Cognitive Impact

All Cause Readmissions

More than any other single measure, readmissions has come to define success in value-based care.
Many care factors influence patient readmissions, including diagnostic accuracy, workflow and care coordination, effective discharge planning, clinical decisions and interventions, medication management, and more. With all of those components of care quality in play, patient readmissions performance has become a bellwether for success in value-based care in hospitals.
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From the inception of the ACA, the Centers for Medicare and Medicaid Services (CMS) identified preventable 30-day readmissions as a critical care quality measure—with significant reimbursement rewards and penalties for performance.
Ten years in, new discussion and debate still emerges as to whether hospitals have or have not truly decreased readmissions. The target also keeps moving, as hospitals prepare for new rules to grade readmissions performance against peer benchmarks.
The imperative for hospitals and systems remains to continue addressing readmissions performance as a top quality and financial priority. Let’s look at the data that spell out the challenges—and how the AI-based Cognitive Clinical Machine is delivering reliable success in reducing hospital readmissions rates.

The readmissions directives cover these six clinical conditions:
  • Heart attack
  • Heart failure
  • Pneumonia
  • Chronic obstructive pulmonary disease (COPD)
  • Elective hip or knee replacement
  • Coronary artery bypass graft (CABG)

Many clinical and demographic factors influence readmissions—too many for even the best clinicians to observe and consider without AI-based cognitive power:
  • Use of high-risk medications
  • Multiple medications for the same condition
  • More than six chronic conditions
  • Unplanned hospitalizations within the last six to 12 months
  • Low health literacy
  • Limited social interaction
  • Lower socioeconomic status
  • Discharge against medical advice

The industry data reflects the challenges in reducing and managing readmissions:
  • Nearly 20 percent of Medicare patients discharged from a hospital are readmitted within 30 days, which costs Medicare $15 billion to $18 billion per year
  • Overall, 36.1 percent of all 30-day readmissions occurred within seven days
  • Often inadequate discharge planning contributes to readmissions, with poor coordination among hospital and community clinicians and lack of community-based care
  • Medicare patients contributed to $20.1 billion in total hospital costs for potentially preventable hospitalizations
  • In 2017 2,573 hospitals were penalized for too many 30-day readmissions
  • Readmissions stays are about 50 percent longer than overall average length of acute care stay at 6.4 days (estimated mean LOS)

Patient and Healthcare Impact

Jvion has a demonstrated track record across multiple hospitals of reducing readmissions by at least 10 percent.
Consider a hospital with a readmission rate of 13 percent (1,300 readmissions per 10,000 discharges). The Jvion Cognitive Clinical Success Machine would help avoid 130 readmissions—an estimated cost savings of $1.43 million.

Akintoye, E., Briasoulis, A., Egbe, A., Dunlay, S. M., Kushwaha, S., Levine, D., . . . Weinberger, J. (2017). National Trends in Admission and In‐Hospital Mortality of Patients With Heart Failure in the United States (2001–2014). Journal of American Heart Association, 1-14. doi:https://doi.org/10.1161/JAHA.117.006955

Boccuti, C., & Casillas, G. (2017, March). Aiming for fewer hospital u-turn: The Medicare hospital readmission reduction progrma. Retrieved from Kaiser Family Foundation: http://files.kff.org/attachment/Issue-Brief-Fewer-Hospital-U-turns-The-Medicare-Hospital-Readmission-Reduction-Program

Centers for Medicare & Medicaid Service. (2018, March 26). Hospital Readmissions Reduction Program (HRRP). Retrieved from https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/HRRP/Hospital-Readmission-Reduction-Program.html

Davis, J. D., Olsen, M. A., Bommarito, K., LaRue, S., Saeed, M., Rich , M. W., & Vader, J. M. (2017). All-Payer Analysis of Heart Failure Hospitalization 30-Day Readmission: Comorbidities Matter. The American Journal of Medicine, 130(1), 93.e9-93.e28. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5482409/

Fitch, K., Engel, T., & Lau, J. (2017). The Cost of Worsening Heart Failure in the Medicare Fee For Service Population: An Actuarial Analysis. Milliman, 1-29. Retrieved from http://us.milliman.com/uploadedFiles/insight/2017/cost-bruden-worsening-heart-failure.pdf

Gheorghiade, M., Vaduganathan, M., Fonarow, G. C., & Bonow, R. O. (2013). Rehospitalization for Heart Failure. Journal of American College of Cardiology, 61(4), 391-403. Retrieved from https://www.sciencedirect.com/science/article/pii/S0735109712052965?via%3Dihub

Heidenreich , P. A., Albert , N. A., Allen, L. A., Bluemke, D. A., Butler, J., Fonarow , G. C., . . . Trogdon, J. G. (2013). Forecasting the Impact of Heart Failure in the United States. Circulation: Heart Failure, 1-14. doi:DOI: 10.1161/HHF.0b013e318291329a

Lagoeiro, A. J., Mesquita, E. T., Rabelo, L. M., & Souza Jr., C. V. (2017). Understanding Hospitalization in Patients with Heart Failure. International Journal of Cardiovascular Sciences, 30(1), 81-90. Retrieved from http://www.scielo.br/pdf/ijcs/v30n1/2359-4802-ijcs-30-01-0081.pdf

Masoud Shafazand, H. P. (2012). Patients with worsening chronic heart failure who present to a hospital emergency department require hospital care. BMC Research Notes, 5, 132. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3315737/

Masuri, A., Althouse, A., McKibben, J., Thoma, F., Mathier, M., Ramani, R., . . . Mulukutla, S. (2018). Outcomes of Heart Failure Admissions Under Observation Versus. Journal of the American Heart Association, 1-7. doi:DOI: 10.1161/JAHA.117.007944

Ogah, O. A. (2017). Heart Failure: Definition classification, and pathophysiology - a mini-review. Nigerian Journal of Cardiology, 14(1), 9. Retrieved from http://www.nigjcardiol.org/citation.asp?issn=0189-7969;year=2017;volume=14;issue=1;spage=9;epage=14;aulast=Adebayo;aid=NigJCardiol_2017_14_1_9_201913

Quaglietti, S. E., Atwood, J. E., Ackerman, L., & Froelicher, V. (2000). Management of the patient with congestive heart failure using outpatient, home, and palliative care. Progress in Cardiovascular Diseases, 43(3), 259-274. Retrieved from http://www.cardiology.org/recentpapers/susiechfc.pdf

Ziaeian, B., & Gregg, F. C. (2016). The Prevention of Hospital Readmissions in Heart Failure. Progress in Cardiovascular Disease, 58(4), 379-385. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4783289/

Jvion's Thinking Machine

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Cognitive Machine in Action

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