The global healthcare analytics market is set to balloon to a value of $129.7 billion by 2028. It doesn’t take much digging to figure out why. Data analytics tools used in healthcare are invaluable assets for providers looking for ways to improve the patient experience, as well as their own bottom lines. These tools allow them to perform a wide variety of tasks — including risk assessment, debt reduction, and revenue optimization — in more streamlined, efficient, and accurate ways.
However, this growth has also come with a spike in analytics challenges. Many providers are overwhelmed by trying to understand and utilize all of the information that analytics tools deliver. Without a clear way to analyze the wealth of data collected, healthcare analytics can often seem like a job that providers don’t have the knowledge or bandwidth to take on. With the right revenue cycle management solution, healthcare practices can collect, digest, and react to data for the betterment of business.
Best Data Practices for Utilizing Healthcare Analytics
If you’re a provider who wants to make the most of the data you’re collecting and the analytics tools you’re using, here are four strategies that can help you better understand and translate information into consumable, actionable insights:
1. Configure data based on your priorities.
Part of the value of data analytics tools in healthcare is that they can be used to pinpoint information that’s important to your organization. To accomplish this, however, you need to figure out what key performance indicators are most valuable to you in advance and focus on measuring those specific numbers. Otherwise, you’ll end up spending too much time sifting through data in search of a needle in a haystack.
Let’s say that your main priority is reducing bad debt. You would need to make sure that you (or your outside partners) were using analytics tools to build reports around this data specifically. The relevant data should be front and center in your dashboard and easily exported so it can be shared with stakeholders.
By Terri Casterton, director of product and strategy and healthcare, Bottle Rocket.
If digital expansion in healthcare was simmering before the pandemic, COVID-19 has set the need for transformation alight. Engagement tactics that were already in use before 2020, like telehealth, remote patient monitoring, and care at home, flew to the forefront of the agenda as providers were forced to close their doors.
COVID-19 revealed a complicated flaw in the industry: a maze of digital obstacles preventing a seamless online patient experience. Post-pandemic, patient habits still lean towards a preference for digital access: in July 2021, a McKinsey report revealed that telehealth utilization in the US has stabilized at levels 38 times higher than before COVID-19.
As everyday healthcare decisions fall upon the shoulders of increasingly digitally-adept populations, providers need to ensure a simple user experience -moving away from patient portals to more robust engagement platforms. At the forefront of every leader’s mind should be how to provide a frictionless engagement path for patients, and what digital tools are necessary to guarantee the seamless delivery of this experience.
Electronic health record (EHR) patient portals have long been an entry-point for basic transactions like viewing test results and refilling medications, but this is no longer enough. Innovators in the healthcare space are recognizing the need to move to more dynamic systems. Luckily, EHR vendors have, of late, been more willing to partner with third-party app and cloud-based developers, building integrated solutions to provide a more cohesive healthcare experience.
It’s clear that unleashing a patient-focused strategy is the way forward for healthcare providers in the wake of the pandemic. But what are the key benefits that will differentiate your company from the competition?
Get hyper-personal in the experience you deliver
EHRs can serve an enterprise, from labs to ICUs, across multiple facilities and geographies. In a world of shrinking healthcare margins, this scale can drive much-needed standardization and efficiencies. But the needs and circumstances of a real population are never contained or linear, effectively diminishing the potential of a one-size-fits-all approach.
Value continues to shift from fee-for-service to fee-for-outcome, and because of this a hyper-focus on specific segments will uncover new opportunities to drive engagement (and corresponding outcomes) on a highly specialized care journey.
By Andrea Sorensen, associate vice president of product consulting, MedeAnalytics.
Andrea Sorensen
Healthcare providers on the front lines of the coronavirus pandemic continue to be overwhelmed by the increase of cases worldwide. Physicians, nurses and other direct providers are overworked, tired and mentally exhausted from non-stop diagnosis and treatment during the pandemic. And just as the number of new cases seems to decrease, they rise again.
In the US alone there are more than 10.3 million cases and 241,000 deaths. Worldwide, cases number more than 51.8 million with more than 1.2 million deaths. These numbers, undoubtedly, will continue to grow in the coming months. “By June 2020, the COVID-19 pandemic had caused hundreds of thousands of deaths around the world, triggered the largest quarterly contraction of global GDP ever recorded, and left hundreds of millions of people without jobs,” according to research published by the McKinsey Global Institute.
