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.

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. 

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.

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.

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.

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.

Predictive Analytics– A Revolutionary Approach to Systematic Healthcare

One of the much talked-about topics in the realm of healthcare analytics is predictive analytics. It is an advanced approach to predicting clinical occurrences before they manifest. By using techniques such as statistics, data mining, machine learning and artificial intelligence,predictive analytics solutions analyze current and historic data to make predictions. Such predictions help the healthcare industry to improve patient care, community health as well as manage chronic diseases.

Working With Predictive Analytics Software

To ensure better healthcare, numerous predictive analytics software are available in the market. They are well equipped and user-friendly which help accelerate the performance of healthcare industry thereby benefiting both the patients as well as physicians. Such analytics also change the patient’s role and help them become more informed consumers who work with their physicians collaboratively to achieve better outcomes. 

Predictive Modeling Healthcare Benefits

Predictive analytics finds prominence in almost every branch of healthcare. Adopting a reliable predictive analytics solution helps in the following ways –

• Increases accuracy of the diagnoses resulting in decreased suffering, saved resources and lives
• Helps providers make better decisions about treatments as in when and where resources need to be directed
• Provides physicians with better direction to treat patients on their individual needs
• Helps monitor and maintain community health with preventive medicine
• Supports hospitals as well as employers with better prediction of insurance costs 

Reducing Hospital Readmissions with Infection Control Measures

The hospital readmission refers to an occurrence when a patient is discharged from a hospital and his readmission takes place within a specific time – 30-day readmissions. The rate of readmissions affects the quality benchmark for the healthcare system and depends on various factors such as diagnoses, severity of illness, and the availability and quality of post-discharge care.

Increased readmissions could result inthe following situations:

• Unnecessary treatment expenditures
• Improper reimbursement for services
• Compromised professionally recognized standards of care.

A hospital's readmission rate is calculated to adjust the associated risks.A measure of a hospital’s readmission performance compared to the national average for the hospital’s set of patients with a similar medical conditions is the hospital’s excess readmission ratio.

Hospitals have been engaging a number of strategies to reduce preventable readmissions. These include providing improved care during the inpatient stay which leads to reduced risk of hospital acquired infections,more careful administration of patient medications and discharge planning with improved communication about follow-up care.One of the most effective ways to reduce readmissions is to deploy infection control measures in healthcare facilities.

By industrializing infection control workflows and implementing real-time patient monitoring, hospitals would be able to better identify high-risk patients and enable clinicians to proactively take appropriate action in real-time to reduce hospital-acquired infection so rout breaks on a population level.

Hospitals should implement strategies that can go across the continuum of care for effective reduction of readmission rates. Data connectivity and information sharing crucial for inter operability of patient data, will improve care coordination between healthcare personnel and disparate health information systems. 

Using Jvion’s RevEgis, providers can pin point high-risk patients and proactively intervene to provide appropriate care when needed. This helps healthcare facilities reduce readmissions, reduce length of stay related complications, and stop the loss of vital hospital resources while improving quality of care and in turn improved patient satisfaction.

An overview of Hospital Acquired Condition Prevention

Each hospital must have infection control measures, and policies and the staff should take every possible precaution to avoid the infection disease. Though the risk of infection will never eliminate completely and some peoples have a high risk of acquiring an infection than others. HAC is an abbreviation for Hospital Acquired Condition, is an adverse condition that affects a patient and that arise during a stay in a hospital.

Hospitals are starting using the digital market which is driving a good cycle where connected devices and cloud-powered services are generating data and Hospital Big Data is famous for using this feature. It may use in various fields which can save your time and create cloud powered innovation.

What is Hospital Acquired Infection Prevention?

Infection is a common disease caused by some microorganism like a virus, bacteria or parasites, and fungal pathogens, mostly these organisms includes germs & bugs. Bacteria and virus are the most common cause of HAI.  Nosocomial infection is the other name for HAI. It usually occurs within 2 to 3 days after admission to hospital and happens at a cost to the group of people and the patient because they cause: illness to the patient, a longer stay in the hospital, and a longer recovery time.

This infection can be treated with antibiotics and respond well. Irregularly, this can be severe and life threatening. Various bacteria are very hard to treat because they are resistant to standard antibiotics, and these bacteria called super- bugs. Some of these bacteria are- Staphylococcus aureus often called golden staph or (MRSA), Vancomycin-resistant Enterococcus (VRE), carbapenem-resistant Enterobacteriaceae (CRE).

The most common types are:

•    Bloodstream infection (BSI)

•    Pneumonia- ventilator-associated pneumonia (VAP)

•    Urinary tract infection (UTI),

•    Surgical site infection (SSI)

•    Wound infection

Steps that should be taken for Hospital Acquired Infection Reduction is:

    Improve awareness of medical staff including administration and other hospital personnel about nosocomial infections and antimicrobial resistance.

    Observe trends: Frequency and distribution of nosocomial infections and when possible, risk-adjusted incidence for Intra & interhospital comparisons.

    Identify the requirement for new or intensify prevention programs and calculate the impact of prevention measures.

