentation by a number of new digital technologies. Telehealth offers the potential for blending both audio and visual interaction with a remote patient and allows for bidirectional information flow, in contrast to the previously discussed digital survey collections. Multiple studies have demonstrated that psychosocial aspects of disease and treatment can be effectively addressed through telephone follow-up compared with in-person treatment.37,38 Studies in various cancer subgroups have demonstrated feasibility and acceptability of conducting post-treatment follow-up visits via telemedicine, including for patients with brain, breast, prostate, endometrial, bladder, and colorectal cancer.39-43
The proliferation and adoption of mobile technology in recent years has expanded the scope of telemedicine. One notable example has been the emergence of teledermatology for skin cancer screening/triage. A randomized study investigating the utility of teledermatology was recently published, in which 484 patients were enrolled to compare the diagnostic performance of clinical teleconsultations with clinical plus Internet-based dermoscopic teleconsultations.44 The combination of teledermoscopy with clinical teleconsultation resulted in an improved sensitivity and specificity of 93% and 96%, respectively, compared with an in-person dermatology consultation control group.
In addition to potential cost benefits, studies have also explored the impact of telehealth in managing QOL in patients with cancer. One such study examined the role of telehealth coupled with remote symptom monitoring in 405 patients with cancer with depression, cancer-related pain, or both.45 Patients randomly assigned to the intervention arm received telecare phone-based management provided by a nurse/physician specialist team coupled with an automated home-based symptom monitoring solution via interactive voice-recorded calls or Internet-based surveys. Through use of this telehealth system, patients with depression at the time of enrollment had greater improvements in their depression-severity score and patients with cancer-related pain at the time of enrollment had greater improvements in Brief Pain Inventory pain severity scores with the intervention compared with patients in the standard-care group.
A chatbot is an artificial intelligence–driven software program designed to interact with people in a conversational manner. Chatbots are commonly found in industry and are often used in the context of customer-service triaging. Similar technology, coupled with more advanced artificial intelligence and neural-network learning ability drives popular voice-based intelligent assistants, such as Amazon’s Alexa, Google’s Assistant, Microsoft’s Cortana, and Apple’s Siri. Similar to telehealth solutions, chatbots and intelligent assistants afford the opportunity for bidirectional information exchange with patients. In addition, these digital assistants have the flexibility to be deployed over various modalities, including text-based services (eg, SMS [short message service] text messaging, mobile applications, or Web/browser-based chat windows) or primarily audio-based services, such as the Amazon Alexa or Google Assistant platforms. Conversational health care with chatbots holds much potential.
At this time, there is a paucity of published clinical trials in the oncologic field using chatbots; however, examples can be found in other health care settings. Recently, Mayo Clinic released an Amazon Alexa skill, which allows users to obtain basic first aid information by asking Alexa questions such as “How do I treat a cut?” or “How do I perform cardiopulmonary resuscitation?” In addition, Mayo Clinic piloted integration of Amazon’s Alexa-powered voice assistant to answer postdischarge questions regarding skin surgeries in the dermatology center, such as “What if I see swelling?” or “Can I take a shower?”
The potential applications of digital assistants are exciting; however, significant hurdles currently exist in their widespread application at this time. One major limitation stems from noncompliance of many commercially available systems with the Health Insurance Portability and Accountability Act. However, other commercial entities have emerged that offer Health Insurance Portability and Accountability Act–compliant solutions, such as Orbita and Kore.ai, which may facilitate clinical integration in future studies.
In conclusion, digital health encompasses the integration of a vast array of technologies with health care (Fig 1). Integration of these technologies into clinical practice has seen applications throughout the spectrum of care, including cancer screening, evaluation of baseline performance status, patient management while receiving treatment, acute post-treatment follow-up, and survivorship. Implementation of these systems may serve to reduce costs and workflow inefficiencies, as well as to improve overall health care value, patient outcomes, and QOL.
