Predictive Analytics and Clinical Intervention

When we talk about clinical intervention, more times than not it is being talked about in reference to quality improvement in clinical matters. One way that clinical quality can be improved is through the application of predictive analytics and to do this you need Predictive Analytics Vendors.

Clinical quality improvement is a continuous process, not just a one-off activity. And when improvement is carried out using predictive analysis, it can lead to measurable improvement in care services and the outcome status of specific patient groups. Clinical intervention is about helping healthcare professionals and healthcare organizations navigate the process of getting better and improving in effective ways.

Clinical intervention focuses on helping with long-term improvements that have lasting effects and this is done with the help of data tracking. With long-term data tracking, clinicians can learn from prior mistakes or inefficiencies and this leads to better outcomes. These better outcomes spread throughout the organization not just with patients.

Internal work processes need to be understood and fine-tuned when necessary because at some point internal workings translate to patient care and efficiencies. Patient care and outcomes learn from the past and should be recorded in a manner that provides insight quickly. One of the most effectual ways to do this is Statistical Process Control (SPC)

Statistical Process Control (SPC)

Statistical Process Control which is also referred to as SPC is defined as a control chart where data is recorded to give a visual account of events. This image helps in a number of ways to find patterns, show outliers, point out inefficiencies, and bring forward patterns that are positive. Statistical Process Control (SPC) is used a lot in the manufacturing industry. It is an industry-standard methodology for measuring and controlling quality during the manufacturing process. Quality data in the form of Product or Process measurements are obtained in real-time during manufacturing.

Tracking Predictive Analysis Vendors Using SPC

When it comes to tracking predictive analysis vendors, this kind of tracking is not so much for seeing short-term changes and trying to refine the process from there, but is for long-term views to establish a baseline, understand patterns within that particular organization and making comparisons. Too many facilities have been tempted by changes reflected on an SPC graph reflecting big changes in the short amount of time and have tried to tweak their process flow too soon. The implemented changes required many people to change routines or generally inconvenienced various positions, only to find out later that the short-term results were more of an anomaly.

The strong suit of Statistical Process Control as it relates to clinical interventions are:

  • Process variations over time
  • Differentiate between random, special-cause and assignable variations
  • Identify and eliminate unwanted assignable variations
  • Assess the effectiveness of changes

Healthcare organizations and practitioners need to select areas in which they want to implement their clinical intervention and improvement strategies. Selecting an area or areas to which improvement processes should be implemented isn’t as arbitrary as a gut feeling. Clinical quality improvement processes are developed to find the greatest return on investment. This may sound a little harsh, especially when talking about the healthcare practices, but no company or organization can run in the red and still remain open to serve the public.

Additionally, many areas of service may already be running very efficiently and thus do not require any sort of improvement course of action. So when a healthcare organization is financially viable, they can better focus on maintaining a high quality of care. This might be different for non-profit healthcare organizations who get funding from charities and donations. But even these organizations also have to allocate their finances for optimum outcomes.

As careful consideration is made, it is also helpful to know that following this method has proven time and time again to improve clinical outcomes, increase patient satisfaction levels and reduce costs. This kind of assurance helps to offset any feelings of risk versus reward scenario.

When a healthcare organization has resources to allocate and need to select a project or region to focus on, there are a number of steps in the planning stage that must be considered. These areas are:

  • Establishment of project selection criteria
  • Identifying all potential projects
  • Assignment of points and ranking system set forth
  • Selection of project(s) made based on rank and availability of resources

When data-driven decisions are being made about finances, then clinical intervention can take place. But this is also true for all other types of data-driven decisions. Just because numbers improve dramatically doesn’t mean immediate success, and just because numbers don’t immediately reflect changes doesn’t indicate failure. Corrections take more time and patience, plus added vigilance to recognizing what areas should expect changes. There could be a hugely positive initial reaction, but later more fine-tuning will be required. As comprehension of clinical interventions is received, more understanding about other areas of concern may be answered. Clinical quality improvement happens slowly over time.


In conclusion, let us examine the crux of what clinical intervention is. What is Clinical Intervention? It is the culmination of time, patience, insight, want for more quality, desire to change and even acceptance of some risk (with the help of data-driven information). Interventions don’t have to be uncomfortable or difficult, because they can produce a more efficient and effective healthcare organization and ultimately serve the purpose of providing better results to anyone seeking better care and those providing that care.