How can a Hospital Management System help in leveraging data analytics?
Is there any truth in the statement that better data leads to better care? Hospitals and public health authorities are increasingly using data to drive decisions about patient care, public health, preventive care, and operational efficiency. Data is collected and analysed in a centralised fashion to support both tactical and strategic healthcare decisions. Data is leading to life altering outcomes. Data is used to support impactful decision making. Actionable insights are being drawn out of both structured and unstructured data that is generated in the healthcare industry. Healthcare analytics helps in preventing medical conditions while keeping costs under control while improving the quality of life. The hospital should focus on deriving meaningful inputs from the massive amounts of data that resides in a Hospital Management System. Analysis of the data leads to developing models that can be prescriptive, predictive, or descriptive. Analytics improves data collaboration and hospital processes.
A Hospital Management System is usually scalable, customisable and integrates across all aspects of the hospitals operations from reception, appointments booking, consultation, outpatient journey, inpatient journey, and touchpoints with the various facilities available in the hospital such as Pharmacy, Lab, Radiology and Billing, among others. Each touchpoint generates data that can be tracked, measured, and analysed. Insights can be unlocked. Patient care is one area where data analytics can greatly help. Personalisation of patient care and patient treatment is possible when insights are drawn from data to come up with the treatment option that will work best for a patient. Effectiveness of various treatment options is possible.
Data analytics also helps in eliminating the use of unnecessary tests and visits to the hospital. Analysing patient loads data can help in reducing patient wait times and appointment no-shows. High risk patients can be identified, and appropriate treatment can be started on time. Analysing data can help in supporting clinical decisions. A clinical decision support system in the underlying Hospital Management System can help clinicians take appropriate treatment measures. The basis for the development of the Clinical Support System is the health records of the patients. The hospital can mitigate risks and improve the response speed. Accurate treatment is possible, and the healthcare team can take informed clinical decisions. Patient engagement is another area where data can support decision making. Data can be used to develop predictive analytics which is especially useful when treating patients with multiple health conditions. Preventive models can also be developed which can facilitate the prevention of diseases in patient groups. The hospital can look at trends and take preventive measures and track outcomes thereafter. Patient-centric or value-based care becomes easier to achieve.
Data from the Hospital Management System can be used to develop models which indicate where the healthcare team should focus more. Telemedicine can support the availability of data in real time using IOT devices. Alerts in real time are available. Doctors can focus attention on the more seriously ill patients or patients needing immediate attention. Care prioritisation is done better as the system automatically allocates scarce medical resources. Patient condition can be monitored anytime, anywhere. Quality of care and quality of life for the patients determine healthcare outcomes. Analytics can help in predicting the probability of infections and readmission. Patient demographic information such as age, gender, income, smoking and drinking history and social history, medical history, are all personal identifiable information (PII). It is very important that data be anonymised and all PII removed to ensure patient privacy and safety. It can thus be seen that using the data residing in a Hospital Management System, the hospital can provide patients with high quality care in an effective and cost optimal manner.
Hospitals can use data analytics to improve the quality of services provided and improve existing procedures. Data can help in automating various processes within the hospital. The hospital can evolve new approaches to healthcare delivery and compete effectively with competition. Insights can be derived on the procedures that are most profitable with the best outcomes and the hospital can look at strategies to focus in these areas. Newer patient catchments can be identified. Patient load data and the use of various equipment and facilities can be used to improve staffing and staff scheduling. The hospital can plan in terms of recruiting, training, and retaining such medical personnel. Equipment and asset utilisation can be maximised. Bed turnover can also be increased. The hospital can lower costs and simplify operations. Data helps in improving day-to-day hospital operations. Analysing data on equipment maintenance and preventive/breakdown maintenance can help in preventing equipment failure. Costs can be managed better. This means that hospital performance improves. Capacity building and planning is another key area where the hospital benefits with data analytics.
Staffing, medical infrastructure and equipment the three key pillars of a hospital can be in planned better. Analysing outcomes and costs also provides insights on training gaps among the healthcare staff. Data from practitioners provides the areas for improvement. Training and learning programmes can be tailored to meet and address specific needs.
Healthcare seeks to care for people and save lives. Data analytics provides customised, actionable and impactful insights that help in providing care to people and saving lives. There is a lot of information that the Hospital Management System stores in terms of the patient’s hospital journey across various touchpoints. The unstructured and structured data that is collected during this process can be analyses to develop descriptive, prescriptive, and predictable models. These can help in improving patient care outcomes, improve hospital efficiency and effectiveness, reduce cost and fill gaps that practitioners have through custom training and learning.