Conception and design: Shivank Garg, Noelle L. Williams, Adam P. Dicker
Administrative support: Adam P. Dicker
Collection and assembly of data: Shivank Garg, Noelle L. Williams, Andrew Ip
Data analysis and interpretation: Shivank Garg, Noelle L. Williams, Adam P. Dicker
Manuscript writing: All authors
Final approval of manuscript: All authors
Accountable for all aspects of the work: All authors
The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO's conflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/jco/site/ifc.
Employment: GlaxoSmithKline (I)
Stock and Other Ownership Interests: Regeneron (I)
Patents, Royalties, Other Intellectual Property: International patent pending for a radiotherapy head-immobilization device. The device is not commercially available or for sale.
No relationship to disclose
No relationship to disclose
Leadership: Department of Defense-Prostate Cancer Research Program, NRG Oncology, American Society for Radiation Oncology
Consulting or Advisory Role: EMD Serono, Ferring, Janssen, RedHill Biopharma
Research Funding: Prostate Cancer Foundation
Travel, Accommodations, Expenses: Merck KGaA, Ferring
|1.||Shannon CE: A mathematical theory of communication. Bell Syst Tech J 27:379-423, 1948 Crossref, Google Scholar|
|2.||US Food and Drug Administration: Digital health. https://www.fda.gov/MedicalDevices/DigitalHealth/default.htm Google Scholar|
|3.||Statista: Internet of Things (IoT) connected devices installed base worldwide from 2015 to 2025 (in billions).https://www.statista.com/statistics/471264/iot-number-of-connected-devices-worldwide/ Google Scholar|
|4.||Markets and Markets: IoT healthcare market by component (medical device, systems & software, service, connectivity technology), application (telemedicine, work flow management, connected imaging, medication management), end user, and region – global forecast to 2022. https://www.marketsandmarkets.com/Market-Reports/iot-healthcare-market-160082804.html Google Scholar|
|5.||Ohri N, Kabarriti R, Bodner WR, et al: Continuous activity monitoring during concurrent chemoradiotherapy. Int J Radiat Oncol Biol Phys 97:1061-1065, 2017 Crossref, Medline, Google Scholar|
|6.||Bennett AV, Reeve BB, Basch EM, et al: Evaluation of pedometry as a patient-centered outcome in patients undergoing hematopoietic cell transplant (HCT): A comparison of pedometry and patient reports of symptoms, health, and quality of life. Qual Life Res 25:535-546, 2016 Crossref, Medline, Google Scholar|
|7.||Van Blarigan EL, Kenfield SA, Tantum L, et al: The Fitbit One physical activity tracker in men with prostate cancer: Validation study. JMIR Cancer 3:e5, 2017 Crossref, Medline, Google Scholar|
|8.||Zhang X, McClean D, Ko E, et al: Exercise among women with ovarian cancer: A feasibility and pre-/post-test exploratory pilot study. Oncol Nurs Forum 44:366-374, 2017 Crossref, Medline, Google Scholar|
|9.||Hooke MC, Gilchrist L, Tanner L, et al: Use of a fitness tracker to promote physical activity in children with acute lymphoblastic leukemia. Pediatr Blood Cancer 63:684-689, 2016 Crossref, Medline, Google Scholar|
|10.||Rhee H, Belyea MJ, Sterling M, et al: Evaluating the validity of an automated device for asthma monitoring for adolescents: Correlational design. J Med Internet Res 17:e234, 2015 Crossref, Medline, Google Scholar|
|11.||Valle CG, Deal AM, Tate DF: Preventing weight gain in African American breast cancer survivors using smart scales and activity trackers: A randomized controlled pilot study. J Cancer Surviv 11:133-148, 2017 Crossref, Medline, Google Scholar|
|12.||Bradshaw PT, Ibrahim JG, Stevens J, et al: Postdiagnosis change in bodyweight and survival after breast cancer diagnosis. Epidemiology 23:320-327, 2012 Crossref, Medline, Google Scholar|
