Archive for the ‘Business Intelligence’ Category
Healthcare BI and Analytics including Master Data Management, Targeted Analytics, Governance, Enterprise Data Strategy, and Data Assessment
by December 20th, 2015on
TREND #10: POSITION BIG DATA TECHNOLOGIES TO ENABLE THE EVERYDAY USE OF DATA SCIENCE
The Internet of Things (IoT) has been growing significantly as consumers and businesses recognize the benefits of connecting devices to the Internet. Gartner, Inc. forecasts that there will be 25 billion “connected things” by 2020. Healthcare organizations are also beginning to understand the value of the IoT. According to a report from MarketResearch.com, the healthcare IoT market segment is poised to hit $117 billion by 2020. While the number of connected devices is impressive, the attention shouldn’t just be on the sheer number of devices but rather the data, analytics, and insights these devices generate.
The healthcare industry is seeing explosive growth in the amount of data available for analytics. One of the biggest drivers is the collection and use of data from IoT devices such as medical devices, sensors and wearables. The IoT generates a massive amount of structured and unstructured data; in fact, healthcare providers are equipped with more data than ever before. A lack of data is not the challenge; the challenge is creating meaningful information that enables healthcare providers to focus on the highest priorities and drive improvements from strategic, operational, and, most importantly, from a quality of care perspective.
There have been vast improvements in analytics tools and techniques, but a lack of system integration continues to be a challenge for healthcare organizations. Uncovering and capturing the value of IoT data and marrying it to other source systems, such as electronic medical records, financials, and claims, is critical to generating a 360-degree patient view and the key to many initiatives such as population health management. Read the rest of this post »
by December 18th, 2015on
TREND #9: DEVELOP AND IMPLEMENT A STRONG GOVERNANCE STRATEGY AND ORGANIZATION
Data governance is the overall management of enterprise data. It encompasses the people, processes, and information technology required to consistently ensure data value, quality and integrity, improvement, development, and maintenance. It also includes a single shared definition for all data, data security, and availability of the right data at the right time to the right people in the right format across the organization.1
The healthcare industry has loosely thrown around the term “data governance” for many years. However, as regulators, hospital executives, and patients have more consistently enforced, demanded, and expected better quality of care, healthcare organizations have begun investing a great amount of time and money into seeking the true value behind data governance. Healthcare organizations own significant amounts of data and data resources, but turning that data into an information asset that can be managed for effective decision making is simply not happening at an enterprise level. Managing information as an enterprise asset requires effective data governance.2 Read the rest of this post »
by December 17th, 2015on
TREND #8: INCREASE LEVEL OF UNDERSTANDING AND CONTROL OVER ACO DATA AND ANALYTICS
Accountable Care Organizations (ACO) spend a good amount of time discussing which technology systems to use, but very little time on determining who owns the data. ACOs have multiple stakeholders including partners in the ACO, patients, and insurers. Each of these stakeholders have a vested interest in the vast amounts of data within the ACO, but many times the question of who owns the data goes unanswered.
According to Definitive Healthcare, there are more than 800 ACOs representing more than 200 health plans, 3,900 providers, and 300,000 physicians. And these ACOs use more than 125 different technology vendors, making it a very complex environment.
There are many different opinions on the topic of data ownership. Some will say patients own their own data; others believe providers own the data on their systems, and insurance companies own the data on their systems. Each of these scenarios provides challenges for ACOs, making it critical for them to have a strategy in place to tackle the issues of data ownership.
In today’s healthcare landscape, all stakeholders need to be true stewards of patient data, and they should act in the best interest of the patient. If patients are expected to be more accountable for their care then they need access to their own health information. If an ACO, health plan or healthcare provider is going to be more accountable for patient outcomes, then they need to have access to the data. Read the rest of this post »
by December 16th, 2015on
TREND #7: LEVERAGE NEW TOOLS AND SKILLS TO TRANSFORM LARGE VOLUMES OF DATA INTO MEANINGFUL INFORMATION
Healthcare analytics have traditionally been reactive, and many healthcare organizations have taken the approach of “we will figure it out when the time comes.” That approach is simply not effective in today’s transforming healthcare industry. In order to survive, organizations must re-evaluate their approach to analytics and identify the skills and tools needed to improve patient outcomes and operational efficiency. These tools and skills are necessary to help sift through and make sense of all the data.