Physicians, nurses and other healthcare providers are not immune from the coronavirus. From its deadly effect or the mental health impact of dealing with the pandemic each day. To date, more than 922 healthcare works in the US likely have died following contact with patients. “America’s health care workers are dying. In some states, medical personnel account for as many as 20% of known coronavirus cases. They tend to patients in hospitals, treating them, serving them food and cleaning their rooms,” according to KHN and The Guardian.
Overall healthcare providers, like those of us in society in general, are extremely stressed by the coronavirus pandemic. A study published in Psychiatry Research found “(o)f all 442 participants, 286 (64.7%) had symptoms of depression, 224 (51.6%) anxiety, and 182 (41.2%) stress. Being female, young, and single, having less work experience, working in frontline were associated with higher scores, whereas having a child was associated with lower scores in each subscale.”
But statistics aren’t necessary to understand that healthcare providers will continue to face substantial anxiety and rising tension for the foreseeable future. “Health-care providers were challenged by working in a totally new context, exhaustion due to heavy workloads and protective gear, the fear of becoming infected and infecting others, feeling powerless to handle patients’ conditions, and managing relationships in this stressful situation,” The Lancet reports.
By Heather Annolino, senior director healthcare practice, Ventiv.
In recent weeks the U.S. has experienced a significant increase in new COVID-19 cases. For healthcare facilities in these regions, this is a constant reminder that things are “not business as normal” and has resulted in administrators needing to continually monitor the changing COVID-19 response landscape to reduce risks which could affect the quality of patient care.
With this in mind, patient safety is more important than ever. The ability for healthcare organizations to implement predictive analytics and data-discovery tools that identify hidden patterns and trends is essential. This allows them to focus on interventions and changes in processes, detect vulnerabilities, and increase preparedness before, during and after an incident to further decrease patient harm.
Moving forward, healthcare organizations must embrace a heightened level of risk management to provide an environment free from harm. These new risks, along with gaps in longstanding processes, require better risk management and patient safety systems with the ability to capture, track, and analyze data in real-time to enhance processes that will mitigate future risks.
Working as a centralized reporting tool, these systems can also remove any biases to assist with making enhanced decisions to continually drive operational efficiencies.
Here are three system requirements for an effective, integrated patient safety tool needed for healthcare leaders to elevate care, enhance quality and reduce risk throughout different phases of the pandemic.
According to a new survey fielded by Definitive Healthcare and sponsored by Dimensional Insight, 90% of hospitals and health systems use the analytics component of their electronic health records (EHRs), with 49% using it exclusively or primarily for analytics. With such widespread use, the technology must be meeting the needs of hospitals and health systems, right?
(Wrong.)
The survey data shows that despite the fact that many hospitals are using EHR analytics, they are also challenged by the technology and give it middling rates when it comes to satisfaction. Let’s look at the survey results in more detail and examine where hospitals and health systems go from here.
Hospitals not highly satisfied with EHR analytics
The survey interviewed 108 healthcare leaders on their experience with EHR analytics. It also asked about their experience with analytics-specific platforms and in-house solutions to serve as a comparison point.
Overall, leaders ranked their satisfaction with EHR analytics as a 5.58 (on a scale of 0-10 with 0 being “extremely dissatisfied” and 10 being “extremely satisfied”). In-house solutions received a satisfaction score of 6.51 (17% higher) and analytics-specific platforms received a score of 6.69 (20% higher).
Leaders feel challenged by technology aspects of EHR analytics. For organizations that are using EHR analytics as their primary analytics tool, they feel challenged by:
The reporting and querying is difficult/slow (43.4%)
The component is not robust or advanced enough (35.8%)
Interoperability (30.2%)
Lack of visualization (28.3%)
User interface is difficult to understand/use (26.4%)
Those that are not using EHR analytics cite similar technology challenges as the reason they are not using the component.
Ever increasing computational power, advances in artificial intelligence, and the lower of the cost computation (because of cloud computing service, such as Azure and Amazon Web Services) has enabled healthcare systems and healthcare logistics companies – often laggards in quality improvement and technology adoption – to rapidly implement analytics systems. Such systems enable enterprises to analyze and model their processes, engage in meaningful quality and process improvement activities, and prepare to succeed in value and risk-based payment models.
Hewlett Packard Enterprises recently published a piece that delineated some of the benefits that enterprises can gain from analytics (specifically the predictive form):
Reducing re-admissions
Gauging operating room (OR) demand
Better manage supply chains
Staffing optimization
Intervene with care pathways prior to adverse events occurring.
