Fulfilling the Promises of Health Information Technology

The challenges to healthcare delivery today are surpassing conceivable solutions. Increasing number and severity of patients exist with concomitant decreased supply of and access to care specialists. Higher acuity patients that were formerly cared for in acute and intensive care settings are increasingly cared for in intermediate, progressive, and general care environments as well as post-acute and homecare settings. Healthcare organizations are challenged to understand under which circumstance patients may receive optimal quality, safety and efficiency; therefore, are continually seeking new knowledge to guide healthcare delivery. The connected health, telemedicine or tele-healthcare approach in particular, is a ripe area for expanding knowledge. There is a growing body of literature that demonstrates connected health technologies make healthcare more effective and efficient by electronically connecting clinicians and patients. This approach expedites remote diagnosis and treatment, enables continuous monitoring and adjustment of therapies and supports patient self-care by leveraging technology and providers across large populations of patients. In the acute care setting, the telemedicine model arose in the intensive care units (Tele-ICU) which has evolved into technology enabled tele-healthcare innovation. These innovations include advanced physiologic monitoring, clinical decision-support functions and cognitive affordances applied in a variety of care settings has shown to provide patients an advanced level of care, increased access and deceased mortality, complications, readmissions and length-of-stay (LOS).

Analytics and Research Rationale: Data Derived from Telehealth Technological Infrastructure Complement Empirical Research Designs and Methods.

Tele-healthcare innovations enable advancement of high quality scientific inquiry for generation of new knowledge about populations, the effectiveness of care delivery and prediction of outcomes. These tele-healthcare technologic infrastructures establish a treasure trove of big data in near-real time and in archive. This treasure of data allows the exploration of previously inconceivable bio-analytical techniques and computational capabilities. Clinicians may advance the discipline of data science to include non-linear modeling, machine learning and artificial intelligence. Additionally, a new era of data-driven recruitment for clinical trials is conceived. Healthcare implementation science (application of new knowledge and strategies to optimize care) will determine what works well outside the research environment in generalizable fashion through tele-healthcare delivery systems data amassments. A scientific paradigm shift is underway towards comparative effectiveness research, the evaluation of the associations between non-randomized populations and clinical outcomes that provide clinicians insights in to how an individual expresses disease and responds to therapy, precision medicine. Healthcare organizations are able to attain greater insights into the unique vulnerabilities of their patients to provide personalized care. Well-designed observational studies are valuable to understand the uniqueness of real world patients under real world standards of care. Clinicians may use the tele-healthcare data for clinical decision-making due to the increased timeliness of the data. Tele-healthcare technology influences health outcomes with predictive analytics that assist to identify early opportunities to predict and prevent (afferent arm) as opposed to recognize and respond (efferent arm).

Measure and Improve It: Insights Derived From Tele-Healthcare Derived Data Improve Clinical Outcomes and Create Positive Impact on Quality.

Substantial variation in healthcare practice exists; configuration of tele-healthcare technology may be tailored to specific needs of organizations. 

The Centralized Tele- Healthcare Hub Model promotes high quality and high efficiency as two sides of the same coin (figure 1). Operational efficiency, throughput improvements and optimal resource utilization occur through consistent assignment of right level of care at the right time. Tele-healthcare affects health outcomes through the provision of consistent and systematic application of staffing, qualifications of providers, peer-to-peer case review and sign-out, collaborative leadership and governance, and use of quality and outcome information. Tele-healthcare provides a symphony of exceptional patient care reliant on diversity of expertise, multidisciplinary team rounding, and integration of consistently applied bundles, best practices and evidenced-based protocols. Tele-healthcare technological infrastructure provides one true source of valid data for comparison of well-established standardized quality metrics and performance indicators. Tele-healthcare data exist in technologically structured tables. These data are accurate, specific, with minimal bias due to the consistent and structured nature of the data. Healthcare organizations gain actionable insights based on the tele-healthcare data to make meaningful changes to improve patient outcomes and control healthcare costs based on unbiased fair population based benchmark comparisons for mortality, complications, readmissions, length-of-stay (LOS), costs, patient level independent indicators for severity of illness and risk of mortality, standardized ratios and risk adjustments. Retrospective analysis and reporting are no longer adequate to make timely and important clinical decisions.

Business Rationale: Investment in Tele-Healthcare Advanced Data Analytics Infrastructure to Deliver Trusted Analytic Results to make Critical Clinical and Business Decisions Differentiates Data Driven Organizations.

Data-driven economic analytic evaluation provides representative analysis of quality, cost, and value to generate insights to drive smarter operational decisions and better customer service. Demonstration and interpretation of the value of investments become major strategic advantages in healthcare delivery. Variation in care affects quality and costs. Tele-healthcare systems become data-driven learning health systems differentiated by the investment in advanced data analytics infrastructure to deliver trusted analytic results to make critical business decisions. Understand that no combination of technological tools or individual efforts in isolation will provide benefit if proportional investment in data analytics infrastructure is overlooked. Timely assessment of ROI for projects and new services leads to generation and optimization of business growth-related decisions. Tele-heath data provides members of the board and heads of departments with sound decision support information on routine intervals to maintain risk-based clinical operations oversight. Further investigation must examine evidence-based medicine techniques to evaluate and improve patient care, severity adjusted prediction methods across varying practice settings, disease and intervention specific analyses and to measure performance to identify areas for further study and improvement. The next generation of research must provide clinicians, healthcare administrators, and policy makers with actionable data to guide optimal tele-innovation configuration tailored to patient type, status, and location.