Two promising areas for digital advancement in healthcare are treatment planning and outcome measurement. The challenges surrounding both areas have to do with lack of data, process uniformity, communication, and high-level data analysis. Today’s technology can help address these challenges.
Treatment & Measurement Challenges
The foremost method for improving treatment planning is improving diagnosis, which we addressed here. But more accurate diagnoses don’t solve all treatment challenges. For instance, it’s well-known in the medical community that a particular medicine or course of treatment may work well for one person but not for another. The reasons for this aren’t always clear. In addition, lack of evidence-based data to support one treatment regimen over another leads to variation in care, as physicians rely on anecdotal evidence. Non-standardized pre- and post-care treatment results in confusing and inefficient care that negatively affect performance measurements. And at the end of the day, clinicians are human—they sometimes make mistakes (hence the existence of malpractice insurance).
Problems arise as healthcare organizations attempt to measure outcomes, both for regulatory and for operational reasons. Measurement of things like mortality, safety of care, and readmission rate is not always accurate. This is primarily due to lack of data reporting, inconsistent data structure and storage silos, and no (efficient) way to take a high-level, holistic look at data to identify patterns and correlations.
The past few decades have seen an incredible rise of new technology designed to meet the specific challenges of healthcare, and there are a plethora of creative devices, programs, and platforms capable of hurdling over treatment and measurement challenges.
Robotic Augmentation & Automation
Fast, precise treatment that enhances the patient experience is something every healthcare organization strives for. But sometimes it doesn’t happen. Maybe there’s a process issue, or medicine is out of stock. It could be a patient who can’t reach medical facilities quickly or simply a tired surgeon whose hand slips a bit. Robotics has branched out into nearly every area of healthcare to solve these issues.
For instance, Robotics Process Automation (RPA) is increasing efficiency in digital processes, and auxiliary robots are taking care of non-clinical work—resulting in more efficient operations.1 So medicines are stocked, rooms are cleaned and prepped, and scheduling snafus are avoided.
Robots are automating and augmenting clinical processes, too. From the laboratory to the operating room, robots have been aiding or completely performing complex tasks with a need for high precision.2 For example, the University of Pittsburgh School of Medicine and Carnegie Mellon University have teamed up with clinicians in various fields to develop the TRAuma Care in a Rucksack (TRACIR) for the U.S. Department of Defense. TRACIR will be an “autonomous, robotic-controlled” CPR system small enough to fit in a soldier’s rucksack and delivered via drone to remote battlefields—a “robotic medic,”
Network & Platform Integration
As more and more providers and patients connect, more data is being exchanged (url to post 1)—often in real-time—benefiting both treatment and measurement. This may be spurred on by the proposed interoperability rule from the Office of the National Coordinator (ONC). With open connectivity and data sharing, all parties can work together to provide better acute and chronic care coordination. When hospitals and clinics communicate treatment records, primary care (PCPs) and behavioral health providers can work with patients to develop long-term treatment plans.
A relatively new trend is the use of Patient Relationship Management (PRM) platforms, a healthcare-specific variation of the business Customer Relationship Management (CRM). These platforms rely on interconnected networks to supply the wide-ranging information sources that enable it to provide a big picture view of a patient’s health. Participating apps, providers, community resources, and care teams can collaborate to create a comprehensive care plan. Piedmont Healthcare, of Georgia, has implemented a PRM, allowing them a more encompassing patient view by combining medical history with social determinants of health (SDoH).
Apps & Smart Devices
Apps and smart devices play a major role in tying together data sources for this holistic view. With devices reporting everything from blood pressure to the time patients take their meds, the Internet of Things (IoT) is providing automated information collection. These devices aid providers in coordinating further care instructions based on readings. An example is IBM and Pfizer’s “Parkinson’s House.” IoT sensors placed throughout the home—on appliances, doors, everything—send providers readings that help them determine the effectiveness of the prescribed medication and make dosage adjustments. Self-reporting via new healthcare apps like PRISM5 allows patients to communicate symptoms and concerns to their PCPs and receive real-time feedback and recommendations.
“APIs and apps underpin our ability today to connect data, knowledge, and action . . . including social services, government services, banking, education, commerce, and transportation.” -UCSF President and CEO Mark Laret, Associate Chancellor for Informatics Michael Blum, MD, and Chief Information Officer Joe Bengfort.
Big Data & Analytics
This new age of interconnectivity has produced enormous amounts of data, but raw data without some semblance of order isn’t helpful to frontline personnel or executives. Big data and analytics step in here to apply structure and logic. This technology collates and analyzes previous patient outcomes and studies, so providers can create evidence-based treatment programs. One interesting development is the use of big data in precision and genomics-based medicine. The theory is that the unique genetic makeup of each patient influences whether or not specific treatment methods are effective.
“Big data and predictive analytics allow the involved parties to uncover unknown correlations, insights, and hidden patterns . . . These can be applied effectively at the individual level, and consequently caregivers are more likely to come up with the correct treatment . . ..”
Big data and analytics can also work to measure and track population health.8 Tracking mortality and readmission rates, safety of care, and effectiveness of care positions healthcare organizations to provide better population health management and meet value-based care requirements. Programs like the American Society of Clinical Oncology’s (ASCO) CancerLinQ initiative are able to document and assess patient care data for both individuals and larger populations, using a careful screening method to anonymize protected health information (PHI).
Digital advances are steadily reducing costs and improving care by augmenting treatment and measurement options. Smart devices and apps provide more patient data, interconnected networks transmit and share information, and big data and analytics platforms assess and order it into usable forms. All of this means more accurate and responsive treatments, end-to-end care coordination, and better outcome measurements.
Healthcare organizations can only thrive by proactively innovating—pursuing ways to put digital technology to use and implementing in a way that serves everyone. Today’s technology for better treatment and outcome measurement is a necessary step in digital transformation.