|13.||Caan BJ, Kwan ML, Shu XO, et al: Weight change and survival after breast cancer in the after breast cancer pooling project. Cancer Epidemiol Biomarkers Prev 21:1260-1271, 2012 Crossref, Medline, Google Scholar|
|14.||Peterson SK, Shinn EH, Basen-Engquist K, et al: Identifying early dehydration risk with home-based sensors during radiation treatment: A feasibility study on patients with head and neck cancer. J Natl Cancer Inst Monogr 2013:162-168, 2013 Crossref, Medline, Google Scholar|
|15.||Basch E, Iasonos A, Barz A, et al: Long-term toxicity monitoring via electronic patient-reported outcomes in patients receiving chemotherapy. J Clin Oncol 25:5374-5380, 2007 Link, Google Scholar|
|16.||Judson TJ, Bennett A V, Rogak LJ, et al: Feasibility of long-term patient self-reporting of toxicities from home via the Internet during routine chemotherapy. J Clin Oncol 31:2580-2585, 2013 Link, Google Scholar|
|17.||Kluetz PG, Slagle A, Papadopoulos EJ, et al: Focusing on core patient-reported outcomes in cancer clinical trials: Symptomatic adverse events, physical function, and disease-related symptoms. Clin Cancer Res 22:1553-1558, 2016 Crossref, Medline, Google Scholar|
|18.||Di Maio M, Basch E, Bryce J, et al: Patient-reported outcomes in the evaluation of toxicity of anticancer treatments. Nat Rev Clin Oncol 13:319-325, 2016 Crossref, Medline, Google Scholar|
|19.||Basch E, Abernethy AP, Mullins CD, et al: Recommendations for incorporating patient-reported outcomes into clinical comparative effectiveness research in adult oncology. J Clin Oncol 30:4249-4255, 2012 Link, Google Scholar|
|20.||Pietanza MC, Basch EM, Lash A, et al: Harnessing technology to improve clinical trials: Study of real-time informatics to collect data, toxicities, image response assessments, and patient-reported outcomes in a phase II clinical trial. J Clin Oncol 31:2004-2009, 2013 Link, Google Scholar|
|21.||Quinten C, Maringwa J, Gotay CC, et al: Patient self-reports of symptoms and clinician ratings as predictors of overall cancer survival. J Natl Cancer Inst 103:1851-1858, 2011 Crossref, Medline, Google Scholar|
|22.||Eliasson L, Clifford S, Barber N, et al: Exploring chronic myeloid leukemia patients’ reasons for not adhering to the oral anticancer drug imatinib as prescribed. Leuk Res 35:626-630, 2011 Crossref, Medline, Google Scholar|
|23.||Atkinson TM, Andreotti CF, Roberts KE, et al: The level of association between functional performance status measures and patient-reported outcomes in cancer patients: A systematic review. Support Care Cancer 23:3645-3652, 2015 Crossref, Medline, Google Scholar|
|24.||Kadakia KC, Snyder CF, Kidwell KM, et al: Patient-reported outcomes and early discontinuation in aromatase inhibitor-treated postmenopausal women with early stage breast cancer. Oncologist 21:539-546, 2016 Crossref, Medline, Google Scholar|
|25.||Fromme EK, Eilers KM, Mori M, et al: How accurate is clinician reporting of chemotherapy adverse effects? A comparison with patient-reported symptoms from the Quality-of-Life Questionnaire C30. J Clin Oncol 22:3485-3490, 2004 Link, Google Scholar|
|26.||Pakhomov SV, Jacobsen SJ, Chute CG, et al: Agreement between patient-reported symptoms and their documentation in the medical record. Am J Manag Care 14:530-539, 2008 Medline, Google Scholar|
|27.||Basch E, Reeve BB, Mitchell SA, et al: Development of the National Cancer Institute’s patient-reported outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE). J Natl Cancer Inst 106:dju244-dju244, 2014 Crossref, Medline, Google Scholar|
