Istvan Pataki, MD
Due to a variety of factors, today’s oncologists face multiple challenges in collecting, managing, and sharing their patients’ health data. One challenge involves the sheer volume of information that is typically generated in routine oncology care. This includes an increasing number of diagnostic, prognostic, and monitoring tests that physicians administer to plan, monitor, and adapt treatment to optimize safety and efficacy. Additionally, the growing use of multimodal therapy creates an increasingly complex treatment environment that may involve care providers across multiple disciplines and departments, such as medical oncology, radiation oncology, and surgery. Moreover, many patients may receive supportive care, such as nutrition counseling and mental health services, which must also be incorporated into their medical records.
The data deluge does not end when the patient completes treatment. Patients with cancer typically face years of follow-up care, which can be as frequent as every few months in the years immediately following treatment. For patients with relapsed cancer, accurate recording of prior therapy is essential for guiding additional treatment planning, because response to earlier therapy can be prognostic for the potential efficacy of second- or third-line treatment. Additionally, use of certain cancer agents may contraindicate patients for repeat use of those drugs or other therapies. Finally, with a growing emphasis on learning from each patient’s experience, many care providers and institutions also collect outcomes data that can be used to understand the interactions among demographic, diagnostic, and prognostic factors and how they influence response to different therapies.
Although the amount of patient data itself can be overwhelming, the challenge to effective and efficient data collection is exacerbated when the data collection platforms vary among departments or facilities and applications, such as the devices used to plan and deliver radiation therapy. The collection and sharing of data between institutions is still largely manual and paper based. This typically means that test results, patient histories, and information about prior or concurrent treatment performed outside a provider’s institution must be collected via fax and scanned before being added to the patient’s electronic health record (EHR) as a PDF file. Finally, physicians must also navigate an increasingly complex regulatory landscape related to the collection, retention, and security of patient data.
As a result of these factors, oncologists are spending an increasingly large percentage of their time managing patient data and generating documentation to complete a comprehensive treatment record. This leaves them less time to spend with patients and can also result in burnout. The growing demands of collecting and managing patient data increase the need for additional administrative staff to support patient data management, which can increase costs for care centers and providers.
Fortunately, a growing number of electronic and software solutions help mitigate the challenges of patient data overload. New EHR platforms can seamlessly integrate data from multiple sources, such as treatment planning software, treatment delivery devices, scheduling, and billing. Moreover, healthcare information technology innovators are capitalizing on advances in automation and artificial intelligence to reduce the amount of manual effort needed to capture, enter, and share patient data. Such advances include voice recognition technology that enables automated, real-time dictation and facilitates data sharing and follow-up actions.
Case Study: North Carolina Center
Cape Fear Valley Cancer Center is one of the largest cancer facilities in North Carolina. The institution is committed to improving the quality of life of all its patients, and the cancer care providers strive to achieve this goal through a patient-centered approach that emphasizes innovation, teamwork, and accountability. Until recently, its ability to realize this vision was hindered by a slow and expensive approach to EHR data entry, which entailed a cumbersome, multistep process that comprised dictation, transcription, and editing. Completing an entry took up to a week, making it difficult to achieve real-time tracking of patients who might have multiple appointments, tests, or procedures within that time frame. This slow and inefficient process was also very costly, requiring 3.5 fulltime transcriptionists and an outside agency to support 14 providers. Moreover, the effort to generate accurate and timely patient notes reduced the time physicians had available for patient care, creating time and cost inefficiencies for the cancer center’s staff and leading to physician burnout.