ONE Liver: A unique, multidisciplinary virtual tool for prospective review of complex liver tumors

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Background: Primary and secondary malignancies involving the liver represent some of the most complex cases we see in oncology. These tumors often require multi-disciplinary decision making to determine the optimal treatment. For example, the treatment plan for hepatocellular carcinoma or colorectal cancer metastatic to the liver can often include one of several liver-directed therapy options for cure and/or palliation. Obtaining multiple opinions in an expedited and patient-centered way can be challenging for community oncologists, especially those serving a rural community. We opted to create a virtual tumor board with geographical reach across Tennessee for timely comprehensive evaluation of these patients at Tennessee Oncology (TO), a community oncology network spanning over 30 clinical sites of care. Methods: In January 2021, we created a multidisciplinary team containing specialists in medical oncology, radiation oncology, hepatology, radiology, interventional radiology, and hepatobiliary surgery. Medical oncologists at any TO clinic in Tennessee or Georgia could make referrals to the program within the Electronic Health Record (EHR). The team reviewed all submitted cases bi-weekly either virtually or at a centralized location. Referring providers were not required to attend the tumor boards to expand geographical reach. Recommendations were communicated back to the referring physician by both email and letters scanned into the EHR. Results: Between January 1, 2021 and May 1, 2022, a total of 154 referrals representing 126 unique patients were placed to the ONE Liver tumor board. Thirty-three physicians referred patients across 21 practice locations across the state. The top 3 diagnoses were: metastatic colorectal cancer (53), hepatocellular carcinoma (33), and primary biliary cancers (32). The most common first line recommendations were for therapy with Y-90 embolization, further imaging workup, continued systemic therapy and/or clinical trial evaluation, and stereotactic body radiation therapy. All patients were evaluated within 2 weeks of referral with most evaluated in less than 1 week. 60% of patients evaluated lived more than 25 miles from a tertiary medical center and 28% lived more than 50 miles out. Conclusions: This initiative successfully facilitated efficient and multi-disciplinary evaluation of patients with complex liver tumors living in rural areas. Tumor boards of this type might scale disease expertise across larger geographical locations than traditional in-person tumor boards.

Implementation of a virtual, on-demand, molecular tumor board at a large, multi-clinic, community oncology practice

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Background: Despite the availability of molecularly-targeted agents for the treatment of many cancer types, gaps remain in integrating comprehensive precision oncology decision support tools and services into routine clinical practice. Molecular Tumor Boards (MTBs) have been shown to improve accurate incorporation of precision oncology and oncologic clinical trial enrolment into clinical practice. However, the traditional MTB model is a didactic meeting occurring at regular pre-scheduled cadences which may not align with treatment decisions or schedules of community-based general medical oncologists and advanced practice providers (APPs) without protected time away from clinic. Herein, we report on the utilization of an on-demand virtual MTB (vMTB) implemented at Tennessee Oncology (TO) powered by the Personalized Medicine (PM) team at the Sarah Cannon Research Institute (SCRI). Methods: “MolecularHelp” (MH) decision support services were implemented in September 2021 for oncology providers at TO – a network of over 100 oncologists and 86 APPs practicing across 34 clinics in Tennessee. The MH services request was a structured order that could be placed directly within the electronic health record (EHR) or through patient-protected email within the practice. MH orders initiated a virtual, on-demand interpretation of comprehensive genomic profiling (CGP) reports by a centralized vMTB run by SCRI's PM team and supported by Genospace, SCRI's precision medicine software platform. The PM team – comprised of pharmacologists, cell biologists, human geneticists, and molecular biologists – analyze CGP results and provide expert advice on both standard-of-care (SOC) and clinical research therapeutic options. Both SOC and clinical research targeted therapy options were relayed back to the treating physician and embedded within the EHR. Herein, we report key metrics including MH order frequency, average turnaround time, and subsequent clinical trial enrolment between September 2021 and March 2022. Results: CGP reports from 120 unique patients were reviewed by the vMTB during the collection period. MH orders were initiated by 30 TO providers from 14 different clinic locations across Middle and East Tennessee. The average turnaround time from referral to vMTB interpretation was less than 10 hours. Of the 120 patients reviewed, actionable mutations were identified by the MTB in 103 patients, of whom 27 subsequently enrolled onto clinical trials (15 phase 1 and 12 phase 2/3). Conclusions: An on-demand vMTB is feasible within an engaged community oncology practice with investments in bioinformatics, decision support software tools, and a team of precision oncology experts supported by a robust clinical trial menu. On-demand vMTBs can be widely adopted to enhance clinical trial enrolment. Future directions include studying the impact of vMTBs on patient outcomes over time.