|28.||Bennett AV, Dueck AC, Mitchell SA, et al: Mode equivalence and acceptability of tablet computer-, interactive voice response system-, and paper-based administration of the U.S. National Cancer Institute’s Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE). Health Qual Life Outcomes 14:24, 2016 Medline, Google Scholar|
|29.||Basch E, Deal AM, Kris MG, et al: Symptom monitoring with patient-reported outcomes during routine cancer treatment: A randomized controlled trial. J Clin Oncol 34:557-565, 2016 Link, Google Scholar|
|30.||Basch E, Deal AM, Dueck AC, et al: Overall survival results of a trial assessing patient-reported outcomes for symptom monitoring during routine cancer treatment. JAMA 318:197-198, 2017 Crossref, Medline, Google Scholar|
|31.||Denis F, Lethrosne C, Pourel N, et al: Randomized trial comparing a Web-mediated follow-up with routine surveillance in lung cancer patients. J Natl Cancer Inst 109:djx029, 2017 Crossref, Google Scholar|
|32.||Li M, Macedo A, Crawford S, et al: Easier said than done: Keys to successful implementation of the Distress Assessment and Response Tool (DART) program. J Oncol Pract 12:e513-e526, 2016 Link, Google Scholar|
|33.||FIHR: FHIR overview. https://www.hl7.org/fhir/overview.html Google Scholar|
|34.||Pfiffner PB, Pinyol I, Natter MD, et al: C3-PRO: Connecting ResearchKit to the health system using i2b2 and FHIR. PLoS One 11:e0152722, 2016 doi: Crossref, Google Scholar|
|35.||Warner JL, Rioth MJ, Mandl KD, et al: SMART precision cancer medicine: A FHIR-based app to provide genomic information at the point of care. J Am Med Inform Assoc 23:701-710, 2016 Crossref, Medline, Google Scholar|
|36.||Health Resources and Services Administration: Telehealth programs. https://www.hrsa.gov/rural-health/telehealth/index.html Google Scholar|
|37.||Beaver K, Williamson S, Chalmers K: Telephone follow-up after treatment for breast cancer: Views and experiences of patients and specialist breast care nurses. J Clin Nurs 19:2916-2924, 2010 Crossref, Medline, Google Scholar|
|38.||Gotay CC, Bottomley A: Providing psycho-social support by telephone: What is its potential in cancer patients? Eur J Cancer Care (Engl) 7:225-231, 1998 Crossref, Medline, Google Scholar|
|39.||Sardell S, Sharpe G, Ashley S, et al: Evaluation of a nurse-led telephone clinic in the follow-up of patients with malignant glioma. Clin Oncol (R Coll Radiol) 12:36-41, 2000 Medline, Google Scholar|
|40.||Faithfull S, Corner J, Meyer L, et al: Evaluation of nurse-led follow up for patients undergoing pelvic radiotherapy. Br J Cancer 85:1853-1864, 2001 Crossref, Medline, Google Scholar|
|41.||Kimman ML, Bloebaum MM, Dirksen CD, et al: Patient satisfaction with nurse-led telephone follow-up after curative treatment for breast cancer. BMC Cancer 10:174, 2010 Crossref, Medline, Google Scholar|
|42.||Beaver K, Tysver-Robinson D, Campbell M, et al: Comparing hospital and telephone follow-up after treatment for breast cancer: Randomised equivalence trial. BMJ 338:a3147, 2009 Crossref, Medline, Google Scholar|
|43.||Smits A, Lopes A, Das N, et al: Nurse-led telephone follow-up: Improving options for women with endometrial cancer. Cancer Nurs 38:232-238, 2015 Crossref, Medline, Google Scholar|
|44.||Ferrándiz L, Ojeda-Vila T, Corrales A, et al: Internet-based skin cancer screening using clinical images alone or in conjunction with dermoscopic images: A randomized teledermoscopy trial. J Am Acad Dermatol 76:676-682, 2017 Crossref, Medline, Google Scholar|
|45.||Kroenke K, Theobald D, Wu J, et al: Effect of telecare management on pain and depression in patients with cancer: A randomized trial. JAMA 304:163-171, 2010 Crossref, Medline, Google Scholar|