Healthcare Fraud Waste and Abuse- From Detection to Prevention

Big data is helping businesses save costs, gain a competitive advantage or identify new opportunities, and the healthcare industry is no different. Big data technologies help predict illnesses, save lives and improve the overall quality of life.  With modern predictive methodologies in place,chronic diseases and illness can be predicted before the manifestation of symptoms. This helps in early interventions resulting in saved costs, resources and lives.

Provider Fraud Waste and Abuse

Healthcare fraud, waste, and abuse means-

• Fraud- Intentional deception
• Abuse- improper billing
• Waste- carrying out unnecessary treatments

Such practices cause losses in the billions each day for the healthcare industry. Efficient and advanced IT solutions play a crucial role in reducing such waste. 

Risk Stratification Tools – A Helpful Approach To Assess Health Risks

Risk stratification is an estimate of the probability of a person suffering from a particular disease. It analyzes the benefits from a particular treatment a person is taking. The assessment helps providers and doctors analyze their patients and make informed and personalized treatment decisions. Care coordination is a key component of patient-centered medicine.

Risk Stratification Methods

Different methods are available for risk stratification. A Risk Stratified Model of Care includes following methods:

• Hierarchical Condition Categories (HCCs)
• Adjusted Clinical Groups (ACG)
• Elder Risk Assessment (ERA)
• Chronic Comorbidity Count (CCC)
• Minnesota Health Care Home Tiering (MN)
• Charlson Comorbidity Measure

Hospital Acquired Infection Prevention: Better Healthcare Delivery

A hospital acquired infection is an infection that is picked up from a healthcare facility. It can be spread within hospitals and other clinical settings. These infections are caused due to microorganisms, which originate from a hospital environment, staff or other patients. The most common infections are urinary tract, upper-respiratory infections (pneumonia), and surgical wound infections. Infections that become clinically visible after 48 hours of hospitalization are considered hospital-acquired.

Susceptibility to infections in a hospital:

Patientsin the hospital are often at the risk of suffering hospital acquired infections. The populations that are more susceptibleto such infections include:

• Premature babies and sick children
• Elderly and frail
• Patientswith chronic medical conditions (e.g. diabetes)
• Persons having compromised immunity 

Repercussions of hospital acquired infections:

Micro organisms including fungi and bacteria are among the major causes behind infections in a hospital setting that contribute to about 99,000 deaths each year.

Some of the consequences of to such infections include:

• Prolongedsuffering for the patient
• Longer hospital stays
• Longer recovery time
• Mounting costs associated with longer stay in hospital and prolonged recovery time

Causes of health acquired infections:

Infection is spread to the susceptible patient in the clinical setting in various ways such as:

-          Contact with healthcare providers

-          Contaminated equipment

-          Infected air droplets

-          Indwelling catheters

-          Infected food, water or medications

Measures to reduce hospital acquired infections:

The infection can be controlled by following the appropriate protocols as follows:

• Rigorous hospital infection control procedure and policies
• Frequent hand hygiene measure by all the hospital staff and patients
• Cautious use of antibiotic medication
• Use of Personal protective equipment
• Isolation of infectious patients

Avoidable ER Visits Can Effectively Reduce The Cost of Treatment

Avoidable emergency room (ER) visits are a critical component of population health management. Frequent and avoidable ER visits are very costly, especially when the same care could be available in a less expensive and non-emergency setting.

Around 30% of ER visits fall into the category of “frequent fliers” and represent the patients with manageable chronic conditions. Such individuals may visit the hospitals more than 10 times a year. In fact about 80% of all healthcare costs merely spent on just one-fifth of the population. The improper use of ER services is a major source of waste in a healthcare system.

Solutions like Jvion’s RevEgis help hospitals predict ER visits and better administer ER costs based on:

30-day ER predictions

30-day inpatient predictions

High-utilizer/"frequent flier" predictions

Unreimbursed ER care predictions and forecasts

ER high utilizers : At a glance

ER high utilizers are typically the people of modest means and poor health who go in and out of emergency rooms day after day. The root of their health issues is rarely resolved incurring ever-growing cost to hospitals, municipalities and taxpayers.

Such people suffer immensely from chronic conditions and live an area with restricted access to outpatient care facilities. Emergency departments become the primary provider for many who are not able to access or lack the resources required to secure primary healthcare.  

Clinical Analytics : Need of the hour

In the present scenario, hospitals and other healthcare organizations understand the need of real time healthcare analytics. Real time predictive analytics use medical data to help healthcare providers make informed decisions; thus reducing patient suffering and loss of resources. 

The need and benefits of Predictive software solutions for Healthcare

Ground breaking developments in technology have made collecting, handling, and applying data faster than ever before. Such recent developments of innovative healthcare software solutions offer one-stop solutions for a healthcare system to turn spreadsheets and raw information into significant data that helps the organization save valuable resources, as well as the patients’ lives.Both large and small healthcare providers utilize such software to streamline patient feedback and incident reports, disease monitoring and claims administration. Innovation in technology allows protected sharing and transfer of sensitive information to enhance and improve efficiency in systemic processes.

Some of the advantages of such healthcare software are as follows:

• Predictive analytics increase the accuracy of diagnoses and can help physicians make informed decisions for their patients leading to reduced suffering and saved resources.
• Predictive analytics help in gaining better health outcomes in turn increasing patient satisfaction.
• Healthcare predictive analytics help target patient-level interventions thus reducing patient suffering.
• Advanced healthcare predictive solutions help organizations predict HAIs and minimize harm by applying preventive measures proactively.

Incident reporting in the healthcare industry is also very critical. Technically sound solutions have made it much simpler to both record and mange incident reporting. Being able to update such information in real time helps better correspondence flow across various locations and departments in a healthcare facility. 

Importance of Patient Satisfaction in Healthcare

The need to improve quality in healthcare delivery is increasing more than ever. Hospitals, clinicians and insurance providers are determined to better define and measure quality of health care delivery. A major factor in the evaluation of quality of health care delivery is patient satisfaction. Furthermore, patient satisfaction is critical to how well patients recover; research has identified a clear link between patient outcomes and patient satisfaction scores.

The healthcare industry faces increasing pressure to provide complete information to patients to help them make informed decisions about their healthcare options. The evaluation of patient experience and satisfaction has emerged as a key area of concern as patient outcomes and patient satisfaction scores are linked to each other.

Leading healthcare organizations utilize a validated inpatient satisfaction questionnaire to estimate the heath care received by patients admitted to various hospitals. Having factored into distinct domains, this questionnaire creates a score for each to help in the analysis. It evaluates probable predictors of patient satisfaction regarding socio-demographic variables, survey logistics, and history of admissions. 

Predictive analytics in healthcare

A healthcare predictive analytics solution is a tool that can be used to predict HAIs and evaluate patient satisfaction. Such evaluations of quality of healthcare delivery through patient satisfaction surveys can be a performance indicator that gives a clear picture to service providers. Predictive analytics solutions offer a one-stop solution that could be widely used in various ways to simplify and enhance healthcare delivery.

Hospital Big Data: Helping doctors deliver better healthcare

Healthcare providers are increasingly using healthcare big data solutions to provide better care for their patients. The results are improved outcomes, saved resources and increased patient satisfaction.

Advantages of healthcare big data

Hospital-focused big data analytics provides doctors with necessary current and past information about a particular patient. The doctors can analyze the patient’s data to plan better interventions for the particular case.Such analytic platforms help predict high-risk patients and help plan more effective interventions before symptoms manifest. This results in better outcomes, increased revenues and saved lives.