Inovalon is a unique breed of technology company – one focused on applying advanced cloud-based data analytics and a data driven intervention platform to empower and achieve large-scale assessment and meaningful improvement in clinical and quality outcomes, care management, and financial performance across the healthcare landscape. At its core, Inovalon is an analytically-focused solutions development and data-driven intervention platform – informed by deep clinical insight and unparalleled proprietary datasets – able to not only arrive at highly valuable healthcare insights, but implement the solutions necessary to turn the insight into meaningful impact and realized value – end-to-end.
Inovalon's technologies bring the power of advanced, enterprise-scale analytics to the point of care, empowering the healthcare marketplace to achieve both meaningful insight and valuable impact. From data integration to value realization, Inovalon's cloud-based, analytics platform drives meaningful improvement in clinical and quality outcomes, and financial performance with unmatched effectiveness, speed, and efficiency.
In deploying our technology, our clients want us to synthesize opaque, convoluted, and disparate data into actionable information aligned with individualized goals and, in turn, empower a patient and provider intervention platform that achieves the realization of their goals in a measurable way. The diagram below illustrates the components of our technology platform.
Our platform's capabilities are currently supporting approximately 200 patient populations that leverage our ability to analyze and improve clinical and quality outcomes and financial performance. This platform is applied in a variety of environments with many additional applications of the technologies being planned.
Datasets and the management of data are part of our core strengths, which give us insight into how a patient, provider, or population is doing. It grants us both relative and absolute insight, and informs the construction of new capabilities, predictive models, and impact predictions. It speeds our time to client impact, decreases the burden on clients choosing to do business with us, and empowers our achievement of mission and results.
We believe that our enterprise-scale data integration and management processes are a critical capability in achieving a material improvement in clinical quality outcomes and financial performance in healthcare. We integrate data seamlessly and securely into our systems through our proprietary ETL tools and processes. This system manages the process of defining and configuring thousands of industry data feeds from our clients and partners, manages the data processing workflow, and monitors the ongoing provision and quality of data through the application of more than 2,000 data integrity checks.
In addition to being maintained and tagged within client-specific data lakes, data we receive in the course of providing our services are statistically de-identified and stored in our MORE2 Registry®. As of December 2016, this registry contained more than 13.3 billion medical events from more than 150 million unique patients, 848,000 physicians, and 371,000 clinical facilities, touching 98.8% of all U.S. counties and Puerto Rico and growing at a rate of 3.8% annually since 2000. The MORE2 Registry® goes beyond just claims data to include information about demographics, enrollment, diagnoses, procedures, pharmacy, laboratory results, and deep medical record clinical data and presents a significant representative mix of commercial, ACA Marketplace, Medicare Advantage, and managed Medicaid care plan patients. The following is a sample of various components within our MORE2 Registry®.
- Patient Demographic Data
- Benefits Data
- Medical Record Documentation
- Encounter and Procedural Data
- Operating Room, Procedure, Discharge Summary Emergency Room Records
- Pharmacy Data
- Imaging Report Data
- Laboratory & Pathology Data
- Electronic Health Record Data
- Durable Medical Equipment Data
- Health Risk Assessment Data
- Self-Reported Data
- Practitioner Profile Data
- Social History Data
- Claim Diagnostic Data
- Activities of Daily Living (ADL)
- Eligibility and Enrollment Data
- Cost Data
For years we have developed, honed, and scaled a portfolio of sophisticated cloud-based analytics. Applying our team’s deep subject matter expertise in compute processing, data architecture, statistics, medical sciences, and healthcare policy, and leveraging the billions of medical events within our significant propriety datasets, we believe that we have developed one of the most advanced analytical platforms within the industry, as well as a culture and set of analytical toolsets that serve to rapidly innovate and significantly expand our platform. Examples of the innovative analytics powered by this combination of data and processing capabilities include:
Disease and co-morbidity presence and closure probability determination analytics
Arriving at an accurate understanding, documentation, and codification of the disease states of patients is critical. In addition, through a proper understanding of each patient’s needs, care can be more effectively guided and delivered, quality achieved, and financial implications understood. In order to guide the efficient use of resources to clarify the disease state of each patient across the landscape of tens of thousands of codes, analytics are employed to predictively determine whether a disease or comorbidity is being overlooked or is progressing at a rate or severity otherwise not noted. Analytics that transcend a single point in time, location, or point of view to take into consideration a more holistic view both in absolute terms (i.e. solely with the patient data in mind) and relative terms (i.e. taking into consideration millions of other similar and different cases) can be achieved. In addition to determining the potential presence of specific disease and comorbidities, our analytics can be applied to determine the statistical probability of successfully confirming and resolving such a potential gap between known and suspected disease conditions. In this way, resource prioritization can be achieved.
