Two patients come to a hospital. One is experiencing breathing difficulty, dizziness and incessant bleeding. The other frequently seeks medical care, has heart problems and is experiencing abdominal pain and pronounced swelling in his legs and ankles.
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A decade ago, both patients would start treatment at point A and go to point B before finally arriving at a specialist, point C. Today, providers are taking a more individualized approach to care and forgoing the A+B=C care model of the past in favor of customizable treatment plans that get to the core of the patient's health problems faster.
In the example detailed above, the first patient has diabetes, lung disease and a bleeding disorder. The second patient has cardiac disease, an autoimmune disorder and liver disease. Both patients need surgery. Using an approach based on a solution shop business model, each patient is assigned to a multidisciplinary care team that can ensure each patient is adequately prepared for surgery and that their respective care pathways are tailored to their individual health needs.
Solution shop business models are based on three key components: a team structure, a work method and a profit formula. The models are prevalent across multiple industries, as they are customizable and effective in solving unstructured problems. Consulting firms, advertising agencies and research and development organizations all employ solution shop business models, according to a piece from the Christensen Institute.1
Solution shop business models will serve as the foundation of team-based, customizable approaches to patient care for the next decade, according to Sonya Pease, MD, TeamHealth's Chief Clinical Officer for Anesthesiology.
"A surgical solution shop allows each patient to navigate [care] through their multidisciplinary teams for optimization in a way that coordinates their multiple specialists along the common surgical path to a cure," Dr. Pease said.
By deploying service-oriented solutions and viewing disease treatment as a team relay race instead of an individual sprint, clinicians can ensure patients with complex challenges receive comprehensive care that supports better outcomes at a lower cost.
During her 20-plus years in medicine, Dr. Pease has seen an array of changes. Today, she works with new medications, monitoring devices and equipment that didn't exist when she began her training but are now commonplace. Yet, despite all the advances, there are daily challenges associated with decreasing complications to ensure a high degree of clinical quality and patient safety. Advancements have not yet made treatment completely safe for patients with mild diseases, "which means there is more we need to be doing to continue to improve anesthesia care," Dr. Pease said.
Through precision medicine and artificial intelligence, Dr. Pease said anesthesia practices can leverage the surgical solution shop approach and emerging technologies to deliver individualized care designed to treat the complete patient.
Individualized care in anesthesiology
Anesthesiologists are positioned to be surgical leaders because they can "facilitate collaboration across multiple specialties on care pathways and robust perioperative pre-admit clinics that manage patients based on comorbidities or by the planned surgical procedure," Dr. Pease said.
For example, before surgery, a care coordinator or an anesthesiologist meets with a patient to identify and prepare for possible pre-existing conditions, such as anemia or diabetes. The coordinator then walks the patient through the entire procedure, and the care team develops a rehabilitation plan to address procedure-specific goals to enhance recovery.
These types of multidisciplinary intervention decrease perioperative complications and enable patients to be discharged quicker and with fewer readmissions, which is the solution we need in healthcare," Dr. Pease said.
Another aspect of individualized care is the emergence of precision medicine. Researchers can now tailor treatments through pharmacogenomics testing, which can give providers a better picture of how a patient will react to a drug. Pharmacogenomics testing can identify the potential for adverse drug reactions as well as the quantity of drugs needed to achieve a desired effect, among other benefits, according to an article in The Lancet.2
Precision medicine has the potential to vastly change the entire medical field. In 2016, President Barack Obama launched the Precision Medicine Initiative. A research team immediately began to seek out genomic data from at least 1 million U.S. citizens, which would be analyzed to "understand the complex mechanisms underlying a patient's health, disease, or condition, and to better predict which treatments will be most effective," according to a White House fact sheet.3 Precision medicine has already emerged in several fields of medicine such as cardiovascular, digestive and neurology, but its influence in anesthesia has been fairly limited, according to a study published in Anesthesia & Analgesia.4
However, precision medicine is still poised to vastly improve aspects of anesthesiology. For example, researchers are exploring how precision medicine-engineered pain management alternatives can be used to curb the opioid epidemic. An FDA presentation honed in on one instance concerning adverse drug reactions. Researchers discovered a small population of Caucasians (7 percent) lacked cytochrome P450 2D6, meaning they could not metabolize codeine or morphine.5 For these individuals, codeine would provide little pain relief, but would still put them at risk for the adverse events associated with the medication. With this knowledge, clinicians could drop codeine from a patient's scheduled medication list and reduce the likelihood of an opioid-related adverse event.
As with pain management, Dr. Pease believes anesthesia will benefit from precision medicine, specifically in the development of novel pharmacological agents. The opioid epidemic has prompted new research aimed at developing opioid-free anesthesia and pain management medications that could be available to physicians in the next few years, she said. These agents combine the sedative-hypnotic drug properties of currently available medications with a rapid titratable intravenous delivery system. The resulting therapies would allow for effective drug delivery with limited side effects.