    Strict hospital infection control procedures and policies

    Proper and frequent hygiene standards by all hospital staff and patients

    Cautious use of antibiotic medication.

    Recognize possible areas needs for upgrading in patient care and additional epidemiological studies such as; risk factor analysis.

    Enhancement in health care with increased quality and safety.

    Need for active surveillance to supervise changing infectious risks and also identify requirements for changes in control measures.

Apart from these strategies patients and their family are encouraged to become energetic participants in various Hospital Acquired Condition Prevention initiatives. This infection is very dangerous for the people more than 70 years; they can start with small steps in preventing infections:

• Wash your hands regularly.
• Insist that your health care provider wash his/her hands.
• Make inquiries about the cleanliness of equipment and the use of sterilized bundles.

Why Every Clinical Organization Needs Big Data Healthcare?

In the present scenario, the healthcare industry has understood the importance of Big Data Healthcare. Due to the era of open information in healthcare now in full stream, the government and different stakeholders are quickly moving toward transparency by making many years of data searchable, actionable and usable by the healthcare industry. This exceptional increment in electronic wellbeing records has driven it in healthcare, permitting doctors an open door to create better clinical decisions at much bigger scale.

With the assistance of this data, pharmaceutical organizations, and suppliers can create proactive procedures to succeed in the new healthcare environment.

Big data in Clinical Analytics

Big data holds a unique role in prevention and prediction. It is useful to effectivelyfigure out who wants care and when. The present healthcare framework is endeavoring to transform into a more remunerating set up for quality care where providers, patients, and community stand to lead. It is easier to make this change with big data in clinical analytics.

One of the greatest advantages of big data in healthcare is that it targets care by giving a comprehension of what works. Using this data can maintain a strategic distance from undesirable occasions, for example, hospital fraud and waste, hospital acquired conditions, furthermore decrease many excessive readmissions. Additionally, it opens the entryways for better treatment and research.

Create Smooth Transition to the New Healthcare Landscape

The leading healthcare solutions offer various solutions for foreseeing patient-level disease, drive forecast contamination control, anticipate populace wellbeing, foresee readmissions and money related misfortunes and enhance the move to ICD-10, etc. Moreover, it plays a critical part in driving the concept of proof based pharmaceutical. The prominent organizations incorporate predictive analysis taking into account a patient-phenotype healthcare big data stage, utilize it to help suppliers keep away from senseless patient suffering and avert the loss of assets.

Enhance the Quality of Patient Care

With the outlook change in patient consideration, big data in healthcare is turning into a primary focus as an organization can no more bear to work with high levels of waste and poor health outcomes. The coordination and investigation of information can help medicinal organizations move from a poor to a robust fiscal balance sheet. In particular, it can enhance the nature of health and Continuum Care of their patients.

Because of the new value-based buying pressures that need financial and clinical data, healthcare centers are mandatory to procure more data. It can scale and streamline the procedure. These arrangements intend to coordinate different data from various sources like clinical, billing, patient satisfaction and much more. One can interpret and analyze it through reports and visualizations that result in better insights into quality control and Cost Reduction Strategies.

Summary:

Using big data healthcare technology is undoubtedly one of the most effectual ways to inflate the success of healthcare acquired infection prevention. Since the technology continues to grow, there will be more proclivities towards prescriptive and predictive practices.

Benefits of Clinical Analytics in Healthcare

The clinical analytics has become a key factor for the healthcare industry today.

Healthcare analytics not only help the healthcare organizations from the operational front, but also on the strategic front. Such analytics also makes a hospital better equipped to improve allocation of the staff where they are needed the most and also the effective use of available resources.  Healthcare facilities can also depend on such analytics to measure effectiveness of the clinical treatments provided to the patients within the facility. Patient specific data collected could help the organization offer customized and streamlined care plans. Such analysis can help providers deliver better care services leading to improved outcomes and significantly reduced readmission rates.

Healthcare organizations are facing great pressures to reduce costs, offer better care and to be more patient centric. As healthcare systems continue to gather large data sets, including claims data, the value of clinical analytics increases.

Clinical Analytics empowers clinicians and researchers to build cohorts, assess patient-specific outcomes, and make informed clinical predictions. Such solutions also help healthcare organizations follow populations of patients and ultimately improve community health.

Benefits of Evidence-based Metrics in Healthcare

The healthcare industry standardsare changing rapidly. Healthcare systems are struggling with rising costs and compromised quality of care despite of the workflows, well-trained clinicians and practices in place.

Healthcare facilities have a range of policies and practices in place to attack fraud and abuse, reduce medical errors, etc.

But when it comes to attaining the maximum benefits in terms of improving care quality and patient satisfaction, reducing costs and managing risks with efficiency, switching to evidence-based healthcare solutions is critical.

Among the many benefits achieved by adapting evidence-based healthcare solutions, feware as follows:

These solutions help healthcare facilities effectively reduce unnecessary healthcare costs by taking into consideration financial gains and risks. They can also help reduce the expenses of the care rendered to the patients by allotting correct resources when and where they are needed the most. With evidence-based practices the chances of readmissions, extended LoS, or emergency room visits can be reduced significantly. Such evidence-based solutions can not only help predict patients with high risk of infections, but also impending or existing health risks in a community.