Enterprises with existing, legacy analytics systems – for example those that mainly work with claims-based data or lack predicative or real-time capabilities can likewise obtain the above efficiencies. A modern data warehouse must be flexible, SQL-enabled, cloud-based, and highly secure. Snowflake Computing’s cloud-based infrastructure is an example of one such system which can be easily scaled as it is offered to clients with usage-based pricing. A data warehouse alone, however, is not sufficient for an enterprise. Tools must be provided to prep, transform, and perform analysis on the data. Alteryx Designer, one such tool, allows analysts to prep and blend data from heterogenous sources – e.g., CSVs, databases, Excel files — in an efficient and reproducible manner, and, more importantly, it includes spatial and predictive analytics. This enables organizations to move from retrospective and barely actionable data to immediately actionable real-time predictive analytics.
The implementation of an analytics systems (or the migration of a legacy system) is not a project to be undertaken without serious thought how change is managed within an organization. Many facets of an organization will be impacted by such projects. Matthew Morris, lead data enabler at an international wholesaling club based in Washington state, who has overseen both the maintenance of legacy analytics system and the migration to a modern one using a team from Decisive Data and Alteryx’s tools, noted some key behaviors or strategies that should be taken to ensure a successful project:
Get leadership buy-in – Many people naturally resist change. Ensuring that leadership across the enterprise is committed to the change will enable a coherent messaging to be addressed to all stakeholders. There are many strategies to achieve leadership buy-in. A notable one, the ADKAR model is described here.
Choosing an effective partner – Especially for mission-critical or strategically sensitive projects, external help is critical. Talented consultants can augment staff skill shortages and bring critical experience (and lessons learned from other projects).
Be there and integrate training creatively – Project leaders should spend time onsite at the various locations where work occurs to ensure proper training and data conversion. During training, don’t just rely on classroom style training; rather, sit down with users and work through actual day-to-day problems. Consider also setting up open office hours where super users or hired technology partners can guide users through specific day-to-day processes.
Train Superusers – A successful analytics systems empowers users – especially key super users – to use the application on their own and not to depend on report requests to an analytics department.
Be honest and use humor – The latter can assist in convincing people to give a new system a chance. Honestly builds rapport within an organization especially during a challenging project. If one is converting from a legacy analytics system to a new one, it is important to empathize with users. They have been doing their work on the old system for years; their apprehension is natural.
Make friends with problem persons but acknowledge that not everyone will accept change – Try working alongside so-called problem persons. It will help you as a project leader to determine why they are negative and show that you are empathetic to their concerns and are personally invested in their successful transition. Note, however, there will be a minority of users that will refuse to accept the change. For the project to be successful, it may be necessary to move on and hope that they come around once the project is successful.
Be a warrior and ignore borders – Sometimes it is important to put a stop to delaying tactics such as an abundance of meetings and just move forward. Additionally, such assertiveness must be used to modify the scope of the project if it is necessary to keep the organization functioning.
Guest post by Abhinav Shashank, CEO and co-founder, Innovaccer.
Abhinav Shashank
The world of healthcare analytics is vast and can encompass a wide range of data that has the incredible potential to tell stories about health and healthcare delivery: right from individual patients to entire populations. Having numbers and an easy-to-use visualization at hand gives providers and caregivers the power to not only look into the lives of individual patients but also track the ongoing activities in their organizations. Simply showing visualizations are not enough and to fully understand their value, healthcare organizations have to take a few steps beyond basic graphs.
The Case for Data Visualization
In the words of Edward O. Wilson, the father or social biology:
“You teach me, I forget.
You show me, I remember.
You involve me, I understand.”
There are many disparate data sources healthcare providers have to deal with: EHRs, departmental data, claims data, resource utilization, administrative data, etc. Consolidating the data and spreading it out in a visually adaptive manner offers a more agile approach to managing complex population health data.
Data visualization was developed with the aim to make it easier to gain actionable insights from volumes of information and work on improving health programs, clinical healthcare delivery, and post-episode care management. Visualization provides real value in learning from disparate data sources, finding outliers, bringing out hidden trends out on the front, and delivering better health outcomes.
Streamlining Different Data Sources into a Single Source of Truth
Since the data pertaining to a patient’s health comes in from various sources, it is vital to pool all the data sets and obtain an aggregated, standard format of data every authorized person can view and manipulate.
Data in the healthcare industry can broadly be categorized into two sources:
Claims data: that comes from payers and contains extremely uniform and updated data about the care patients receive and how they are billed for it. This data is usually structured and has all the meaningful data required for provider reimbursement.
