Thursday, November 10, 2016

Predictive Analytics Software for CJR Readmissions

Today physicians are well skilled and make efforts to stay current with the latest studies and practices. However, it is not expected for them to memorize each individual patient’s records. But in today’s technology era, this can be made possible with clinical predictive analytics. Healthcare providers have the predictions at their fingertips, which help them make appropriate decisions and deliver better care.

The comprehensive care for joint replacement model helps support effective and efficient care for patients who undergo the most frequent inpatient surgeries such as hip and knee replacements. This model examines bundled payments and aspect measurements for an episode of care related with hip and knee replacements to motivate hospitals, physicians, and post-acute care providers to work in sync to improve the quality of treatment.

A hospital readmission is an episode when a patient who been discharged from the hospital gets admitted again in a specified time interval (e.g. 30-day readmissions). CJR readmissions refer to the patients who have undergone the CJR surgeries and get readmitted to the hospital within a specific time interval.

Jvion’s artificial intelligence predictive analytic solution uses deep machine learning and clinical data to deliver the most advanced and accurate CJR readmissions predictions. RevEgis helps providers improve care quality, drive down cost, and meet the demands of value-based models of care.

Wednesday, October 5, 2016

Predictive Analytics and Its Major Role in Healthcare

Clinical Predictions cannot be beneficial until they are transformed into actions. This applies to predictive analytics solutions that provide early notifications to doctors and medical staff about a specific patient. Healthcare facilities do not have to wait for symptoms to manifest before the treatment begins. With the analysis of current and historical patient data from hospitals, high-risk patients can be effectively pinpointed and resources can be diverted where they are needed the most.

Clinical Predictive Analytics
Clinical predictive analytics is an effective way to reduce readmission rates in hospital settings. Healthcare analytics software not only reduces readmissions, but also provides patient-level predictions to determine interventions that prevent specific diseases and infections. 

Few ways in which clinical analytics help: 
  • Considers different components such as patient phenotype, patient specific sensitivities and case history
  • Predicts the possible state of a patient with reference to current and historical information before symptoms are evident
  • Optimizes the allocation and utilization of resources to boost healthcare cost reduction techniques
  • Predicts hospital acquired conditions, one of the primary reasons of loss of resources and increased LoSto adequately contain and handle any such cases before they happen 
  • Helps examine live information and numbers assisting hospital staff proficiently function towards reducing readmissions, reducing suffering while being patient-centric.

Importance of big data healthcare
The healthcare sector has realized the importance of big data. In this era of open information in hospitals, stakeholders and the federal government are quickly moving toward transparency through creating the decades of disparate data more searchable, operational and actionable for the healthcare industry. This data helps pharmaceutical companies, payors and providers to develop proactive plans to thrive in the new healthcare environment. The remarkable increase in electronic health records enable doctors to take better clinical decisions and provide improved care yielding better outcomes.  

The leading clinical predictive solution like Jvion’s RevEgis for providers, utilizes big data and helps predict patient level diseases, hospital acquired conditions, improve community health, drive predictive infection control, reduce readmissions and more. 

Sunday, October 2, 2016

An Insight into Infection Control and Prevention

Infection control is a measure to reduce and prevent nosocomial or healthcare facility related infections. Infection control solutions address aspects related to the spread of infection within the healthcare setting and investigation with monitoring of suspected or confirmed spread of infection within a specific health-care facility. Healthcare providers engage a substantial amount of resources handling nosocomial infections resulting in wasted resources and increased suffering.

In addition to measures such as hand hygiene, sterilization and disinfection, an evidence-based approach to infection control and prevention is also effective to reduce infection rates. With the help of predictive analytics, it is easier to improve the quality of patient centered medicine.  

Use of clinical analytics for patient stratification is the primary step in the risk management measures adopted by stakeholders like ACOs. Its major objective is to pinpoint high-risk patients so that medical staff can plan proactively to ensure that the chances of patients being exposed to infections are minimized.

All these measures help reduce and control hospital acquired infections which in turn improve patient satisfaction, reduce suffering, save resources and improve community health.

Wednesday, September 28, 2016

Pressure Ulcer Prevention in Hospitals - An Overview

Pressure Ulcer Definition
A pressure ulcer is a kind of skin wound created by friction, shear or pressure. They could occur when pressure reduces or cuts the blood supply to a specific part of the body for a long period of time. Prevention is definitely an ideal form of protection from pressure ulcers.

Pressure Ulcer Prevention In Hospitals
The pressure ulcer prevention can be nursing intensive. After the development of pressure ulcer,the goal for the healthcare staff is to help the healthcare unit in closing the ulcer as early as possible. It includes preventing further ulcer deterioration, keep the area clean and minimize possible infections from developing while keeping the patient pain free. Several aspects of managing pressure ulcers are parallel to prevention (nutrition, support surfaces and mechanical loading). Some major guidelines include recommendations on policies such as utilization of pressure redistributing support surfaces, repositioning, nutritional support, wound care and biophysical agents.

Predictive Analytics To The Rescue
Another way to prevent pressure ulcers is by ensuring they don’t occur in the first place. Jvion’s RevEgis, built on advanced artificial intelligence using the Clinical Patient Pod technology delivers predictions using the data for patients that a provider already has on hand. The solution looks at a patient data and predicts any possibility of pressure ulcers before any clinical signs are present. This helps a provider target preventions that are low cost, non-invasive, and easy to apply where they are needed the most. As a result, providers are able to reduce readmissions, length-of-stay, save resources and most of all, reduce patient suffering caused by pressure ulcers.

Predictive Analytics – A Proven Approach for Readmission Reduction

Readmissions to hospitals have become a major concern for hospitals and post acute care providers as it may lead to increased penalties. Most of the hospitals are striving hard to find suitable measures for reducing the rates of readmissions.