Differences in the utilization of palliative care support services among patients with metastatic solid tumor cancer in a community oncology setting: A retrospective review

82

Background: Palliative care has been underutilized in the setting of advanced cancer despite its established benefit in improving the quality of life in cancer patients. Few studies have evaluated socioeconomic disparities in receiving palliative care in the outpatient oncology setting. We aimed to evaluate for disparities in utilization of palliative care among patients with metastatic solid tumor malignancies at Tennessee Oncology, a large outpatient community oncology practice with an established palliative care program. Methods: We completed a retrospective review of medical records of 1513 patients that were seen in Tennessee Oncology clinics from 12/2020 to 12/2021. We compared the baseline characteristics of patients with metastatic solid tumor malignancies who did and did not receive palliative care. Chi-square and two-sample t-tests were used for data analysis with the 5% significance level using R statistical software. Results: Male patients utilized palliative care less often than female patients (17% versus 24% for females, p =.0002; 95% CI,.05-1.0). Of payer types, Medicare had the least palliative care utilization (14%) compared to commercial (25%) and other payers (23%). Utilization also varied by cancer type, with melanoma (9%), lung cancer (15%) and renal cancer (21%) being least likely to receive palliative care (p <.00005; 95% CI,.19-1.0). We did examine racial differences in palliative care utilization, but those did not reach statistical significance. Conclusions: There are multiple disparities in the utilization of on-site palliative care support services among patients with metastatic solid tumor cancer in this outpatient community oncology setting. Further research is needed to gain insight into why this is, including an in-depth analysis of both patient and provider utilization/referral practices.[Table: see text]

Utilizing data and artificial intelligence to optimize treatment room scheduling and staffing

436

Background: Tennessee Oncology is a large community oncology practice with over 30 clinics providing 89,000 treatments per year across Tennessee and northern Georgia. Tennessee Oncology’s scheduling application was unable to optimally schedule treatment appointments. This scheduling gap was causing frequent patient delays and employee extended hours. Tennessee Oncology partnered with Smirta, Inc., to develop a data and artificial intelligence (AI) driven scheduling overlay platform that would optimize and simplify cancer treatment scheduling as well as predict scheduling patterns and resource needs. Methods: Named OncoSmart, the scheduling optimization platform ingests historic scheduling data, detailed clinic configuration data including provider and nursing schedules, and available resource data such as treatment room chairs. Utilizing AI, the platform generates optimal scheduling recommendations matching the specific set of services that need to be scheduled. The platform overlays the current scheduling app and provides dynamic, real-time recommendations based on current resource (treatment room, provider, etc.) schedule availabilities and bookings. Tennessee Oncology piloted the scheduling optimization platform at 1 clinic and has currently expanded the pilot to 12 additional clinics. Results: After various ranges of clinic pilot times (6 months to 2 years), Tennessee Oncology treatment volumes have increased by 7%. In parallel to this increase, the optimization platform has helped decrease extended hours by over 32%. The original pilot site has shown major improvement in all 4 primary key performance indicators (KPI): treatment volume +12%; Chair utilization +12%; treatment delay -9%; extended hours -82%. Additionally, using the platform’s predictive analytics capabilities, analyses have been completed to generate optimal treatment scheduling patterns as well as optimal treatment nursing staffing models. Conclusions: Within a short period after deployment, Smirta Inc’s OncoSmart has helped Tennessee Oncology identify better treatment scheduling options for these 13sites. The scheduling optimization platform has proven to be very effective in identifying optimal treatment scheduling strategies and in identifying critical resource bottlenecks. The platform’s clinic management, optimization, nurse assignment, business intelligence, and resource management modules has empowered Tennessee Oncology to better manage critical clinical resources and reduce staff overtime during a period of growth.

