The American Cancer Society reports 1.8 million new cancer diagnoses within the United States for the year 2020. Cancer remains the second leading cause of death in the nation. However, prompt oncology treatment can significantly improve cancer patient survival rates.
Unfortunately, hospitals and oncology programs may delay diagnosis and treatment for time-critical situations due to case backlogs and underperforming patient management practices. Patients too are choosing to delay or cancel appointments.
Hospitals and other healthcare providers may face issues from a clinical, operational, or financial perspective. AI technology can replace manual processes and eliminate the guesswork from standard procedures by implementing a consistent and accelerated workflow structure.
Patient treatment is the priority of every healthcare provider. Unfortunately, healthcare facilities may lack navigation nurses to provide cancer patients with the right care pathway. An AI-optimized patient management platform can effectively direct patients to the most suitable medical professional according to their condition.
AI systems will ensure that cancer patients receive the appropriate care and assistance based on their specific needs. Automated systems will provide relevant information from pooled clinical analytics that optimizes clinical decision support systems. Advanced patience intelligence can collate and interpret radiology and pathology reports to streamline oncology practices, empowering care navigation teams with improved efficiency.
The AI system can also allocate the earliest time-slots for cancer treatment through a closely monitored scheduling system (ideally within a window of 72 hours upon diagnosis). An automated clinical system will reduce the burden on doctors, nurses, and frontline staff while minimizing the risks of error and lapses.
Care teams will have the opportunity to invest their time in more value-added processes while the AI operates without disruption to expand the number of navigated patients. Under AI infrastructures, more cancer patients can expect to receive prompt diagnosis and treatment.
The complexity of cancer management involves multiple hand-offs that result in delays between primary and specialized care providers. An AI-moderated system will help facilities and program managers improve coordination by standardizing aggregate data for easy reference while reducing fragmentation risks.
AI takes mundane and repetitive documentation tasks out of the equation so caregivers can focus on value-added services like improving patient adherence and optimizing health outcomes. An advanced AI system may integrate sophisticated technological processes such as NLP and machine learning to provide accurate and reliable documentation and patient support.
Cancer care teams can dramatically improve patient queue periods, an automated system that prioritizes diagnoses, and treatments. Adopting a reliable healthcare AI solution can help hospitals and oncology programs improve productivity, equivalent to hiring additional navigation professionals.
Oncology care and treatment involve complex patient data and budget management, which requires close monitoring at every juncture. A systematic AI structure enables hospitals and oncology program managers to generate EMRs that provide greater transparency in spending. The elimination of labor-intensive manual processes can improve patient care management ROI while increasing patient satisfaction levels.
Advanced AI structures can help hospitals and oncology centers reduce data leakage instances during patient migration processes, a crisis that continues to undermine the healthcare industry. IBM reports healthcare being the top target of data breaches, costing healthcare facilities an estimated average cost of $8.6million in the United States.
The COVID situation (and any similar outbreak) brings a new wave of urgency in patient retention, with enterprise visibility expert Central Logic reporting that 38% of healthcare leaders lack the visibility tools to monitor data leakage. Oncology care providers need a reliable system in place that effectively monitors the transfer of patient data from primary caregivers, such as referrals from community hospitals.
Automated AI systems should include updated analytics that provides hospitals and oncology clinics with the key features that support the transition to value-based cancer care. Value-based healthcare establishes strong patient-doctor relationships based on improved care outcomes, which is necessary for navigated oncology treatment.
Additionally, coordinated access to real-time and stored patient data can help care providers streamline treatment processes and reduce the time and cost required in developing future oncology solutions. Through AI technology, cancer patients can expect prompter treatment, improved care quality, and the availability of more breakthrough treatments in the long-term.
The Life-saving AI of the Future
Digital Reasoning specializes in AI solutions that redefine the standards of patient management. Our leading services provide healthcare facilities with improved patient retention by raising overall satisfaction levels. Additionally, we facilitate prompt cancer diagnosis to help healthcare practitioners improve their efficiencies in life-saving technology.
Our customers from various hospitals and cancer centers reported a 97.5% improvement in cancer registry reporting times, translating to an estimated 39 times increase for some sites.
Visit our site to discover how your facility and program can provide your patients and their loved ones with an advanced, risk-free management system for faster treatment and improved care outcomes. Take the first significant step in overcoming the oncology backlog for good!
“Cancer Facts & Figures 2020.”, American Cancer Society, https://tinyurl.com/y5ym4nf2.
Alder, Steve. “IBM Security 2020 Cost of Data Breach Report Shows 10% Annual Increase in Healthcare Data Breach Costs., HIPAA Journal, https://tinyurl.com/y6dr5d9v.
“Central Logic Survey: 96% of Healthcare Executives See Patient Leakage as a Priority.”Central Logic, https://tinyurl.com/y6gh9n2v.
Spatharow, Angela, et al. “Transforming Healthcare with AI: The Impact on the Workforce and Organizations.”, McKinsey & Company, 2020, https://tinyurl.com/vu74atf.