Precision medicine and individualized care can both support the surgical shop model and improve care for the whole patient. Specifically, when care coordinators meet with patients to gather patient information and relay this information to the care team, this information can be used to develop individualized care plans that include therapies best suited to that patient.
AI and virtual care
AI-supported solutions play a pivotal role in supporting successful surgical solution shop models. By incorporating automation and data analytics into individualized care, elements like advanced early warning systems — wireless surveillance technology that uses algorithms to recognize potential symptoms and diseases — will allow clinicians to better utilize technology to recognize patient deterioration and activate interventions.
Dr. Pease said AI will also support nurse avatars that will reach patients in their homes via telemedicine. Patients will be able to interact with these avatars, and the avatars will lead patients through exercises and repetitive tasks to ensure optimal preoperative and postoperative care.
"With analytical and reasoning capabilities and a wide range of clinical knowledge, this automated routine work will support clinical decision-making and free clinicians to focus more on direct patient communication and complex care decisions," Dr. Pease said.
Research published January 2018 in Baylor University Medical Center Proceedings examined the role of AI in anesthesia. Researchers found that while many attempts to incorporate automation into anesthesia have failed, "recent innovations in AI, especially machine learning, may usher in a new era of automation across many industries, including anesthesia."6
In addition to the nurse avatars, AI can also facilitate clinical guidance that better tracks real time data to optimize anesthesia care. Such automation requires rule-based, closed-loop feedback systems to manage or maintain general anesthesia within patient specific goals of care such as goal directed fluid therapy or low pressure ventilation. Efforts to develop these types of systems are in their infancy, but experts still believe systems like these are the next generation of technology improving patient safety and quality.6
Researchers argue that while AI will influence how anesthesia is practiced, the complexity of the specialty will limit it from ever achieving full-automation. These researchers feel the best utilization of AI in anesthesia will be to support elements of perioperative home care and clinical decision support.6 Dr. Pease echoed this sentiment, since AI can interpret hard data elements but can never replace the tactile feel of a patient's response to therapy and clinical judgement calls that occur minute to minute within the operating room. AI may be able to interpret patient's vital signs but will not appreciate the nuances of the surgical care playing out in the surgical field that impact those vital signs.
"In the specific case of anesthesiology, these innovations may allow anesthesiology the freedom to reinvent itself from an intraoperative specialty to one of true perioperative medicine," she said.
AI-supported tools like nurse avatars allow clinicians to reach patients outside of the hospital. Advances in AI-based diagnostic technologies can also flag patients and alert clinicians to conditions before appointments. Such advancements support the solution shop model by extending and sharpening a clinician's expertise and ability to generate better outcomes.
The solution shop model and the evolution of anesthesiology
The emergence of the solution shop model is an example of healthcare's continued evolution. A concept created in the business space is being co-opted into healthcare to continue improvements in patient-centered care. This isn't the first time healthcare providers have looked to outside industries to improve processes, according to Dr. Pease. Surgical checklists, safety protocols and Lean Six Sigma were all adopted from other industries and have made deep impressions in healthcare.
The future of anesthesia care will be largely influenced by personalized medicine and AI-supported technologies. By incorporating these advancements into a solution shop model, practices will be able to standardize processes, improve patient flow, improve clinical outcomes and decrease waste and error.
"By being more solution-oriented, we can better coordinate care and manage complex patients across the continuum of care in a more cohesive manner," Dr. Pease said. "These types of multidisciplinary interventions decrease perioperative complications and enable patients to be discharged quicker and with fewer readmissions, which are solutions we need in healthcare."
References
1 Wanamaker, B. (2016, August 22). Why Cleveland Clinic always wins: The solution shop business model. Retrieved from https://www.christenseninstitute.org/blog/ why-cleveland-clinic-always-wins-the-solution-shop-business-model/
2 Ferner, R. E., & Aronson, J. K. (2010). Pharmacogenetics and adverse drug reactions. Adverse Drug Reaction Bulletin, &NA;(264), 1015-1018. doi:10.1097/ fad.0b013e328340bc88
3 White House. (2015, January 30). FACT SHEET: President Obama's Precision Medicine Initiative. Retrieved from https://obamawhitehouse.archives.gov/the-pressoffice/2015/01/30/fact-sheet-president-obama-s-precision-medicine-initiative
4 Iravani, M., Lee, L. K., & Cannesson, M. (2017). Standardized Care Versus Precision Medicine in the Perioperative Setting. Survey of Anesthesiology, 61(5-6), 143. doi:10.1097/01.sa.0000527518.23397.ca
5 Center for Drug Evaluation and Research. (n.d.). Drug Interactions & Labeling - Preventable Adverse Drug Reactions: A Focus on Drug Interactions. Retrieved from https://www.fda.gov/Drugs/DevelopmentApprovalProcess/DevelopmentResources/ DrugInteractionsLabeling/ucm110632.htm
6 Alexander, J., MD, & Joshi, G. P., MD. (2017). Anesthesiology, automation, and artificial intelligence. Baylor University Medical Center Proceedings, 117-119. doi:10. 1080/08998280.2017.1391036
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