Just like any other field, healthcare has also seen high proliferation of technology. Artificial intelligence, analytics and machine learning are some of the most frequently discussed subjects in healthcare technology We are seeing how machine learning and AI has benefited numerous industries; undoubtedly, it can be leveraged to improve healthcare as well. In the realm of analytics, predictive analytics seems to hold the key.
Predictive analytics can be used to provide better care to patients, improve the management of chronic diseases, achieve greater efficiency in hospital administration and supply chain. Healthcare systems need to define what predictive analytics means to them, and how they can use it to get the maximum benefits.
Predictions are the most useful when the knowledge is actionable. It is crucial that there is a willingness to intervene; only then can the power of data – both real time and historical, be truly leveraged. When the intervention and the predictor are integrated in the same system and workflow where trends occur, only then can the efficiency and value be gauged properly.
Predictions allow physicians, financial experts, and administrative staff to get alerts about potential events before they happen, and therefore make more informed choices about how to proceed with a decision.
It’s always good to be one step ahead of events, but it’s most evident in the areas of emergency care, intensive care and surgery. A patient surviving could depend on speedy reaction time and an intuition about something that could go awry.
However, the use of predictive analysis is not limited to these realms; they may also not always include real-time alerts that necessitate a team acting immediately.It’s always good to be one step ahead of events, but it’s most evident in the areas of emergency care, intensive care and surgery. A patient surviving could depend on speedy reaction time and an intuition about something that could go awry.
However, the use of predictive analysis is not limited to these realms; they may also not always include real-time alerts that necessitate a team acting immediately.
Implementing Predictive Analytics across the Healthcare Organization
Risk Scoring For Chronic Diseases, Population Health
Population health management is one area where predictive analytics can provide significant benefits. Organizations that can pinpoint people with high risk of developing chronic conditions in the very initial stages have a high likelihood of helping them keep long term health problems at bay.
Risk scores are created on the basis of patient generated health data, biometrics, lab test results, claims data and social health determinants; these provide insights to healthcare providers about the individuals that can benefit from improved services or wellness practices.
Avoiding 30-Day Hospital Readmissions
Under the Medicare’s Hospital Readmissions Reduction program (HRRP), healthcare systems can be heavily fined if the patient needs readmission within 30 days. Predictive analytics can be used to warn providers when a patient’s risk factors show a high likelihood of readmission within 30 days. Patients may contract certain infections during their hospital stay; analytics tools can pick out them patients with traits that indicate high chances of readmission. Healthcare providers can take extra precautions and educate the patient n follow up visits and so on, to prevent quick readmission.
Preventing Patient Deterioration
Often, patients contract infections or develop sepsis while in hospital – sometimes, they just take a turn for the worse because of their existing state of health. With data analytics, providers may be able to react quicker whenever a change is detected in the patient’ s condition, and may also successfully recognize a potential deterioration before the symptoms manifest themselves fully.
Machine Learning is pretty apt for predicting clinical events in a hospital – for example, development of sepsis, or an impending heart attack.
When patients book appointments but don’t show up, it can disrupt the clinician’s workflow, and also affect the organization financially. Predictive analytics may be able to identify patients most likely to skip appointments without prior notice. This will enable organizations to offer slots to other patients. This has three effects:
- Cuts revenue losses
- Provide quick access to care
- Increased patient satisfaction
Providers can also use the data to send extra reminders to patients who may not show up, or offer services like transportation to ensure that they get to their scheduled appointments – or reschedule the appointment.
Preventing Suicide And Self-Harm
A look at data can throw predictions about the individuals who are likely to harm themselves; healthcare providers can make sure to provide counseling or other services to ensure that such events, including suicide attempts, do not happen. Substance abuse, prior attempts at self-harm, and use of psychiatric meds, are all predictors.
Predicting Patient Utilization Patterns
Predictive analytics can also help predict utilization patterns to help ensure optimum staffing. Emergency departments and urgent care centers do not have fixed schedules, and so need to adjust their staffing levels to handle to fluctuations in patient flow. There should be sufficient beds in inpatient wards so that patients who need it can be admitted. Outpatient clinics and physician offices should also be appropriately staffed so that patients don’t have to wait long.
Managing The Supply Chain
The supply chain is one of the biggest source of the healthcare providers’ expense; PA can help them to cut unnecessary expenses and enhance efficiency. Tools help executives to reduce variation and provide actionable insights into order patterns and utilization of supply.
AI and PA play a huge role in improving cyber security; tools monitor patterns in data access, use and sharing, and provide alerts when variations occur, especially if the change indicates nefarious users.
Developing New Treatments
Analytics can help researchers and providers with drug discovery techniques and supplementing conventional clinical trials. ‘In Silico’ testing helps to minimize patient recruitment for conducting expensive trials, as well as to hasten the evaluation of new therapies. Currently, such models are is use for trials of Alzheimer’s, Parkinson’s, and other degenerative conditions. PA and tools that support clinical decisions are also enabling the translation of new drugs into precision therapies.
CDS systems may be able to predict patient response to specific treatments, by comparing genetic information with results from prior cohorts, enabling providers to choose the therapy most likely to succeed.