Predictive analytics in healthcare assesses current and previous healthcare data to garner insights about clinical and administrative workflows. These insights can later be used to make informed decisions to improve operational efficiency, identify patterns, manage diseases, and enhance the quality of care. One of the most promising implementations of healthcare predictive analytics is assessing patients’ health information and identifying who might be at greater risk of disease. Additionally, predictive medical solutions have implementations in public health as they help physicians understand epidemics better.
OSP can develop healthcare predictive analytics software to analyze patients' health information from many years to the present. This generates vital clues about the patient's medical situation and identifies if they are likely to develop a disease. This provides a predictive approach to care instead of a reactive one, enabling doctors to respond faster before the disease progresses, leading to better clinical outcomes.
Chronic diseases are the leading causes of death and disability in the United States and account for a large part of healthcare spending. OSP can design and develop predictive analytics healthcare solutions to assess clinical data about patients with chronic diseases and the treatments prescribed. This will highlight important insights for doctors regarding medications and the patient's interaction with them. As a result, doctors can make informed decisions and manage chronic diseases better.
Studies have estimated that fraudulent insurance claims cost billions of dollars in losses to insurance payers. Since payers need to process numerous claims daily, the likelihood of fraudulent ones getting approved tends to be high. But OSP can develop customized predictive health intelligence applications for payers to automate the process of claims adjudication and filter out anomalous claims for further scrutiny. This cuts down insurance fraud significantly.
We’ve reached out and found companies like OSP to create our technology. This is my first time working with a company that has been so thorough. These guys are amazing. If you really are looking for someone for a technology solution, these guys are the real deal.-- Stephen Carter
We reached out to OSP to provide an estimate on a technology solution we were interested in developing. From the initial conversation, the team was professional, courteous, and thorough. We were able to make a quick decision to move forward with OSP because we were confident that our requirements were accurately captured and the development deliverables and associated costs were clear.
The OSP development team stayed on schedule and within budget throughout the build phase and provided weekly communications to keep our team informed along the way. If we require application development in the future, OSP will be the first call we make.-- Selita Jansen
We have worked closely with OSP for two years, meeting twice a week to work through development requirements, strategy, design, progress, and support. OSP has become an integral part of our business, and our mutual teams work together as one team. OSP tackles problems that arise with integrity and operate with respect for budgeting.-- Charlie Langdon
Yes, I would certainly recommend their services because they were diligent and the offered price was very reasonable which is a challenge these days to get a great product at excellent pricing.-- Bert Lurch
We built a tailored RPM solution with telehealth features.
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We built a technology solution to address the shortage of Maternal Fetal Medicine experts.
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Predictive analysis, in general, is the process of analyzing data to highlight important insights and patterns to get a perspective on future events. Healthcare predictive analysis involves historical and current healthcare data to help clinicians and healthcare professionals serve their stakeholders better through informed decision-making.
Identification of At-Risk Patients
Analyzing patients’ health histories enables clinicians to identify which patient is likely to contract a disease. This will enable doctors to address the causes in the early stages before the disease becomes full-blown.
Better Management of Epidemics
Healthcare predictive analysis can process large amounts of medical data from epidemics to know more about the causes of an epidemic and how it spreads. This allows public health experts to implement policies to curb the spread and deal with epidemics better.
This is one of the most exciting benefits of predictive health analytics. By knowing who is more likely to contract which disease, doctors can prescribe preventive treatments before serious symptoms appear. This results in better clinical outcomes and lowers the costs of care in the long run.
Predictive health intelligence software can assess large amounts of clinical data on patient health alongside several parameters. This provides an insight into the best treatment for a condition since what works well for one patient might not do so for another. Such an individualized approach to care will lead to better outcomes for patients.
Preventing Hospital Readmissions
According to research, hospital readmissions cost billions of dollars to Medicare every year. Solutions for predictive healthcare analytics can assess multiple data points to know if a recently discharged patient is at risk of getting re-admitted. This knowledge helps clinicians to stage appropriate interventions and serve patients better.