Healthcare organizations are inundated with an enormous amount of data that has the potential to not only make sense of the past but also to predict the future. The challenge is filtering through the mountains of data and turning it into useful information. While traditional analytics methods were performed by database administrators, the new era of healthcare analytics requires new competencies and a fundamental shift that focuses on preventing and targeting desired patient outcomes rather than reacting to past events. Historically, data analysts worked in isolation. However, today’s healthcare data scientists must be team-oriented and lead the collaboration of cross-functional teams.
While new skills are the foundation of transforming healthcare analytics, even the most skilled healthcare data scientists need new tools to convert data into powerful and actionable insights that impact patient outcomes and improve operational efficiency. Business intelligence and analytics tools remove data silos that inhibit strategic decision making, resulting in: Read the rest of this post »
by December 15th, 2015on
TREND #6: USE PREDICTIVE ANALYTICS TO REDUCE READMISSIONS AND IMPROVE OUTCOMES
Predictive Analytics solutions uncover insights from trends and patterns to determine the impact of operational adjustments and market forces on healthcare organizations. Statistical analysis and predictive modeling expand on the findings gained through business intelligence solutions to answer “What will happen?” given certain business and/or clinical situations. Predictive analytics combs through massive amounts of data and analyzes it to find patterns, assess probability of occurrence, and predict outcomes. Healthcare organizations are leveraging predictive analytics to:
- Reduce patient readmissions
- Increase the accuracy of patient diagnosis
- Deliver more targeted care to high-risk patients
- Provide better overall outcomes for the individuals they serve
by December 13th, 2015on
TREND #5: UTILIZE REUSABLE ACCELERATORS TO QUICKLY ACHIEVE ACTIONABLE DATA-DRIVEN INSIGHTS
One of the keys to a data-driven organization’s success is interoperability of all data systems. Data integration is not only critical for making sound business decisions but it is also the cornerstone to understanding and engaging with consumers.
For healthcare organizations, integrating data is a struggle in itself, but understanding data and turning it into useful and actionable information to improve decision-making and patient outcomes is a great challenge. In most cases, data resides in inflexible, disparate silos inside and outside of the organization, making it impossible to create a holistic and complete view of this information. Extracting data from critical sources including electronic medical records, financial systems, operational data, and external data sources including claims is imperative for population health initiatives, enhancing patient experience and reducing the cost of care.
One way to combine some of this data is through an interface engine that connects legacy systems by using a standard messaging protocol – providing the framework for the exchange, integration, retrieval and sharing of electronic health information. According to Definitive Healthcare, of the 7,257 hospitals in the United States, 4,400 are known to have some type of interface engine. A more complex but comprehensive way to combine this type of data is through the extract, transform, and load (ETL) process. Historically, ETL processes required to obtain this necessary data and place it in a data repository took 12-to-18 months to complete. However, many organizations are leveraging accelerators, reducing ETL time by more than 50%. Healthcare accelerators allow organizations to focus on delivering rapid results by fast-tracking the time-to-value. With healthcare accelerators, data is integrated much quicker so executives can spend less time collecting data and more time making data-driven decisions that positively impact business. Read the rest of this post »
by December 11th, 2015on
TREND #4: GROW ENTERPRISE INTELLIGENCE TO MEASURE AND IMPROVE PATIENT AND ORGANIZATIONAL HEALTH
Most healthcare organizations are collecting more data than they know what to do with. Unfortunately, data is not helpful unless it can be transformed into timely and actionable insights that impact performance throughout the organization. Organizations that leverage enterprise intelligence have access to the relevant clinical, financial and operational data and can transform this rich data set into actionable information that can improve decision-making and overall performance.