Clinical and quality outcomes gap presence and closure probability determination analytics
Every patient, whether healthy or acutely, or chronically ill, needs a specific set of preventative or treatment-based healthcare services in periods specific to each patient’s clinical profile. Additionally, patients with specific conditions, such as diabetes, need specific elements of care such as blood sugar testing, medication compliance, and examinations to detect complications of diabetes. Standards within the industry around quality of care have been created by organizations such as NCQA, URAC, PQA, NQF, and medical societies looking to provide thought leadership on behalf of their patients. In order to help guide patients and their physicians in addressing the preventative care and treatment needs of each patient, our predictive analytics are employed to determine each patient’s clinical profile, their compliance with treatment protocols and quality measure standards, and how these match up to established quality standards. Further, our analytics are not only focused on determining accurate quality measure profiles, but also on predicting which measures that are unfulfilled today will become resolved on their own by the actions of the patient or provider independent of any new intervention. Not only do these analytics empower better quality care, but they make care more cost effective, by suggesting the avoidance of unnecessary testing, diagnostics, or treatment, that may not benefit the patient or change the patient’s clinical course based upon historical patient behavior.
Medication compliance and persistence analytics
Critical management of many chronic conditions is the effective utilization of prescription drugs to stabilize disease progression, ease symptoms, and facilitate healing. However, many barriers exist to patients reliably filling their prescriptions and taking the medications that their physician has prescribed, including the cost of treatment, the side effects of treatment, and the patient’s engagement in the treatment process. In order to determine which patients are the most likely to achieve compliance with their prescribed treatment, the least likely, and susceptible to influence, we apply predictive models that examine patients against their historical behavior patterns and clinical profiles to guide the right resources to the right patient in order to maximize medication compliance and persistence.
Principally Relevant Provider (PRP) determination analytics
In order to best engage a patient with the healthcare delivery system, it is important to identify the physician whom the patient considers to be his or her PRP with respect to specific issues needing attention. Particularly important for patients with chronic conditions or complex issues that see multiple physicians, the determination of which physician possesses the greatest bond can make a significant difference when seeking to assist the patient with resolution of an identified concern. In some cases, for instance, the patient’s health plan assigned primary care provider may or may not be the physician that has established a trusted care provider relationship with the patient. Rather, a patient’s key specialist may be most applicable to address the patient’s needs and to engage the patient in effective self-management. We analyze utilization patterns, follow-up patterns, treatment compliance patterns, and other patient behaviors to help identify the provider that is most relevant to address specific issues which the patient may need addressed within their care plan.
Targeted intervention timing optimization analytics
While the clinical lives of patients always present opportunities for improvement, the presence of a gap does not necessarily mean that such gap should be acted upon with high intensity, or even acted upon at all depending upon the historical utilization patterns of the patient. Through predictive models that examine the historical behavior patterns of the patient in combination with the gaps that need to be addressed, optimal intervention timing can be achieved to allow the patient to address his or her gap without external intervention based upon their preferences in utilizing the healthcare system, suggesting the intervention occur only after the patient would have been expected to act on their own. Often watchful waiting may be the most appropriate recommendation. By watchfully waiting and evaluating the patient’s self-management of his or her issue, resources can be applied only after the patient has demonstrated a failure or delay in acting themselves. Through successful intervention timing analytics, multiple goals can be achieved: cost avoidance (by not undertaking costly interventions that may not have been needed), confusion and frustration avoidance (by not accidently directing a patient or provider to undergo an intervention when the same was imminently being done), and resource planning (by having insight into when during a year an intervention is most likely expected to be needed).
Targeted intervention venue and logistics optimization analytics
For those patients who have been identified with a gap that needs to be addressed, in order to cost effectively deliver the appropriate care and achieve gap closure, the right intervention tool must be selected and deployed to effectively address the specific patient and their needs. This avoids deploying a low cost activity, such as a message or phone call, when such an intervention has little or no likely or predictable ability to achieve gap closure, while also avoiding deploying high cost activities, such as a home visit or emergency room visit, when the gap could have been easily addressed through a scheduled appointment at a convenient retail clinic or provider office. Applying analytics to determine the right venue for gap closure, sensitive to the cost profile and effectiveness of each, is critical for achieving cost effective and high quality healthcare.
Gap resolution valuation determination and prioritization analytics
Because patients have multiple gaps and needs, particularly those patients with chronic conditions, it is important to prioritize which gaps need to be understood by the patient and addressed in a manner that increases their engagement and self-management capability, without overwhelming the patient or provider. As such, analytics must be employed throughout the year to evaluate the unresolved gaps of each patient and prioritize the resolution of such gaps based upon the combined likelihood of closure and the ultimate value of closure to the patient and their health plan. By understanding the context of each gap in light of the patient’s full clinical profile and by understanding the patient’s situation in light of the health plan’s quality metrics and financial performance, gaps can be valued and prioritized to make sure that the most important gaps are known and addressed at the right time for each patient.
Population simulation analytics
We apply advanced analytical processes to create propensity-matched patient cohorts from our MORE2 Registry® to simulate the characteristics of patients, their behavior, their providers, and how these factors translate into their utilization of healthcare resources, financial performance, and the achievement of clinical quality and outcomes goals. This simulation process allows us to effectively provide a control group for demonstrating the outcomes trajectory of such patients in comparison to populations that we manage to highlight performance variations. This simulation process also allows us to understand these populations and design effective tools for improving their quality of care and clinical outcomes. Additionally, these simulations allow us to bring new technology capabilities and associated products to market more quickly, accurately, and cost effectively. Lastly, these simulations allow us to gain insight into how a potential client population may perform, enabling us to have an additional differentiator during a sales process.