Clinical data: this data comes in from the providers’ end and contains valuable information about their diagnoses, claims, and medical history. While this data isn’t often structured, incorporates data elements critical to analyze a patient’s health in every time frame.
Fine-tuning Real-Time Visualization
The amount of data healthcare institutions aggregate is enormous: by 2012, it was estimated to be a whopping 150 exabytes (150 million * million * million) and is growing at a rate of 48 percent per year. As the volume grows, healthcare organizations need state-of-the-art, real-time analytical capabilities to improve the care quality and its effectiveness. Real-time analytics can turn the tables in ways more than one:
Monitoring end-to-end care delivery across a wide range of facilities.
Observing the progress of clinical decision support systems.
Identifying overhead cost drivers and detect care or documentation gaps.
Since data visualization holds great advantage to understand the going-ons in the organization in real-time, here are some key elements that count as best practices for data visualization:
Customized reports: Each set of users in healthcare requires different metrics and different orders. Offering customized reports with specific visualization provides actionable insights and can answer specific questions about risks, rewards, and success of the organization.
Visually adaptive: Data presented on the dashboards has to be complete with functional and visual features that aim to improve cognition and quick interpretation. Data listed in a color coded-manner will provide physicians with functional features and real-time alerts.
Create actionable insights: A dashboard or any other visualization tool will provide clinicians with the data, but unless someone looks at it, it will go unnoticed and may have potentially critical outcomes. Users should be made aware of how to review the dashboard, drill down to every immediate level, and initiate corrective actions.
The end user’s ultimate need: It’s paramount that end users can communicate their needs and demands and what is even more important is that their demands and performance indicators are incorporated well in advance of structuring the report.
Wrap-up with Healthcare IT
By leveraging healthcare IT, organizations can have their hands on simple but effective visualization and take a look at additional, important information that might have been difficult to notice in tabular format. Here are some ways healthcare IT can drive real-time data visualization to success:
Immediate access and sharing: Putting bidirectional interoperability to use, providers can access and share relevant data across the network, despite technological barriers.
Clear data visualization: Graphic, color-coded cues help physicians swiftly learn about the areas that need performance improvement or track the growth their organization is making.
Drilling down: To learn more about the reason behind certain shortfall, physicians can always drill down and narrow their area of focus to pinpoint the anomaly, and take quick remedial actions.
Driving Value with Visualization
With healthcare IT now an integral part of the value-based care system, there is little doubt that convenient, real-time data visualization will be heavily used to achieve positive health outcomes. Combining real-time data with advanced analytics will completely reshape how healthcare IT can improve clinical and operational outcomes. Once physicians move away from long, incomprehensible data flows, and find an alternative that helps them instinctively read, isolate, and act upon the insights, only then can we be one step closer to a data-driven value-based care.
Guest post by Syed Mehmud, associate of the Society of Actuaries (ASA), MAAA, FCIA, Wakely Consulting Group.
Syed Mehmud
The Affordable Care Act (ACA) produced a wealth of data from its first two years in operation. Health actuaries voraciously consumed that data, using predictive modeling techniques to solve healthcare industry problems that have never been seen before. While we don’t yet know how the ACA may change, I know actuaries will find solutions, because we thrive in the realm of the uncertain.
Actuaries have always been in the business of data. Centuries ago the work involved scanning clerical ledgers to create the first mortality tables. Today, human activity, including healthcare, is far more complex. Every two days, we create more data than was created from the dawn of civilization through the year 2000[1].
A significant portion of my recent work has involved studying ACA data, particularly deconstructing a health plan’s performance using the prism of risk adjustment.
Risk adjustment used to be a niche on the spectrum of a healthcare actuary’s work. However, since the ACA risk adjustment program is now a permanent fixture – for the time being – in commercial individual and small group markets, it is the focus of many actuaries’ every day work. Risk adjustment involves adjusting a health plan’s revenue based on a measure of morbidity of the average member enrolling with the plan. It aims to mitigate incentives to select low-risk populations, and instead re-focus the basis of competition on other factors such as quality, efficiency, and benefits delivered.
The program presents a great opportunity for actuaries to apply predictive modeling concepts on large scale data to deliver actionable insights to clients and employers. From the predictive modeling work, actuaries have learned that risk adjustment renders seemingly intuitive notions of health plan performance and profitability rather meaningless. For example, sicker and costlier individuals may have threatened a health plan’s viability in the past. But that may not necessarily be the case going forward.