Enterprise intelligence creates accountability for all stakeholders because it provides transparency at an organizational level rather than at just a departmental level. According to Definitive Healthcare, many organizations are not using enterprise intelligence software. In fact, of the approximately 7,257 hospitals in the United States, only 3,450 are known to use some type of enterprise intelligence software. Read the rest of this post »
by December 10th, 2015on
TREND #3: LEVERAGE CROSS-CONTINUUM DATA ANALYSIS FOR IMPROVED PATIENT CARE AND OUTCOMES
Despite all the changes within the industry, the healthcare continuum remains relatively the same. However, our perspective across that continuum has changed considerably due in large part to the enhanced view enabled by healthcare analytics. Historically, healthcare analytics has been used to manage care within the four walls of the traditional care setting. Healthcare reform, specifically the arrival of accountable care and payment reform, has led to a greater emphasis on care across the entire continuum.
As the transformation from a volume-based to a value-based care model continues, healthcare organizations are experiencing a reduction in reimbursement. This reduction in reimbursement is putting added pressure on organizations that are trying to meet new demands. The increasing number of individuals covered by high-deductible health plans, (many of whom are delaying care as long as possible) is adding to the complexity, forcing healthcare organizations to look for alternate ways to reduce costs while still providing quality care. Read the rest of this post »
by December 8th, 2015on
INTEGRATE CLINICAL AND CLAIMS DATA TO ENABLE POPULATION HEALTH MANAGEMENT INSIGHT
Technology-enabled population health initiatives have traditionally relied solely on either clinical data or claims data. While this method has value, there is a tremendous opportunity for healthcare organizations to take population health efforts to the next level by integrating both clinical and claims data. Integrating clinical data from electronic medical records with claims data can help develop a more complete patient view and improve overall care. Merging clinical and claims data provides healthcare organizations with a more complete picture and delivers the insights needed to address their most complex challenges.When combined, clinical and claims data can be used to:
- Compare recommended care against evidence-based practices
- Improve overall care management and population health efforts
- Reduce unnecessary clinical procedures
- Help patients avoid hospitalization by identifying early risk factors
The integration of clinical and claims data empowers healthcare providers to delivery higher quality and more proactive care for their patients.
Read the rest of this post »
by December 6th, 2015on
TREND #1: ALIGN CLINICAL, QUALITY AND FINANCIAL ANALYTICS TO ENABLE VALUE-BASED CARE
Healthcare organizations today are being challenged to reduce costs, improve care coordination and outcomes, be patient-centric, and provide more with less. And they are doing all of this while trying to adhere to regulatory requirements and untangle the entrenched web of inefficiencies that negatively impacts both progress and, ultimately, clinical outcomes.
One change that is directly impacting healthcare providers is the transition from a volume-based (fee-for-service) to a value-based care delivery model. Value-based care requires reporting hundreds of process and performance measures to various quality and regulatory programs, like the Medicare Shared Savings and Pioneer ACO programs, in order to be reimbursed. The Patient Protection and Affordable Care Act (ACA) changes provider reimbursements by focusing on the Triple Aim: improving the health of the population, enhancing the experience and outcomes of patients, and reducing the cost of care. Medicare reimbursements are based on value, not volume. This means hospitals and physicians will see their payments modified so that those who provide higher-quality care will receive higher payments than those who provide lower-quality care. For example, looking at just the value-based purchasing adjustment, which incorporates a number of quality measures, Definitive Healthcare estimates in 2015 there were 100 hospitals that received more than $250,000 in additional revenue, and nearly 130 hospitals that were penalized more than $250,000.
To avoid these penalties, it is critical for healthcare organizations to unite quality indicators and measures with clinical analytics that drive operational performance. Care delivery teams within healthcare organizations are dependent on high-quality data in order to make timely decisions that positively impact the outcomes of the individuals, organizations, and communities they serve. By truly harmonizing clinical analytics and quality indicators, healthcare organizations can move from reactive reporting to proactive and actionable insights that uncover process improvement opportunities in real-time.
by December 4th, 2015on
It is hard to believe we are in December already! Where has 2015 gone? We are still entrenched in the transformation of healthcare and it will be interesting to witness the continued evolution in 2016.
One area that will continue to be a focal point for healthcare organizations is analytics and the ability to transform large amounts of data into meaningful information that can be utilized to improve patient care and operational performance. Check out the below infographic for a look at 10 healthcare analytics trends for 2016. In our new guide, we take a deep dive into these trends and also provide some real-world client stories. Get the guide here or at the bottom of this post.