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How can predictive analytics prevent adverse events in healthcare?

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Adverse events are harmful and preventable incidents that occur in healthcare settings, such as medication errors, infections, falls, or surgical complications. They can cause patient harm, increase costs, and erode trust in the system. Predictive analytics is a branch of data science that uses statistical models and algorithms to forecast future outcomes and trends based on historical and current data. It can help healthcare organizations identify and prevent adverse events by providing insights, alerts, and recommendations for quality improvement. In this article, we will explore how predictive analytics can prevent adverse events in healthcare by addressing four key aspects: risk assessment, process optimization, decision support, and performance evaluation.

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Fitzgerald Felix Sakul, CPPS, CPHQ, FISQua

Chief Operating Officer | Leading Operational Excellence & Quality Standards | Transforming Healthcare Delivery…
Frank Resch, MScHQRS, MLT (CSMLS), MLS (ASCP)

Tenacious Quality Assurance & Improvement Leader | Regulatory Accreditation Strategist | Proactive Surveillance…
Dr. Achim Simons

Surgeon | Founding Partner and CEO of Bluerock Healthcare Advisors | Investor | Advisory Board Member

1 Risk assessment

Predictive analytics can help healthcare providers assess the risk of adverse events for individual patients or groups of patients based on their characteristics, conditions, and treatments. For example, predictive models can estimate the probability of readmission, mortality, or infection for a patient after a hospital stay or a surgery. These models can use data from electronic health records, claims, registries, or sensors to identify risk factors and patterns that may indicate a higher likelihood of an adverse event. By stratifying patients according to their risk level, healthcare providers can prioritize interventions, allocate resources, and monitor outcomes more effectively.

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Dr. Achim Simons

Surgeon | Founding Partner and CEO of Bluerock Healthcare Advisors | Investor | Advisory Board Member
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Predictive analytics is transforming patient care by identifying individuals at heightened risk of adverse events and implementing targeted interventions to prevent them. This proactive approach is significantly enhancing patient safety and reducing healthcare costs. One of the most prominent applications of predictive analytics in healthcare is its ability to predict readmission rates with up to 80% accuracy, allowing healthcare providers to intervene early and prevent unnecessary hospital admissions. Studies have shown that predictive analytics-based interventions can lead to a 20% reduction in readmission rates, saving hospitals millions of dollars in unnecessary costs.

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2 Process optimization

Predictive analytics can also help healthcare organizations optimize their processes and workflows to reduce the occurrence of adverse events. For example, predictive models can analyze data from various sources, such as scheduling, staffing, inventory, or equipment, to optimize the allocation of beds, nurses, drugs, or devices. These models can also identify bottlenecks, gaps, or inefficiencies in the delivery of care that may increase the risk of errors or delays. By improving the coordination, efficiency, and quality of care processes, healthcare organizations can prevent adverse events and enhance patient satisfaction.

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Frank Resch, MScHQRS, MLT (CSMLS), MLS (ASCP)

Tenacious Quality Assurance & Improvement Leader | Regulatory Accreditation Strategist | Proactive Surveillance Facilitator | Client Satisfaction Promoter
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When adverse events are tracked for trends by risk category, much can be gained in improving systems when reviewing near miss events. Near miss events demonstrate an opportunity to analyse where systems are falling before harm happens, allowing for proactive measures to be implemented to workflows, reducing the event type occurrence.

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3 Decision support

Predictive analytics can provide decision support for healthcare professionals and patients to prevent adverse events. For example, predictive models can generate alerts or recommendations for diagnosis, treatment, or prevention based on the best available evidence and guidelines. These models can also incorporate patient preferences, values, and goals to tailor the interventions to their needs and expectations. By providing timely, accurate, and personalized information, predictive analytics can help healthcare professionals and patients make informed and shared decisions that can prevent adverse events and improve outcomes.

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Fitzgerald Felix Sakul, CPPS, CPHQ, FISQua

Chief Operating Officer | Leading Operational Excellence & Quality Standards | Transforming Healthcare Delivery Nationwide & in Flores | Influencing National Healthcare Quality Landscape
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Decision support with predictive analytics unveils a challenge: the intricate dance between data and human nuances. The BMJ (Vol. 367, 2019) sheds light on patient cases defying predictive modeling, urging a nuanced approach. Striking the right balance between data metrics and healthcare intricacies is essential. Let's refine our strategy, embracing both the power and limitations of predictive analytics for a more holistic and effective decision and evaluation

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4 Performance evaluation

Predictive analytics can help healthcare organizations evaluate their performance and identify areas for improvement to prevent adverse events. For example, predictive models can measure and compare the rates, types, and causes of adverse events across different units, departments, or facilities. These models can also assess the impact of quality improvement initiatives, such as training, education, or feedback, on the prevention of adverse events. By using data-driven and transparent metrics, predictive analytics can help healthcare organizations monitor, benchmark, and improve their quality of care and patient safety.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can predictive analytics prevent adverse events in healthcare?

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1 Risk assessment

2 Process optimization

3 Decision support

4 Performance evaluation

5 Here’s what else to consider

Quality Management

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How can predictive analytics prevent adverse events in healthcare?

1

2

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1 Risk assessment

2 Process optimization

3 Decision support

4 Performance evaluation

5 Here’s what else to consider

Quality Management

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