Implementation of a scalable integrative oncology (IO) program in a large community oncology network

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Background: Integrative Oncology (IO) has become a specialized area of cancer care because of patient desire for holistic approach to care and in response to unmet symptom burden. Until now most IO programs have been limited to large academic medical centers. At Tennessee Oncology (TO), a large community oncology program spanning over 30 clinical sites of care throughout Tennessee and Georgia, an IO program was developed and implemented to bring IO to patients in the community. Methods: In June 2021, the IO program was launched with a physician and a nurse practitioner, both trained in Integrative Oncology. The program started at 8 clinics with visits primarily performed through telemedicine to allow access to each clinic. Providers were educated via email communication and a short video describing the program. Patient education was provided through our website and flyers placed in clinics. A referral order was created within the electronic health record. Results: Within one year, the IO program grew from seeing less than 20 patients per month to seeing over 100 patients per month. To date we have provided approximately 1,050 IO visits for 432 unique patients. Of these patients, 362 (83%) were female and 70 were male. The average age was 59 years old. The top three associated malignancies for patients were breast (n = 182), colorectal (n = 30), and gynecologic oncology (n = 29). Our IO program has expanded from eight to 16 clinics during this time frame. 75% of visits were provided through telemedicine. The most common reasons for IO referral were nutrition and symptom management (fatigue, neuropathy, etc). Conclusions: Implementation of an IO program is possible and scalable in a large community oncology setting. Future directions include studying the impact of our program on patient experience and overall health and wellness.

Provider-led advance care planning in community oncology: A successful multidisciplinary quality improvement intervention

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Background: Advanced care planning (ACP) is an important aspect of shared decision making in cancer treatment. Due to its importance, in 2016, Medicare expanded coverage and reimbursement for advance care planning (ACP) services (CPT codes 99497 and 99498). Despite this, ACP has been underutilized in practice. Methods: Tennessee Oncology aimed to increase knowledge and utilization of this service by medical oncologists and advance practice providers and corresponding CPT codes through an educational and quality improvement project. We formed a multidisciplinary team with individuals representing medical oncology providers, palliative care team, billing and accounting, information technology and informatics, nursing, navigation team, and operations. This team created an educational video, incorporating the “PAUSE” framework for addressing advance care planning and its role in community oncology, and details of documentation and billing. We also built in documentation templates into the medical oncology note and created a process to automate the charge capture to avoid additional steps for oncology providers. Results: Prior to this initiative, there was no baseline method to measure ACP and corresponding documentation. After two months of launching our educational video and new documentation templates, 120 documented ACP discussions were completed. ACP documentation was performed by 61 total providers practicing across 16 clinics. Providers completing documentation included both medical oncology (n = 53, 86%) and palliative care (n = 8). Of medical oncology providers, 39 (73%) were physicians and 14 (27%) were advanced practice providers. The three most common cancer diagnoses in ACP encounters were lung (20%), breast (13%), and prostate (8%). Conclusions: This combination of education and automation with multidisciplinary team input helped establish a baseline for ACP measurement that will help identify gaps and improve ACP discussions and documentation in our practice going forward.

Growth and scalability of a palliative care program in a large community oncology practice

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Background: Tennessee Oncology (TO) is a large community oncology practice with over 180 oncology providers spanning over 30 clinics throughout Tennessee and northern Georgia. In 2017, TO began embedding palliative care (PC) providers in clinics. However, the program growth was slow and by the end of 2019, TO offered PC services within only five clinics. In early 2020, TO implemented various initiatives to expand access and improve utilization of palliative care. Methods: In May 2020, TO hired a palliative care physician to grow and oversee the program. TO physician leadership established and communicated the importance of PC to providers and began providing feedback to each provider on utilization of PC for metastatic lung and pancreatic cancer patients. These diseases were selected due to poor prognosis, high morbidity, and known benefit of palliative care. Expansion of telemedicine reimbursement helped our PC team offer in person and telemedicine visits. Increasing demand allowed for expansion of the team and hiring of additional physicians, advanced practice providers (APPs), and a PC nurse coordinator to provide triage, follow-up and scheduling for PC providers. Results: Between the end of 2019 and the end of 2021, the average number of PC visits per quarter (averaged across three quarters) increased from 1,279 to 2,480, representing a growth of 194%. During this time, TO provided over 19,600 PC visits for 3,955 unique patients, of which 53% were female and 47% were male. Of visits provided, 40% were performed through telemedicine. The program has grown from five providers to 11 providers (three physicians, eight APPs). The number of clinics offering in person PC services has grown from five to 13. The three most common malignancies associated with patient visits were lung (16%), breast (10%), and colorectal (7%). Conclusions: Embedding palliative care within a large community oncology practice is feasible and can grow rapidly. A combination of in-person and telemedicine visits can expand reach to improve accessibility across a large patient population.

