Norma is a 60-year-old obese female with a relatively uncomplicated medical history. She has a great family life, spends plenty of time with her children and grandkids, and overall enjoys her career as a secretary in a small, rural hospital. However, the hospital was recently acquired by a larger urban, hospital network. There was a massive overhaul in their enterprise web services network which was stressful for many employees.
Norma was not enthusiastic about the abrupt change, especially because she had become so accustomed to the previous system. With a few years left until retirement, she was ill-prepared for such a drastic maneuver. While her younger colleagues picked up computer skills easily, her aptitude for learning new networks paled in comparison.
Norma needed some R&R after the first few weeks of the transition period, and visited the Outer Banks with her granddaughter. The trip was fun-filled and relaxing, but there were many children around who had common viral upper respiratory infections.
Presentation and Examination
Two days after returning from vacation, Norma arrived at work early to play catch-up on what she had missed while away. She walked into the hospital and suddenly became very dizzy. As she slumped to the ground, a colleague who was walking by, caught her and immediately contacted nurses in the Emergency Department.
While in the ED, as long as she remained idle she felt fine. However, as soon as she moved her head she would feel faint. Her systolic and diastolic blood pressures were slightly elevated with a reading of 122/85. The basic neurologic examination was normal although her complete metabolic profile showed blood glucose was 400 mg/dL and the serum ALT was elevated. An ECG showed a mild irregularity of heart rhythm without evidence of ischemia. She was discharged from the ED with instructions from the physician to follow up with Norma’s family physician and a cardiologist.
The day after she returned home, she was walking down the stairs and became dizzy once again. Her two-story house was notorious for its dim lighting and she certainly paid for it that day, as she took a tumble down 15 steps. Her hip pain was immense and she could not get up after the fall, but had her cell phone with her and dialed 911 for assistance.
Back in the ED, an X-ray of her hip presented a subcapital hip fracture. Norma underwent an emergency surgical procedure for total joint replacement. Post-operatively, she was on low molecular weight heparin but still developed a deep vein thrombosis (DVT). Thankfully, she did not develop a pulmonary embolism and the thrombus resolved without incident. She made a good functional recovery but had a slight limp after the injury and surgery.
Subcapital fracture, deep vein thrombosis, vertigo, home safety
Analytics: Easily Implemented Methods of Prevention & Tools for Predicting Risk
Medical algorithms can save hundreds of thousands of lives, and prevent the adverse outcomes that were showcased here. The Medical Algorithms Company (TMAC) is a platform with analytics which physicians can use to address many of the complications that arose in this case.
Upon admission to the ED, physicians could have administered a few different algorithms to determine what was the root cause of Norma’s dizziness. The possibility of a schwannoma could be addressed using the algorithm: Classification for Jugular Foramen Schwannoma.
With Norma’s symptoms, it likely would have been reasonable to assess her for metabolic syndrome. When blood sugar is very high, patients may experience wooziness. Norma’s physicians in the ED should have used Criteria for Diagnosis of Metabolic Syndrome. Features of this illness include: (1) obesity; (2) physical inactivity; (3) high blood pressure; (4) abnormal blood lipids; (4a) high serum triglyceride level; (4b) elevated LDL; (4c) decreased HDL; (5) abnormal glucose metabolism; (5a) increased fasting blood glucose; (5b) insulin resistance; and (5c) abnormal glucose tolerance test.
There are several fall-risk algorithms that can assist physicians in evaluating risk for Norma’s conditions:
Risk for Osteoporosis
- The Simplified Calculated Osteoporosis Risk Estimation (SCORE) to Help Identify Women at Risk for Low Bone Density
- The ABONE Score to Select Women for Osteoporosis Screening
Risk for Hip Fracture
- Risk Factors of Roche et al for Postoperative Complications Following Repair of a Hip Fracture
- Nottingham Hip Fracture Score for Predicting Mortality within 30 Days of Hip Fracture Surgery
DVT – Deep Vein Thrombosis
- Risk Assessment Profile (RAP) of Greenfield et al for Venous Thromboembolism in Adult Trauma Patients
- Hip fracture and DVT are both preventable with the implementation of analytics in clinical practice
- Medical algorithms, if used strategically and appropriately, can be instrumental in diagnosis, assessment and disease management
The medical algorithms highlighted in this case study are available at The Medical Algorithms Company and also on the Apervita health analytics platform.
Apervita is the leading health analytics community and marketplace, where prominent health professionals and enterprises from around the globe are transforming the world’s health knowledge into thousands of health analytics.
At Apervita, we believe that health researchers and practitioners have already created the greatest wealth of health knowledge that has ever existed and it is just waiting to be unleashed to improve health. Today, the majority of this knowledge is paper-based or locked into proprietary systems. The Apervita community is already unlocking them, turning them into computable and shareable analytics and putting them to work to improve health. They are addressing some of the biggest health challenges, such as the 100,000s of patients that die prematurely every year in the United States from chronic disease, complications and preventable adverse events.
Apervita is a secure, self-service platform, that enables health professionals and enterprises to author, publish and subscribe to a market of evidence-based algorithms, quality and safety measures, pathways, and protocols, easily connecting them to data and workflow. Available to every health professional and powerful enough for the entire health enterprise, Apervita provides health analytics at a tenth of today’s cost, in a hundredth of the time.
About the Authors
Farva Jafri is a manager at Apervita, Inc. on the Content & Authoring and Legal teams. She has a Juris Doctorate and MBA from University of Illinois at Urbana-Champaign, and a Masters of Public Health from the University of Southern California Keck School of Medicine. Farva has an extensive background in consulting, marketing, research and legal fields. She works closely with TMAC to bring many of their high-quality algorithms onto Apervita’s platform.
Dr. Chad Rudnick, MD, FAAP is a board-certified pediatrician in Boca Raton, FL. He is the Medical Director of The Medical Algorithms Company. A proponent of incorporating medical technology into his practice, Dr. Rudnick uses telemedicine and medical algorithms from The Medical Algorithms Company in his daily practice to better serve his patients and their families. An accomplished medical writer, he maintains a popular pediatric blog, All Things Pediatric, and has written for numerous online and print publications including KevinMD.com.
John Svirbely, MD is a founder and Chief Medical Officer of The Medical Algorithms Company and the primary author of its medical algorithms. John is a co-founder of the Medical Algorithms Project and has developed its medical content for nearly 20 years. He has a BA degree from the Johns Hopkins University and his MD from the University of Maryland. He is a board-certified pathologist with a fellowship in medical microbiology and biomedical computing at Ohio State University. Currently he is in private practice in Cincinnati, Ohio. He has authored multiple books and articles on medical algorithms.
Note: This case study is fictional and based on actual events.