Healthcare data is information that is collected about patients, providers, and other entities in the healthcare industry. This data can be used to improve patient care, understand population health trends and support research.
There are many different types of healthcare data, including electronic health records (EHRs), claims data, clinical trial data and genomic data. Healthcare data is often stored in databases or data warehouses. Data analytics can be used to mine this data to find patterns and insights that can help improve patient care.
Healthcare data is important because it can be used to improve the quality of care and make it more cost effective. Additionally, by understanding population health trends, healthcare providers can better target interventions to improve the overall health of their communities.
Various tools are used to store and analyze the data that has been collected, so let’s take a look at these tools in a little more detail.
Electronic health records (EHRs)
An electronic health record (EHR) is a digital version of a patient’s medical chart. EHRs can be shared with other healthcare providers, which can make coordination of care easier and more efficient.
EHRs can improve the quality of care by providing clinicians with easy access to a patient’s complete medical history. This information can help clinicians make more informed decisions about diagnosis and treatment.
EHRs can also help to improve population health by giving researchers access to de-identified data that can be used to identify patterns and trends in disease. This information can be used to develop new treatments and prevention strategies.
Personal health records (PHRs)
A personal health record (PHR) is a digital file that contains a person’s health information. The PHR can be created and maintained by the individual or by their healthcare provider. PHRs are becoming increasingly popular as they offer patients a way to easily track and manage their health information.
There are many benefits of using a PHR. These digital files can help patients keep track of their medical history, medications, immunizations and test results. They can also help patients manage their appointments and medications. In addition, PHRs can give patients quick access to their health information in case of an emergency.
While there are many benefits to using a PHR, there are also some risks. One risk is that if the PHR is not password-protected, it could be accessed by anyone who finds it.
Electronic prescription services (E-prescribing)
Healthcare providers are under constant pressure to do more with less. They are looking for ways to improve efficiencies and patient outcomes. One area that is ripe for improvement is prescription management.
E-prescribing, or electronic prescribing, is the process of sending prescriptions electronically from a healthcare provider to a pharmacy. This can be done through a secure website, an email system or even a dedicated app. E-prescribing has many benefits over the traditional paper prescription method.
For healthcare providers, e-prescribing can save time and reduce errors. In one study, e-prescribing was found to save physicians an average of 15 minutes per patient visit. Other studies have shown that e-prescribing can reduce medication errors by up to 50%.
Patient portals
Patient portals are web-based applications that allow patients to access their health information and connect with their healthcare providers. They are a key part of the healthcare industry’s move towards patient-centered care.
Patient portals can give patients 24/7 access to their health information, which can help them make more informed decisions about their care. They can also help patients stay connected with their healthcare providers and make appointments and refill prescriptions.
Patient portals can be a valuable tool for both patients and healthcare providers. They can help improve communication and coordination of care and ultimately lead to better health outcomes.
Master patient indexes (MPI)
A master patient index (MPI) is a database that contains demographic and medical record information for patients in a healthcare organization. The MPI is used to identify duplicate patient records and to link together records from different systems within the organization.
The MPI is important because it helps to ensure that the correct patient information is associated with the correct medical record. This can prevent errors in care, such as wrong medications being prescribed or incorrect lab results being sent to the wrong patient.
The MPI can also be used for data analytics. For example, analysts can use the MPI to track how often certain diagnoses are made or to identify which treatments are most effective for certain conditions. This information can then be used to improve care quality and outcomes.
Smartphone apps
There are a number of health-related smartphone apps that can be used to track data and analytics related to one’s health. Some of these apps include Fitbit, MyFitnessPal and Endomondo. These apps allow users to track their fitness progress, set goals and see how they are doing over time.
While some people may be hesitant to use these apps because they feel like they are being monitored or because they do not want to share their personal health information with others, the reality is that these apps can be very helpful in keeping track of one’s health and progress. Additionally, many of these apps have privacy settings that allow users to control who sees their information.
Overall, data analytics in the healthcare industry is becoming increasingly important as more people turn to technology to manage their health.
Different types of healthcare analytics
There are different types of healthcare analytics, each with its own strengths and weaknesses. The most common type is descriptive analytics, which can be used to identify patterns and trends in data.
However, it cannot be used to predict future events or trends. Another type of healthcare analytics is predictive analytics, which uses data mining and machine learning techniques to predict future events. However, predictive analytics is often less accurate than descriptive analytics.
Finally, there is prescriptive analytics, which uses data to recommend actions that should be taken to improve outcomes. Prescriptive analytics can be more effective than other types of healthcare analytics, but it requires more data and is more difficult to implement.
We’ll discuss each type of healthcare analytics in more detail below.
- Descriptive analytics
Descriptive analytics is the process of organizing and analyzing data to describe trends and patterns. It is used to answer questions about what has happened in the past and can be used to predict what will happen in the future.
Healthcare organizations use descriptive analytics to track patient outcomes, identify areas for improvement and make predictions about future trends. This type of data analytics is important because it can help improve patient care and reduce costs. By tracking patient outcomes and identifying areas for improvement, healthcare organizations can make informed decisions about where to focus their resources.
- Predictive analytics
Predictive analytics is a relatively new tool that is being used in the healthcare industry to help identify potential health risks and trends. This type of data analysis can help healthcare providers to better tailor their care to individual patients and groups of patients. Additionally, predictive analytics can be used to help plan for the future health needs of a population. For example, if a certain disease is on the rise in a certain area, predictive analytics can help healthcare officials allocate resources accordingly.