One of the most preferred and effective ways to readmission reduction is predictive analytics. It helps the hospitals quickly pinpoint patients who are at greater risk of readmission.

How predictive modelling works?
Readmission prediction is possible with the help of predictive analytics. Being a distinctive statistical technique, predictive analytics uses data mining and modeling to identify patterns and trends. With the help of complex calculations, patients who are at an increased risk of readmission can be shortlisted. Both, inpatient and outpatient environments could be considered to ensure more accuracy in predictions and in turn yield better outcomes.


On the basis of calculated risk scores, post acute care providers are able to proactively take measures towards readmission reduction. With daily reports obtained from predictive analytics, allotting resources when and where they are needed the most to focus on high-risk patients becomes very easy.Such an approach also helps reduce readmissions, save resources, and improve patient satisfaction while reducing suffering.

Wednesday, September 21, 2016

Hospital Acquired Condition Reduction Strategies with Big Data Analytics

Hospital acquired conditions or HACs emerge as a great challenge for healthcare systems and clinicians as it is one of the major causes for mortality and penalties since such situations occur during a patient’s stay in the hospital. The main causes behind the occurrence of such infections may include – poor maintenance of healthcare facilities or inappropriate treatment procedures adopted by the staff.

Being a prominent safety concern for patients and healthcare providers, such conditions create a distinctive urge for the deployment of hospital acquired infection prevention measures. 

Who is at High Risk?
Prior to making any significant changes toward hospital acquired infection prevention measures, information about high-risk individuals needs to be reviewed. The major important factors behind the development of such infections include –
  • Patients within the age-group of 65 and above
  • Patients who consume antibiotics 
  • Shock treatment or any major trauma
  • Acute renal failures may also result in such conditions

Hospital Acquired Infection Reduction Program
The increasing rate of mortality, morbidity and lengthy stay of patients has resulted in the introduction of hospital acquired infection reduction program by CMS (Center for Medicare and Medicaid Services). According to this program, CMS is liable to penalize the healthcare organizations and hospitals that have higher patient complication rates in comparison to their peers. The qualifying hospitals will require paying one percent of every Medicare payment they receive.

Hospital Big Data – A Preventive Measure
As per the study of CDC (Centers for Disease Control and Prevention), the hospital acquired conditions are directly becoming a major cause for more than 15000 deaths every year. However, providers are striving hard to seek better methods for managing such conditions by gathering reliable data concerning these problems.Of course, hospital big data can greatly help in the prevention of such infections. In fact, some of the suggested measures for its control according to CDC include –
  • Ensuring appropriate hand hygiene
  • Making use of equipment for personal protection (PPE)
  • Establish proper cough etiquette and respiratory hygiene
  • Making sure complete injection site safety 
  • Effective handling and storing of medication
  • Disinfecting and cleaning the surgery instruments as well as devices
  • Improvising the techniques of sterilization.

Reducing the Penalties
Having the right clinical analytical software such as Jvion’s RevEgis can help improve the HAC scores of a hospital. Here are some of the important tips for healthcare organizations to reduce penalties under the HAI reduction program of CMC –
  • Remain proactive in evaluating, measuring and optimizing the processes as well as outcomes of critical care. 
  • Putting in place the right processes of coding in order to capture accurate present on admission (POA) data of patient. 
  • Implement active systems of surveillance for identification of HACs and any potential harm to patients. 

Summary: You can ensure absolute prevention from hospital acquired condition with the help of big data analytics. It combines and analyzes the patient history to predict the probability for the occurrence of such infections.

Monday, September 12, 2016

Patient Predictive Data Warehouse – A Must-Have for Effective Functioning of Healthcare Systems

The healthcare industry is showing a tremendous shift towards the adoption of predictive analytics to serve an array of purposes. Experts view it as a major pre-requisite for the management of community health. Most of the hospitals employ such statistical tools to pinpoint the patients at a high-risk of readmissions. Whereas, some use it as a patient predictive data warehouse to identify as well as intervene the patients with severe and chronic illnesses.

Although, the process of using predictive analytics software may not be new for the healthcare industry, it may come with several challenges specially with gathering data. This calls fora requirement for well-structured patient predictive data warehouse. Hospitals and healthcare organizations can reap the following key benefits –

Ensure Patient Stratification
Patient stratification is a distinctive measure that integrates the chronic conditions, cost trends as well as social determinants of risk, together to identify the individuals who can benefit from the proactive programs of care management. It can further expand the capability to identify the candidates who are critical for the programs of care management.

Helps In Predicting Hospital Acquired Infection
Almost every healthcare system is facing the similar dual challenges of wringing out their expenses and matching up with the regulatory challenges imposed by the government over HAI (Hospital Acquired Infection). Previously, the implementation of traditional EDW (Enterprise Data Warehouse) by medical centers resulted in poor outcomes. However, with patient predictive data warehouse, EHR software gets more agile resulting in better outcomes.

This data can help you predict hospital acquired infections with accuracy thereby avoiding any kind of penalties. It is undoubtedly a cost-effective measure that is beneficial for both the patients as well as healthcare organizations.

Promotes Community Health Solution
When it comes to addressing the major challenges of community health solution, big data warehouse plays a vital role in predicting patients who are at a highest risk. It is a faster, smarter as well as more actionable approach to ensure community health welfare.This can further provide granular insights driving improved outcomes for both the hospital as well as patients.

Such predictions can also turn out to be valuable in the process of policy making, spreading education, interventions as well as care coordination for community health.


Summary: Having a patient predictive data warehouse can help the healthcare organizations store the past and current clinical information for every patient. It ensures better interpretations and predictions of hospital-acquired infections, community health and patient stratification.