Healthcare Analytics Aid the Control and Rational Use of Medicines

Adverse drug reactions (ADR) events result in significant morbidity and mortality causing ADR’s to be ranked among the top 10 causes of death in the U.S. according to the findings of a study published in Journal of the American Medical Association (JAMA).

 

ADRs are a major reason  of hospital admissions and may be caused by the incorrect use of medications.  Furthermore, the probability of an ADR increases with the addition of each new medication to the patient’s existing medication regimen resulting in the loss of drug effectiveness and the waste of financial  resources.

 

The “rational use of medicines” is an effective intervention strategy to reduce the ADRs.   It is achieved through communication between all parties and tracking of medication with information management system.

 

According to the World Health Organization (WHO), the rational use of medicines means that patients receive medication appropriate to their clinical needs, at doses appropriate to their individual requirements, for an adequate period and at the lowest possible cost to themselves and to the community.

 

Inappropriate and excessive use of medications is a financial burden for patients and their healthcare insurers and can result in considerable harm to the patient by not delivering effective results and the possible incidence of adverse drug reactions.

 

“The World Medicines Situation – 2011” study published by WHO estimated that globally over half of all medications are prescribed, dispensed or sold inappropriately and that half of all patients fail to take their medication as prescribed or dispensed.

 

The World Health Organization advocates 4 key interventions to promote more rational medication use such as:

  1. Supervision
  2. audit and feedback
  3. use of clinical guidelines
  4. use of appropriate and enforced regulation

 

 

Healthcare Analytics Tools to Automate and Improve Rational Use of Medicines

To comply with the key interventions suggested by the WHO Conexia uses healthcare analytics tools for its clients with the objective of embedding the rational use of medications doctrine into the existing clinical guidelines. As a result, clients leverage from the application of Healthcare Analytics software achieving not only better healthcare outcomes, but optimized utilization of financial resources as well. 

 

 

Collect Data, Find Patterns and Create Alerts

In order to help clients make informed decisions about the rational use of medicines, the Conexia team of healthcare experts identified the prescription patterns. As a result of these findings, alerts were generated to detect aspects related to:

  1. Patients prescribed with multiple medications (when there are 5 or more medications are being concurrently administered),
  2. Medication interactions
  3. Quantities
  4. Costs and suppliers

These alerts are managed by the client’s medical audit team communicating actionable data to the healthcare providers.

 

 

Descriptive and Predictive Analytics

To understand the underlying factors and get a full picture of what is really happening in a workers compensation healthcare entity we start with descriptive analysis. This analysis is done by taking two different types of measurements; one related to patient quality of life and the other related to the optimization of financial resources.

  1. The patient quality of life was measured according to the monthly quantity of medications they took such as patients with multiple medications (5 or more medications per month) and patients with previously documented ADRs.
  2. To optimize the financial resources, the analytics software measures the average monthly amount of medication prescribed to a patient, the average cost of each individual medication, and the average cost of all patient medications.

 

Once the descriptive analysis is completed, the predictive analysis is generated. The predictive analysis uses a variety of statistical modeling and machine learning techniques (also known as Artificial Intelligence) to study recent and historical data and predict the outcome of future decisions.  In this case a “joint point regression” model was used by building a temporal series which was constructed to identify and predict future trends of the prescription patterns using the following characteristics:

  1. Multiple medication prevalence
  2. Interactions
  3. Costs

 

 

The three main benefits of applying the strategy were:

  1. In a patient population where 19.5% was deemed to be multi-medicated,  the strategy obtained a reduction to 12%.

 

Prescription Drugs per Patient per month:

January 2015 to June 2017

 

 

2.  Patient safety (likelihood of an adverse event due to drug interactions) was strengthened  by a reduction from 43% to a post strategy level of 12%

 

Comparative interactions year 2015 Vs January – June 2017

 

 

 

 

3. A  decrease in the average number of prescribed medications  starting at 3.5 and reduced to 2.5 prescriptions per patient resulted in a 26% decline in the average cost per patient prescribed.  This positive trend change represented a 45% savings in total patient medication costs  compared to before the application of the Rational Use of Medicines Strategy.

 

Medications Formulated per Patient per month:

January 2015 to June 2017

 

 

Key factors of success:

  1. Multidisciplinary participation of  clinical care team, the insurer, and an exclusive Conexia client support team
  2. Raising multiple medication awareness among  all stakeholders
  3. Management and audit of the alerts generated by the Conexia system  

 

With the addition of Healthcare Analytics, Conexia was able to identify cost deviations and help improve the quality of services rendered to patients. Through the use of descriptive and predictive Healthcare Analytics software which provides actionable interventions via customized alerts, Conexia can assist  healthcare entities to achieve better health outcomes by reducing the ADR burden through the automation of a rational use of medicines strategy.

 

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