Impact of clinical trial enrollment on episode costs in the Oncology Care Model (OCM)

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Background: Clinical trials are critical for improving outcomes for patients with cancer. However, there is some concern from health insurers that clinical trial participation can increase total cost of care for cancer patients. We investigated the impact of clinical trial participation on total costs paid by Medicare during the OCM program in a large community-based practice. Methods: Tennessee Oncology (TO) is a community oncology practice comprising over 90 oncologists across 30 sites of care. We linked TO trial data and electronic medical record data with OCM data for episodes of care from 2016-2018. To assess the impact of trial participation on total cost relative to routine care, we created matched comparator groups for each OCM episode based on cancer type, metastatic status, number of comorbidities, performance status, and age. Patients with breast cancer receiving hormone therapy only were excluded. Absolute and percent cost differences between groups were calculated for episodes that had a comparator group size of five or greater. Differences in total cost for trial episodes were compared to non-trial episodes, and significance was assessed using the Mann–Whitney U test. We also studied the impact of trial participation on receipt of active treatment in the last 14 days of life (TxEOL), hospice use, and hospitalizations. Results: During the study period, 8,026 completed OCM episodes met study criteria. Patients were enrolled in a clinical trial for 459 of these episodes. On average, episodes during which patients were on trial cost $5,973 less than matched non-trial episodes (Table), independent of early versus late-phase trial. Most savings resulted from decreased drug costs. There were no differences in rates of TxEOL (15% vs. 14% p=1.0), rates of hospitalizations (31% vs. 30% p=0.54), or hospice use (52% vs. 62% p=0.08) between trial and non-trial episodes. Median difference from comparator group average cost was significantly lower for clinical trial episodes (-18% vs. -6%, p<0.01). Conclusions: In the community setting, total costs paid by Medicare for patients participating in clinical trials during OCM episodes were lower than costs for similar patients receiving routine care. Clinical trial participation did not adversely impact end-of-life care or likelihood of hospitalization. These findings suggest that patient participation in clinical trials does not increase total cost of care nor enhance financial risk to payers.[Table: see text]

New drug approvals and their effect on performance for participants in the Oncology Care Model

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Background: The Oncology Care Model (OCM) is an oncology-specific value-based care model that holds participating practices accountable for all costs of care. Medicare implements quantitative adjustments to model target costs in OCM, including a trend factor to reflect aggregate cost growth and a novel therapy adjustment for new indications. However, it is unclear how well these adjustments account for the emergence of new therapies that are evidence based and influence standard of care for an individual cancer type. We sought to investigate this by studying the impact that FDA approval for brentuximab vedotin (BV) in the first line setting in March 2018 had on OCM practice performance in Hodgkin’s Lymphoma (HL). Methods: We identified all HL OCM episodes attributed to Tennessee Oncology (TO), a large community oncology network of over 90 oncologists, during performance periods (PP) 3 through 6. HL episodes within the lymphoma bundle were identified through the use of individual ICD-10 coded diagnoses on claims for antineoplastic infusions and E&M visits. Using OCM performance data, our electronic health record, and claims data analytics software, we calculated average episode target costs, drug spending by drug type, and hospitalization costs to determine key determinants of OCM performance. Results: During the study period, there were 577 episodes of lymphoma attributed to TO, of which 28 were for patients with HL. TO’s OCM performance in HL was significantly under target in PP4 (under target by $13.5K) and significantly over target in PP5 (over target by $32.1K) after the updated BV FDA approval. Average episode spending on BV increased by over $45K during this timeframe, while OCM target cost increased only by approximately $19K. Despite the change in OCM performance, hospitalization costs and hospice utilization remained relatively stable. Conclusions: In the OCM, despite quantitative payment factors that in principle are intended to adjust target prices to reflect changing cost dynamics, significant gaps exist. These gaps can inappropriately shift risk to providers for the appropriate use of new indications, including those that change standard of care. The example of brentuximab vedotin in HL illustrates the difficulty in reaching performance benchmarks due to dynamics associated with the rising cost of drugs. Further methodological changes are needed in future oncology value-based care models to ensure accurate prediction of rapidly changing treatment costs for appropriate therapies. Hodgkin’s lymphoma OCM payment period cost and utilization comparison data.[Table: see text]