The potential benefits of predictive analytics are vast, and it has been shown to improve patient outcomes and satisfaction rates. A study published in the Journal of Healthcare Information Management found that hospitals that implemented predictive analytics experienced a decrease in readmission rates and length of stay. Furthermore, another study published in BMC Medical Informatics and Decision Making found that patients who were treated with predictive analytics were more likely to be satisfied with their care.
- Prescriptive analytics
Prescriptive analytics is a branch of data analytics that uses data to generate specific recommendations for decision makers. It is similar to predictive analytics but also takes into account factors such as resources, constraints and preferences to generate more specific recommendations.
Healthcare organizations can use prescriptive analytics to improve clinical decision-making, optimize patient care pathways and reduce costs. For example, prescriptive analytics can be used to identify the most efficient care pathway for a patient with a particular condition, or to recommend the most effective course of treatment for a patient based on their medical history and current health status.
Prescriptive analytics is an important tool for healthcare organizations because it can help them make better decisions about how to allocate resources and provide care. By using data to generate specific recommendations, prescriptive analytics can help healthcare organizations improve patient outcomes and reduce costs.
Healthcare solutions identified by data analytics
In the healthcare industry, data analytics is used to identify patterns and trends in order to improve patient care. By analyzing data from patients’ medical records, doctors and other healthcare professionals can better understand how diseases progress and what treatments are most effective.
Data analytics has already led to some major breakthroughs in healthcare. For example, researchers have used data analytics to develop a more accurate test for predicting whether a person will develop Alzheimer’s disease. This test could help doctors diagnose the disease earlier and begin treatment sooner.
Data analytics is also being used to personalize cancer treatments. By analyzing a patient’s tumor DNA, doctors can match them with the best possible treatment plan. This approach is helping more people survive cancer and reducing side effects from treatment. Data analytics is also helping doctors improve the quality and value of care for patients. The Patient-Centered Outcomes Research Institute (PCORI) has used data analytics to identify treatments that provide the greatest benefit to people with heart failure, at a lower cost.
Predictive modeling
Predictive modeling is a type of data analytics that is used to make predictions about future events. This can be used in the healthcare industry to predict things like demand for certain treatments, how effective a new drug will be or what the outcomes of a clinical trial will be.
Predictive modeling is important because it allows healthcare organizations to make more informed decisions about planning and allocation of resources. It can also help to identify potential problems early on so that they can be addressed before they become major issues.
However, predictive modeling is not without its challenges. One major challenge is that it requires access to large amounts of data, which can be difficult to obtain in the healthcare industry. Another challenge is that predictive models are often complex, and it can be difficult to understand how they work and how accurate their predictions are. This can lead to healthcare organizations having a lack of trust in the forecasts generated by predictive models.
Reductions in healthcare costs
Though the healthcare industry has been notoriously bad at managing costs, data analytics may help to change that. By collecting and analyzing data on everything from patient behavior to treatment outcomes, healthcare providers can start to see where they can make improvements that will lead to cost savings.
One area where data analytics is already making a difference is in readmissions. By identifying which patients are at risk of being readmitted, hospitals can take steps to prevent it from happening. This not only saves money, but it also improves patient care.
Data analytics can also be used to improve population health. By understanding which populations are most at risk for certain diseases, healthcare providers can target their prevention and treatment efforts more effectively. This can lead to both cost savings and improved health outcomes.
In the long run, data analytics has the potential to help the healthcare industry finally get a handle on its costs.
Studying for a career in data analytics within the healthcare industry
Data analytics is critical for the healthcare industry because it helps to identify patterns and trends in patient data. This information can be used to improve patient care, reduce costs and increase efficiency.
There are many different career paths in data analytics, such as working as a data analyst, data engineer or data scientist. Healthcare organizations will need individuals with these skills to help them make sense of the large amounts of data they collect.
Individuals who are interested in a career in data analytics should consider studying mathematics, statistics and computer science. There are many online courses and programs that offer training in data analytics with one of the best being the executive master’s of health administration online Canada qualification in conjunction with the University of Ottawa’s Telfer School of Management, a unique qualification that combines healthcare and business management.
The future for data analytics in the healthcare industry
The healthcare industry is under pressure to improve patient outcomes while reducing costs. Data analytics can help healthcare organizations meet these challenges by providing insights that can guide decision making.
There is a lot of potential for data analytics to improve healthcare. For example, data analytics can be used to identify patterns in disease outbreaks, track the spread of infectious diseases or predict how a disease will progress. Additionally, data analytics can be used to study which treatments are most effective for certain conditions and to evaluate the quality of care delivered by providers.
In the US, a study conducted by the Center for Disease Control (CDC) and published in April 2016 explored the use of data analytics to identify cases of infectious diseases. In this study, the researchers used data from a previous outbreak of the West Nile virus to test their approach. The team found that their system was able to accurately identify cases of the disease from public health reports with 90% accuracy. This is promising, as it shows that data analytics can be used to identify outbreaks of infectious diseases.
The researchers also found that their system was able to identify cases of West Nile virus in counties where the disease had not been reported. However, this may be due to the fact that the study took place in a region where the disease was endemic.
The researchers said that their system could also be used to identify other infectious diseases such as Zika, dengue and chikungunya. The researchers are now investigating how to use their system in the real world. They plan to work with local health departments and state public health agencies to determine which areas have a high risk of disease outbreaks.
This will help them identify regions where they can focus their efforts. They will also work to improve the accuracy of their predictions by incorporating more data sources. The researchers hope that their system will one day enable health officials to identify areas at risk of disease outbreaks in a timely manner.
The above is just one example of how data analytics is being used in the healthcare sector. Many companies are working on developing new tools to improve healthcare delivery. It’s safe to say that the future for data analytics in healthcare looks promising.