Benefits of Big Data Analytics in Healthcare

Big data analytics in healthcare is rapidly evolving helping provide insights into very large data sets and improving outcomes while reducing costs. Not only does such analytics help predict diseases, but also improves care provided resulting in reduced suffering and saved lives.

One of the significant applications of big data analytics or predictive analytics is to prevent healthcare fraud, waste and abuse. Such analytics help identify, predict, and minimize fraud by implementing advanced analytic systems for fraud detection.Analyzing large numbers of claim requests rapidly is a crucial step to reduce fraud, waste and abuse.

Another crucial benefit of big data analytics is being able to identify and pin point high-risk patients. This helps ensure that the most effective intervention is applied to the specific patient – and that it’s provided at the appropriate time. Big data analytics also helps analyze disease patterns and record disease outbreaks in the populations. Such data can help deal with large populations where it becomes important to know who can potentially benefit from interventions as a way to improve community health and lower costs while saving lives.

Big Data in Healthcare – Reducing Health Care Costs

Health care providers face many obstacles while analyzing health care data and realizing the costs involved. Securing, sharing, and organizing sensitive data with the help of BI tools and data warehousing is key.

Health care data is diverse, disparate and distributed into hard-to-penetrate silos owned by a multitude of stakeholders. Each stakeholder has different interests and business incentives while still being closely intertwined.

Healthcare analytics connects the technologies to help deliver insights into the complex data and clinical mutuality that drive medical outcomes, costs, and oversight. These follow the disease models, which help the bridge the gap between researchers and clinicians resulting in better outcomes, saved lives and resources.

Fully integrated data is needed to harness ability to identify the area of waste and opportunities of healthcare cost reduction. Healthcare data warehousing is the best method to organize a health system’s financial, clinical, administrative and patient satisfaction information. Once the aggregation of all such data occurs in one place, it helps categorize and identify the sources of waste reducing the involved costs with improved outcomes. 

The Increased Dependence Of Big Data In Hospitals

The hospital big data contains billions of data points collected from different sources within the healthcare facility. These data files are too large, complex to capture, store and manage for any data tool. Big data applications are rapidly transforming the healthcare industry by quickly transforming loads of such disparate and unstructured data into instantly available and highly actionable information.This data improves population health management, care delivery, performance reporting, and resource utilization.

Big data software applications represent the single most effective solution to addressing the multitude of problems faced by healthcare facilities, including the reduction of avoidable hospital readmissions.

A ‘hospital readmission’ is admitting a patient to the hospital within 30 days of discharge from a prior hospitalization. With the help of big data, hospitals can track the patients who are at a high risk of readmissions. There are various reasons of unplanned readmission such as surgical wound infections.

Big data applications aid evidence-based medicine, which involve making use of all clinical data available to improve patient outcomes. This includes improved ability to detect and diagnose diseases in their early stages, before clinical signs are present. This patient level care helps save lives and reduce suffering along with waste of resources.

Healthcare organizations also need to be able to detect fraud based on analysis of irregularities in billing data or patient records. Big data applications can analyze patient records and billing to detect over utilization of resources and services in a hospital.

How Is Big Data Instrumental In Improving Healthcare?

Over the past one-decade or so, hospitals and healthcare centers have witnessed a huge amount of data generation. Big data is the intersection of these changes. In healthcare, it provides profits and reduction on waste overheads. It is benefiting the healthcare industry in multiple ways.

Barriers for using big data

The big data phenomenon has great value and is yet to face several challenges. Specifically, it has two major drawbacks in the healthcare field:

• Requirement of technical expertise
• Lack of robustness

Security of patient data

Security of every patient record or data is primary. Big data is not able to manage an integrated amount of data. For this reason, hospitals require data scientists to take some steps to confirm better security of data. Big data acts on an open source technology with non-compatible security technology. To avoid problems, organizations should select original big data vendors to implement.

Structure of healthcare and predictive analytics

Predictive analytics is one of the most discussed topics in healthcare today.Meaningful analysis of the data in healthcare can improve patient care and chronic disease management based on predictions. However, making predictions would be a waste of time if they do not get transformed into consequential actions.

Enterprise data warehouse is essential for the management of patient records and data. With EDW, systematic integration of data is made easy using the analytics approach.This step helps prioritize resources within the health system focusing on cost reduction and revenue enhancement while improving patient outcomes.

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.

Healthcare Predictive Analytics

Healthcare predictive analysis is a technique used to obtain and combine current and historical healthcare data to make predictions. This involves predictive modeling, data mining, and machine learning to effectively and accurately predict health events for a patient or population before symptoms manifest. The analysis of current and historical clinical information for a particular patient helps healthcare providers better manage and direct resources and treatments that prevent illness.

Various predictive analytics applications exist that offer easy to use interfaces and improved outcomes. These predictive analytics software applications are designed for healthcare entities to help optimize healthcare delivery in turn saving valuable resources and lives.