Relative Comparative Analytics
An increasing number of measurement, incentive, shared savings and reimbursement programs are based upon ‘‘budget neutral,’’ ‘‘zero sum games,’’ and other relative or comparative models. Using our data and analytics capabilities, we can inform the relative comparison of population and cohort performance levels to assist in guiding strategic investment decisions. More importantly, we can perform these analytics during a relevant date of service period so that our clients can gain insight into how they are performing and how they can make changes within their patient and provider groups to improve their outcomes while there is still time within the relevant date of service period to achieve improvement. In the absence of comparative analytics, many organizations would otherwise use a previous year’s results to guide changes — a set of data that often does not even become available until well into a year, let alone representing information that is long outdated and largely irrelevant when performance is not only based upon how one is doing, but moreover based upon how one is doing in comparison to others.
Our data-driven intervention platform is a cloud-based toolset and service that enables our clients to take the insights derived from our analytics and implement solutions at the patient and provider-level in order to achieve meaningful impact. While most of our clients engage with us to apply our data-driven interventions in an end-to-end formulation to achieve value realization from our analytics, some clients utilize our analytical outputs to achieve value on their own. Others license our intervention platform to support their ability to achieve data-driven impact. Yet others engage us to not only license our intervention platform, but also provide the personnel services necessary to leverage these toolsets and actually achieve the patient and provider-level impact. Examples of our intervention platform tools include:
- Point-of-care tools that provide patient-level insight to the healthcare provider, which guides the provider through precise data-driven topics, issues, and decision support to aid in the assessment, documentation, and care of a specific respective patient. For example, our analytics may identify that a patient’s diabetes has potentially progressed — possibly due to a non-compliance with their medications. Our decision support tools provide a mechanism for this information to be made known to a provider in such a way as to help them know that a patient visit may be warranted, aid them during the patient clinical encounter to efficiently determine the situation with the patient, support proper documentation, reporting, and outcomes measurement;
- Communication tools that support a wide range of notifications and interactions with patients and providers via phone calls, mail, SMS messages, e-mails, etc., at the appropriate level of implied education and language to aid in the process of achieving patient and provider actions. It also may include education outreach which coordinates the communications with health plan patients regarding their health issues and to support self-management of their conditions by guiding them to supplemental resources, coaching and health literacy;
- Supplemental patient encounter tools that facilitate the coordination of data-driven patient encounters for those who are unable to participate in traditional office encounter venues; and
- Clinical data tools that facilitate electronic clinical data extraction, remote accessing, and clinical facility communications for site, scheduling, clinical data extraction, abstraction, review, quality control, archiving, and process tracking — regardless of the underlying clinical data medium (e.g., digital or paper).
Our business processing toolsets are made up of a powerful business intelligence system and comprehensive data warehousing to provide historical and current data insight, reporting, and benchmarking to support multiple client business needs such as government-mandated data filings, financial planning, and compliance requirements. Examples of our business processing tools include:
Data Warehousing and Business Intelligence: We provide toolsets that enable comprehensive warehousing and management of healthcare data in raw, native formats as well as processed, high-integrity data. We provide the flexibility and accommodation for healthcare practice groups who have varying levels of data sophistication — from advanced electronic connectivity (i.e. remote medical practices) to onsite digitization and collection, to self-provision of medical records via fax, mail and electronic mail allowing for clinical data collection throughout the U.S. These datasets are presented to our clients’ users through business intelligence systems that include flexible dashboards, parameterized reports, and ad hoc querying capabilities for summarizing key analytics, allowing for the investigation of data trends and deeper data segregation and analyses, and access to key benchmarking information. These data warehousing and business intelligence toolsets are built on industry leading technologies to integrate our clients’ data (e.g., provider, facility, patient, enrollment, benefits, lab results, pharmacy, claims, quality scores, financial metrics, performance forecasts, etc.) and the data results and benchmark information from our MORE2 Registry®. We are able to provide our clients with the ability to gain insight into both their own data and their own data in comparison to our large integrated dataset to help improve the quality of care provided to patients, drive financial performance, and aid in strategic business processes of the client organization.
Data Management and Submission: Leveraging our data warehousing toolsets, our solutions help our clients to manage their data and translate their data into the formats necessary for, among other needs, submission to government entities in support of quality and outcomes measurement and revenue determinations, and provision to their various internal and external business processes. These data management solutions address the formulation of data submission files in summary and patient level-data format as required by regulatory bodies, as well as the workflow processes to receive submission response files to support the reconciliation of data submissions, corrections to data submitted with response issues, and resubmission processes. These processes operate in an integrated manner with our business intelligence solutions to provide our clients visibility into the details of their data submissions at the population level, the patient level, the attributed provider level, and for user defined custom cohorts.