Utilization of Data: Room for Improvement with Patient Assistance

The healthcare industry is becoming increasingly data-driven, as organizations harness the power of big data, artificial intelligence (AI) and machine learning (ML) to enhance their operations, decision-making and overall performance. However, a recent survey conducted by the Healthcare Information and Management Systems Society (HIMSS) and healthcare data analytics company Arcadia highlights that less than 60 percent of health systems' data is being utilized for intelligent business decisions. This underutilization of data can have significant implications for patient assistance, impacting care quality, treatment decisions, and overall patient outcomes.

1. Inaccurate diagnosis and treatment recommendations

Access to high-quality, accurate data is essential for healthcare providers to make informed decisions regarding diagnosis and treatment. Lack of comprehensive data utilization means that physicians might miss crucial information about a patient’s history, lifestyle or genetic predispositions that could influence their treatment decisions. This could lead to misdiagnoses, ineffective treatments or even harmful interventions that could have been avoided with a more holistic view of the patient's data.

2. Fragmented patient experience

Health systems often consist of multiple departments and facilities, each holding different pieces of a patient’s healthcare data. When data is not utilized effectively across these different departments, patients may experience fragmented and inconsistent care. This fragmentation can make it difficult for patients to navigate the healthcare system and may result in them having to repeat tests, receive conflicting advice from different providers or miss out on philanthropic aid opportunities since multiple departments are working with the same patient.

3. Delayed care delivery

Inefficient data utilization can also lead to delays in care delivery. When healthcare providers lack access to timely and relevant patient data, they may spend additional time searching for information, waiting for test results or seeking clarification from other departments. When finding patient assistance, inefficient data utilization can result in delays in care that can be detrimental for patients with high-cost, chronic conditions, where timely interventions are crucial for successful outcomes.  

4. Reduced patient engagement

Patients who feel disconnected from their healthcare providers or believe that their providers don’t have access to all relevant information are less likely to engage actively in their care. This lack of engagement can lead to missed appointments, poor adherence to treatment plans and reduced patient satisfaction. Engaged patients, on the other hand, are more likely to follow their treatment plans, make healthier lifestyle choices, and report better overall health outcomes.

5. Increased costs

Inefficient data utilization can also result in increased healthcare costs. Redundant tests, misdiagnoses, delayed treatments and poor patient engagement can all contribute to higher costs for both providers and patients. By optimizing data utilization, healthcare organizations can streamline their operations, reduce waste and provide more cost-effective care.  

To address these challenges, healthcare organizations need to prioritize data accuracy, integration and utilization. By adopting advanced analytics platforms and integrating AI and ML tools, organizations can gain deeper insights into their patient data and enhance patient assistance. However, it is essential to balance these technological advancements with the human touch that is so vital in healthcare. By combining data-driven insights with compassionate care, healthcare providers can offer improved patient assistance and drive better outcomes.