Gravitas research provides the data analysis and models that are critical for clinical or technology innovation sustainability. Comorbid brain illness research, on each chronic and life threatening illnesses that is most impacted, in specific study populations, with additional technology and disease management preferences data reported by consumers, provides tremendous opportunity for clinical and patient support. The research follows the same rigorous processes used for clinical guidelines by using systematic randomized clinical trial study, health record and outcomes review, cost to treat, and patient outcomes, so that clinical practice support solutions follow the course of the condition. This means that disparate process, diagnostics, and support solutions can be replaced by seamless end-to-end clinical practice technologies that use evidence based metrics to reduce illness incidence or severity.
Comorbid disease management has not been a priority to mainstream healthcare. This may be due to the current demands of the HITECH Act that are supported by Federal dollars, or the change that would be required by payers and providers who are currently focused on treating one illness at a time. The greatest challenge is that when intervening in two biologic pathways, science must be included in medicine, and this is rarely done in healthcare settings. This is why Gravitas Research Partners include pharmaceutical and biotech companies, as no organization understands it more. Brain health has become a priority from the BRAIN initiative to the vast challenges faced by a medical system without enough neurologists and psychiatrists to solve them. Brain illness, disease, and injury are the largest and most mismanaged problems that people and doctors face.
There is a lot of credence given to using patient record data to improve the quality of inpatient and outpatient health services. In the dialogue of its benefit, it has not been translated to the patient community that the benefits of studying it provides more advanced scientific discovery, earlier and more responsive diagnosis, and the training of doctors in interdisciplinary medical management that expands their ability to provide whole person care. And while health record data is important, it must also be recognized that the data is biased to the insurance provider care coverage, the population that the closed health network serves, and the lack of completeness of medical records because they cannot yet be electronically shared. This is the primary basis for our more rigorous study of comorbid brain illness.
Pathophysiology doesn’t change when you cross the street. What often changes when patients go between competing health providers is the technology used in electronic health records, quality and decision support systems, and patient engagement tools that may result in differing care management solutions. This happens most often when specialists become more of the patients primary care giver, simply because they see them more. The bi-directional pathophysiology of brain illness also has more influence on the severity and trajectory of the primary illness, which makes the specialist more vulnerable to increases in the cost of care, thus effecting reimbursement. Providers and payers also fair worse, as the investment each has made into the technologies and covered services require patient adherence to the course set for them in order for both the provider and the patient to benefit cost efficiently.
Understanding how decisions are made and how the brain functions is a critical piece of information when developing clinical practice or technologies that depend on human behavior. Just as the intellectual capacity of patients impacts treatment adherence and response, uncovering the brain’s influence on disease severity, complications, or the causation of new conditions provides exponential value to doctors.
Clinically Relevant Measurements
The ability to develop measurements for predictive analysis, risk, outcomes, medication response, quality, cost, value, mortality, and decision support systems using the combined power of randomized controlled trials, health data, and patient decision profiles guides more finite solutions within larger populations. Population health data is beneficial for a single condition, but without large enough cohorts of patients with the same multiple conditions, it can’t provide the power needed for statistical modelling in comorbidity.
Compelling Diagnostic Measurements
Brain and health assessments come in many forms and are often ignored based on the time doctors and patients have together. Baseline and severity measurements can be collected in a variety of formats and immediately consumed by EHR analytics when it is known precisely what to look for and how to model aggregates.
Health Data Collection and Analytics
Technologies being developed, or improved, to collect and utilize self-reported information and assessments can address specific clinical management inputs and synthesize it across other data entry points that can also benefit from our research. This creates more seamless workflows using empirical evidence of disease models within analytics programs.
Disease Management and Coordination
Informed technology expands the capacity, capability, and comprehension of every medical professional and generalist involved with inpatient and outpatient care. Similar data structuring can be used to coordinate care beyond reporting tests and brief notes that are cumbersome and disconnected to any “whole”.
Interdisciplinary Care Management Support Systems
Be realistic and be prepared. There are 246,090 practicing primary care doctors and 321 million people in the US. There are 25,040 practicing psychiatrists and 57.7 million people over 18 with mental health conditions. There are 16,366 practicing neurologists and over 50 million people with neurological disorders. Doctors that treat chronic and life threatening disease can quickly intervene, but they and their staffs need interdisciplinary training and support systems designed from real science and evidence based comorbidity guidelines.
Expanded Practice Services
Expanding the role of doctors requires expanding the responsibilities of their support staff and technologies that can manage the billing for them. Our research also supplies the data needed to support quality, value, and reimbursement justification.
Knowing the Patient
Patient decision and preference models support instant familiarization with what patients need, what they understand, and what they will follow through with.
Patient Engagement and Self-Management Support Systems
Different people respond to different technologies such as mobile, gaming, video, self-assessment, learning environments, and links to health providers, despite age or level of education. Matching self-management technology with individualized condition information that they can use to monitor their progress, assess severity, and see the results of options or choices based on clinical data is more empowering.
Care Value and Quality Measurements
Saving money and saving lives are two different measurements, unless the clinical practices can demonstrate reducing the incidence and severity of a disease, in which case, it does both. This is our primary objective.
Technology Design and Purchase Metrics
In essence, our research also provides a “technology biomarker” using the results of our disease profile and care models to evaluate internally developed technology or make purchasing decisions.