Using retrospective adverse event data to assess the impact of visitor management during a pandemic emergency plan at a community oncology practice

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Background: During the COVID-19 pandemic, our community oncology practice, with over 150 providers at 33 locations, incorporated infection control guidance from the CDC into our Pandemic Emergency Plan, including visitor restrictions at all locations. There was an increase in patient fall events in our clinics after visitor restrictions were implemented in March 2020, as there were fewer care-givers available in the clinics to assist patients. Methods: Using our adverse event reporting system, we abstracted and trended all safety events that involved patient falls from March 2019 through May 2020. We compared patient fall events during the period of visitor restriction (March-May 2020) to the same period in 2019, and to the 3 months preceding March 2020 and the implementation of COVID-19 restrictions. We report patient fall events per 1,000 patient visits. Results: Prior to COVID-19, patient fall events averaged .207 falls per 1,000 patient visits for March thru May 2019 and .137 falls per 1,000 patient visits for Dec 2019 thru Feb 2020. Following the implementation of visitor restrictions in March 2020, patient fall events increased to .271 per 1000 visits, with a vast upward trend resulting in .435 patient fall events per 1,000 visits in May of 2020 when the restrictions were tightened, more than double previous averages prior to COVID-19. Conclusions: Family members and care-givers play an important role in the patient’s care team. We are confident that the significant increase in patient falls in May 2020 is attributed to visitor restrictions. These findings support the vital role of family and care-givers in patient safety. They not only provide transportation, emotional support and information on patient health status, but assist with ADLs, ambulation and transfer needs during the patients’ visits to the clinics. Healthcare facilities are often under-resourced and under-staffed to fully address patients’ physical needs. Limiting care-givers during a pandemic may reduce the transmission of infection, but also may lead to other unexpected adverse events. Using these findings, we will be implementing standard fall prevention procedures. The practice’s emergency pandemic plan on visitor restrictions will also be amended to take this into account.

Utilization of telemedicine to meet the demand throughout the COVID-19 pandemic at a community oncology practice

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Background: A large community oncology practice in Tennessee participates in value-based payment arrangements, the success of which depends on close patient monitoring. Telemedicine as an innovative solution was initiated in 2017. The service was limited, due to regulation, licensure requirements, and lack of reimbursement, to survivorship visits, clinical trial consent visits, rural hospital consults and genetic counseling. During the COVID pandemic and loosening of restrictions, telemedicine services were expanded. Methods: We identified a cloud-based platform that allowed patients to use any device with a camera and microphone and required no software downloads. On-line training sessions were provided to clinical staff. All training and workflow implementation were completed in a 2-week time frame. Telemedicine was expanded to include surveillance, urgent care, psychology, palliative care and post-BMT visits as well as new patient consults for medical, radiation and gynecologic oncology patients. Patient satisfaction surveys were administered. Results: Our telemedicine visits increased weekly beginning March 1, peaking in the month of April with an average of 77 scheduled telemedicine visits per day across the practice. During the month of April, our practice saw a record clinical trial accrual in our Phase-1 Drug Development Unit with a 22% increase over the previous average. Patients who responded to a satisfaction survey were highly satisfied with the telemedicine visit with a 73% positive response rate. Nearly half of our eligible patients did not have the technology or broad-band access to be able to participate in telemedicine. Conclusions: Our prior experience with telemedicine, though limited, facilitated the development of an infrastructure that provided adequate number of devices and internet bandwidth capacity to support rapid expansion of telemedicine. We were able to maintain high quality care and access to clinical trials during the pandemic and see the value of this service long-term. We hope to add tele-pharmacy and care coordination services. Political leadership and patient advocacy groups should explore ways to ensure that all patients may benefit from this technology, especially those in under-served areas.

