Exploring the Impact of Patient-Centered Care Models in Oncology: Addressing Treatment Adherence and Support Needs

Patient-centered care (PCC) means treating patients as unique people. It also means knowing about their social situations and including them in decisions about their treatment. The Institute of Medicine (IOM) calls this a key part of good healthcare in its “quality chasm” report. Traditional healthcare often has doctors leading the visits as the main authority. Patient-centered care is different because it focuses on working together, showing kindness, and respecting what patients want.

In cancer care, there are strong reasons to use patient-centered care. Cancer treatments can be very complicated. Patients often need lots of support, information, and teamwork among doctors, nurses, and family. PCC helps patients better understand their illness, treatment choices, risks, and benefits so they can make good decisions. This is very important in cancer because treatments can cause serious side effects and the emotional stress is high.

Dr. Ronald M. Epstein, a researcher in patient-centered communication, says that good PCC makes patients feel “known, respected, involved, engaged, and knowledgeable.” These feelings can help lower patient stress and encourage them to stay involved in their care. For clinic managers, building these qualities not only improves health results but can also raise patient satisfaction scores. These scores often affect insurance payments.

Challenges in Treatment Adherence in Oncology

One big challenge in cancer treatment is making sure patients follow their therapy plans. Treatment adherence means how well patients do what doctors tell them, like taking medicine on time and finishing treatment. If patients do not stick to their plan, their illness can get worse. This can lead to more hospital visits and higher healthcare costs.

The Cumulative Complexity Model, made by Nathan D. Shippee and others, helps explain why some patients find it hard to stick to treatment. This model looks at the balance between a patient’s “workload” and their “capacity.” Workload means the demands from treatment and daily life, such as medication schedules and self-care. Capacity means what the patient can handle physically, their understanding of health, social support, money, and mental health.

If the workload becomes too much for the patient’s capacity, it is hard for them to manage treatment. Cancer patients often face heavy treatment and strong emotional stress, so this is a big problem. Medical practice managers need to know that helping patients follow treatments is not only about medicine. It also means helping with things that make treatment hard, like teaching, counseling, financial help, and making sure teams coordinate care.

Patient Support Needs in Oncology Care

Helping cancer patients well means more than just giving treatment. It includes teaching them about the disease and treatment, talking with them in ways that suit their needs, offering mental health support, and helping them handle the healthcare system. Amber Specialty Pharmacy shows this by providing special care and ongoing education for patients and their caregivers through its Oncology Center of Excellence (COE).

Amber Specialty Pharmacy also uses a special AI system to find patients who might not follow their treatment well early on. Finding these patients early means the clinic can give extra help before serious problems start. This lowers chances of treatment being stopped or missed. For managers in oncology, using this kind of technology can lead to better patient health and fewer costs from problems or readmitting patients to the hospital.

The Role of AI and Automation in Improving Oncology Care Delivery

Artificial intelligence (AI) and automation are starting to change many parts of healthcare and patient care. Oncology is also part of this change. Companies like Massive Bio and Amber Specialty Pharmacy are using AI tools to help find and manage patients for clinical trials. Their goal is to make it easier for cancer patients to join trials by using AI-powered platforms that simplify the process.

Massive Bio’s AI platform, backed by the National Cancer Institute (NCI), helps find clinical trials that fit each patient’s specific cancer and condition. Their Oncology Clinical Trial Command Center (OCTCC) is like a central point that gives real-time data to many patients, researchers, and doctors. This helps patients join trials faster and stay in them longer, which is important for treatments that need quick action.

These AI systems help cut down on paperwork by automating routine tasks such as checking if patients qualify for trials, following up, and collecting data. IT managers and practice administrators can use AI to make workflows smoother, reduce mistakes, and let staff spend more time directly helping patients.

AI also helps patient-centered care. It finds when patients might have trouble with their treatments, like signs they might stop taking medicine or face social challenges. This lets healthcare teams act fast and give tailored help. Amber Specialty Pharmacy’s use of advanced AI analytics shows how data can be used to improve patient support and treatment outcomes.

Automation can also help in front-office work like answering phones and scheduling appointments. Companies like Simbo AI offer AI-based answering services that reduce phone wait times and administrative delays. These systems make sure patient calls are answered fast, appointment and medicine reminders are sent regularly, and urgent matters get priority. This lowers staff workload and makes communication better, so patients stay more connected and satisfied.

Practical Considerations for Medical Practice Administrators

• Training and Communication: Staff should learn how to talk with patients in ways that respect their wishes and concerns. This means having nurses, pharmacists, social workers, and caregivers work together, not just doctors leading the talks.

• Investing in Technology: Buying AI systems that help with clinical trials and tracking treatment can improve care while keeping costs down. Tools that provide real-time information let teams change treatment plans quickly and use resources better.

• Enhancing Patient Access: Systems that help patients overcome challenges like location, money, and understanding improve treatment results. For example, Massive Bio’s project connects 60,000 cancer patients across the country to clinical trials, removing limits based on where they live.

• Supporting Workflow Automation: Automating front-office tasks like phone answering and scheduling reduces delays and makes the patient experience better. This helps avoid missed appointments and keeps care continuous.

• Measuring Care Quality: It is important to check not just if patients are happy but also if they understand their care, take part in decisions, and follow their treatments. New ways of measuring these things that include ideas from patients and doctors help clinics improve.

Patient-Centered Care in the Broader Oncology Ecosystem

The U.S. cancer care system is changing to better link patient-centered care with technology. There are more than 13,000 active cancer clinical trials at any time. Tools that help patients find and join trials are very important. AI and data analysis make the process easier, help develop drugs faster, and keep patients involved—meeting both medical and cost challenges.

The partnership between Massive Bio and Amber Specialty Pharmacy shows a way to use lots of patient data, payer information, and smart algorithms to build care plans that keep patients involved and supported throughout treatment.

For healthcare administrators, this change needs careful planning and investment. Staff training and technology must be balanced with rules and payment policies. Good leadership is needed to create care systems that meet patient needs and work well on the operational side.

By focusing on patient-centered care and using AI and automation, oncology clinics in the U.S. can better handle the tough needs cancer patients have. These methods help solve problems with treatment adherence, offer needed support, improve access to trials, and make clinic work run smoother, which together lead to better results in cancer care.