Chronic Disease Management
About 40% of adults in America suffer from at least one chronic disease. Healthcare predictive analytics uses data points like demographics, health history, family history, treatments, medications, lifestyle, and others to know who might contract a chronic disease. This insight will enable them to begin interventions and treatments before the disease manifests, resulting in better health outcomes. Chronic diseases can only be prevented or managed, not cured. So, predictive medical solutions can be a major victory against chronic conditions.
Decision trees are among the most popular techniques in predictive health analytics. It consists of nodes connected by lines branching from them and interconnecting multiple nodes. Beginning at the top, decision trees allow users to proceed through a narrowing range of possibilities, each indicating certain outcomes. They can be used to classify multiple aspects and assess many possibilities.
Too Much Trust
One of the most discussed risks involving predictive analytics in healthcare is blind trust in the system. Since these solutions leverage cutting-edge technology, medical professionals may need to put more faith in their results. This could eventually result in a lack of consideration of all the pros and cons before deciding. Such rushed processes might result in unforeseen consequences.
People design the algorithms powering predictive medical solutions. So, it is possible for the algorithms to have the same biases as the designers. These might be unconscious ones or seemingly benign ones that the designers might not even realize they have. However, these get programmed into the solutions, which will end up producing biased results that might not be applicable.
Adaptability By Doctors
The increasing penetration of cutting-edge technology into the healthcare industry has resulted in doctors adapting to them continuously. Older doctors might find it challenging to adapt to new technologies as the years pass.
This is the first step for any data analytics in any industry. Data from all the relevant data points need to be compiled into a centralized repository with which data analytics programs can be used.
Querying the Data
Large datasets in the repository need to be appropriately queried for them to make any sense and be usable for decision-making. There needs to be means of querying the data for maximum effectiveness.
Incorporation of New Data
With a rapidly growing digital footprint in healthcare, there is a constant influx of newer data. This data needs to be sorted, stored, and managed in a way that would make it usable for analytics operations.
Implement Consistent Analytics Solutions
This is the last step in deploying predictive analytics in healthcare. The rules and parameters for analyzing data need to be programmed for the solution to take effect. The analysis and the subsequent insights would be based on those parameters. So they need to be accurate.
This is one of the most promising applications of predictive analytics in healthcare. By analyzing medical data and patient history, doctors can predict if a person will likely suffer from disease shortly. This allows them to provide relevant care services and prevent the patient from ever catching the disease.
Streamlined Clinical Workflows
Patient care involves several activities involving doctors and nurses. These include everything in the process flow of treatments. By analyzing these, clinicians can identify areas for improvement and optimize the clinical workflows to benefit the patients more.
This is also among the more promising trends in medical science. Predictive health analytics determines what suits a person best and allows doctors to administer treatments and dosages accordingly. What affects a person in a certain way might produce varying results for other people. But a personalized approach to care minimizes risks from potential side effects.
Lowered Healthcare Costs
The insights derived from predictive analytics solutions help clinicians target their treatments more effectively. Moreover, they also facilitate a faster response during the early stages of a disease, which ultimately prevents the worsening of patients’ health. These advantages lower the overall costs of care, in addition to facilitating better outcomes for patients.
Risk profiling involves identifying risks before they appear. When it comes to chronic care, identifying the people at higher risk of chronic diseases is the first step. This is done by accumulating data points from multiple sources and factoring parameters like family history, ethnic group, lifestyle, medical history, and others to identify the level of risk.
Predictive analytics in healthcare uses risk profiling tools to predict which patient is likely to contract chronic diseases. Such profiling helps providers recommend changes in diet and lifestyle and prescribe relevant medication. Additionally, payers can leverage predictive analytics to alter their health plans and premiums.
Data hold insights that help in making informed decisions. Assessing this data is what reveals those insights. When it comes to healthcare, data on patients hold vast quantities of actionable insights about various operations. These include patient care, clinical workflows, insurance processes, etc.
By leveraging data-driven insights, clinicians and other medical professionals can optimize and streamline all the workflows and processes. They can provide better patient care, help prevent diseases, recommend improvements to patients’ lifestyles, achieve better clinical outcomes, accelerate payer approvals for processes, and so forth. In this, predictive analytics in healthcare improves patient experience and benefits all stakeholders involved.