Partnership with an independent genetic counselor and standardized screening: Effect on the identification, referral, and genetic testing of eligible patients in a community oncology clinic

138

Background: A nine provider, community oncology clinic had limited local access to genetic counseling. Additionally, the practice had no process for identifying appropriate patients for genetic counseling or testing and no method to track referrals and test results. The practice partnered with a contracted genetic counselor and a study was completed to standardize screening and follow-up and to increase referrals and testing. Methods: Baseline data on genetic testing performed in 2018 was obtained from three major genetic testing labs. Based on the NCCN guidelines for genetic assessment, the practice created automated screening reports from the EMR, supplemented by manual chart review, to identify appropriate patients for genetic counseling. Front office, clinical and billing workflows were created. Patients were scheduled to see the counselor via in-person appointments or remotely via a HIPAA compliant telemedicine platform. The genetic counseling sessions included education and consent for testing followed by review and discussion of results. Consultations and genetic testing results were documented in the practice’s EMR. Results: Baseline data showed that the clinic tested 7 patients in 2018; 2 patients in the first quarter. During the pilot from Jan-Mar 2019, 34 patients were referred for genetic counseling; 30 consented to testing. This is a 329% increase over 2018; 1400% for the first quarter. Of the 30 patients tested during the pilot, 6 were positive for a pathogenic mutation. Conclusions: By contracting with a genetic counselor, and establishing procedures for screening, counseling, consenting, testing and follow-up, the practice was able to increase the number of appropriate genetic testing considerably. This process will be scaled to multiple sites of a community practice.

Impact of community practice on providing in-patient oncology and hematology consults via telemedicine to remote rural hospital

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Background: A rural oncology/hematology clinic located a far distance from the local community hospital was not able to provide hospital consultation support. Collaboration between the practice and the hospital resulted in a telemedicine pilot study to provide oncology and hematology consults to in-patients using a telemedicine robot that connected patients to a hematologist/oncologist (heme/onc) over the internet. Methods: When an appropriate patient was identified at the hospital, a referring provider contacted the heme/onc for a consult. The heme/onc determined and relayed the appropriate time to schedule and perform the telemedicine consult. The referring provider arranged for hospital staff to deliver the telemedicine robot to the patient’s room at the scheduled time. The heme/onc reviewed clinical data in the hospital EMR and logged into the telemedicine robot to speak in consultation with the patient. Notes and orders were placed in the EMR. Out-patient follow-up at the oncology/hematology clinic was scheduled as needed. Supporting front-office and clinical workflows were developed, and policy and procedure established. Surveys were sent to patients and referring providers. Results: At baseline, hospital consults were not provided. In 2018, there were 27 oncology/hematology consults, of which 89% (24 of 27) were for malignancies. 52% (14 of 27) were seen in the clinic after discharge. To date 40 telemedicine consults have been completed. Patient and referring physician satisfaction are inconclusive due to low survey return. Conclusions: Telemedicine provides an effective means to provide specialty consultative support to rural hospitals by remote community providers. Despite the complexity and sensitive nature of oncology and hematology concerns, the technology has been embraced by referring providers and patients.

Utilizing a case management system to reduce the response time for symptom management calls in a high-volume practice

170

Background: A five physician, three nurse practitioner,community oncology clinic experiences high call volumes (average 352 daily), many of which are related to side-effects of chemo/biotherapy and health related issues. A study was completed to decrease the time for a patient symptom management call to be addressed and concluded. Methods: Initial primary data were collected over a four month period (April-August 2015) using the phone system and the Electronic Health Record (EHR) reporting capabilities, by cross-referencing the caller-ID data with the EHR patient demographics data; only documented symptom management calls within the EHR were included. Twenty-nine percent (202 of 691) of symptom management calls were identified for the sample. Secondary data were collected prospectively using a handwritten call log completed by triage nursing, detailing the purpose of every call routed to triage nursing. Changes were made in the daily telephone call process to include a full-time operator, additional triage nursing staff, and implementation of a structured case management system. Follow-up primary and secondary data were collected for six weeks (August-September 2015) utilizing the case management system. Of the calls routed to triage nursing, 100% were captured in the case management system; call response time and call purpose were recorded. Results: During the initial primary data collection, a baseline of 48% of symptom management calls being addressed within 2 hours was established. Staffing changes resulted in an improvement to 68%, and an additional improvement to 73% after implementation of the case management system. Secondary data collection at baseline showed that 35% of calls were inappropriately routed to triage nursing; this improved to under 1% after implementation of the case management system. Conclusions: The reallocation of staff to concentrate on patient call processes, and the use of a case management system, significantly improved symptom management call response time. The implementation of a case management system nearly eliminated all inappropriate calls routed to triage nursing.