Transforming Nursing

Quality in the Present and in the Future

The Impact of Artificial Intelligence on Quality and Safety

Michelle S. Lee, BA1, Matthew M. Grabowski, MD1 , Ghaith Habboub, MD1, and Thomas E. Mroz, MD1

Abstract As exponential expansion of computing capacity converges with unsustainable health care spending, a hopeful opportunity has emerged: the use of artificial intelligence to enhance health care quality and safety. These computer-based algorithms can perform the intricate and extremely complex mathematical operations of classification or regression on immense amounts of data to detect intricate and potentially previously unknown patterns in that data, with the end result of creating predictive models that can be utilized in clinical practice. Such models are designed to distinguish relevant from irrelevant data regarding a particular patient; choose appropriate perioperative care, intervention or surgery; predict cost of care and reimbursement; and predict future outcomes on a variety of anchored measures. If and when one is brought to fruition, an artificial intelligence platform could serve as the first legitimate clinical decision-making tool in spine care, delivering on the value equation while serving as a source for improving physician performance and promoting appropriate, efficient care in this era of financial uncertainty in health care.

Keywords artificial intelligence, machine learning, spine predictive modeling, spine quality, spine safety

In the most recent version of the projections of the Office of the

Actuary in the Centers for Medicare & Medicaid Services,

national health spending growth is expected to average 5.5% per year for 2017-2026 in the United States (US): approxi-

mately 1.0% higher than projected gross domestic product

(GDP) growth.1 This results in an increase in the health-

related costs as a percentage of GDP from 17.9% in 2016, to

nearly 20% by 2026, reaching a total of $5.7 trillion by 2026.1

When looking granularly at specific medical fields, a signifi-

cant portion of the rising costs of health care in the US relates to

the diagnosis and treatment of spinal pathology. It is estimated

that 12% to 30% of US adults have an active back problem with

approximately 6% having made a visit to a physician for these

conditions at one point in their lives, costing upward of

$100 billion to the system each year.2,3 Specifically with regard

to spine surgery, fusions and laminectomies were the third and

fifth most commonly performed surgical procedures in the

United States in 2015, respectively.4 Given the rising costs

associated with spine surgery and an aging population, it

becomes increasingly clear that the current trajectory is not

sustainable, and further scrutiny will be placed on the field in

assessing the effectiveness, efficiency, and safety of care deliv-

ered. As more healthcare systems invest in healthcare analytics

and “big data” (large, complex datasets such as those found in

electronic medical records), the opportunity arises to employ

predictive analytics via machine learning (ML)/artificial intel-

ligence (AI) approaches to improve quality, reduce waste and

error, and minimize cost.5,6

Recent developments in the technologies related to health-

care data collection and analytics have led to a rapid rise in the

application of AI within health-related fields. One such appli-

cation is ML, a branch of AI that involves the construction and

application of statistical algorithms that continuously learn and

make observations from existing data, and then create a pre-

dictive model based on that data.7 With advances in computer

processing capability, data storage, and networking, these

computer-based algorithms can perform the intricate and

extremely complex mathematical operations of classification

or regression (specifically nonlinear regression) on immense

amounts of data to detect intricate and potentially previously

1 Cleveland Clinic Foundation, Cleveland, OH, USA

Corresponding Author:

Thomas E. Mroz, Director, Center for Spine Health, Spine Research Lab,

Departments of Orthopaedic and Neurological Surgery, Cleveland Clinic,

9500 Euclid Avenue, S-40, Cleveland, OH 44195, USA.

Email: [email protected]

Global Spine Journal 2020, Vol. 10(1S) 99S-103S

ª The Author(s) 2020 Article reuse guidelines:

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unknown patterns in that data.8 ML algorithms have been able

to analyze complex and large volumes of electronic medical

record data to produce predictions for a wide range of clinical

problems.9 For example, Rajkomar et al9 demonstrated that

ML models outperformed traditional, clinically used models

in predicting mortality, unexpected readmission, and increased

length of stay (LOS) in a study cohort of all admissions in 2

major hospitals from 2009 to 2016. Various investigators have

been developing image analysis methods using ML algorithms

that have shown promising results in fields such as dermatol-

ogy, radiology, and ophthalmology. For example, Esteva et al10

have trained ML algorithms to classify skin cancer with a level

of competence comparable to dermatologists. These early

examples provide insight into early contemporary use of AI

in medicine and provide a view of technology that may trans-

form the medical field over the decades to come.

While not the first medical field to adopt an “AI approach”

to problem solving, the spine surgery field has recently seen an

outpouring of publications related to research in this area. An

initial topic of focus by researchers was related to the cost of

spine care, as there has been heightened emphasis on moving to

a value-based (quality/cost) health care market. For example,

as Medicare payments are standardized by procedures per-

formed regardless of hospital LOS, ML systems have been

designed with the ability to accurately predict spine surgery-

related LOS, discharge to nonhome facility, and early

unplanned readmissions using only presurgical or predischarge

variables.11-14 These models can help identify/target certain

high-risk patients and the variables that contribute to that risk

status, allowing hospitals to allocate specific clinical and social

resources to reduce costly LOS and readmissions. This can help

to maximize efficiency of care delivered, while also keeping

constant or even increasing the quality of care delivered.

As spinal surgery has evolved with an explosion of new

techniques and technologies in recent decades, there still

remains a lack of quality, high-level evidence to support much

of the spine care rendered in the US, especially with the cost

associated with many of the treatments and devices. As there

are numerous surgical treatments in spine surgery that do not

easily lend themselves to traditional randomized controlled

trials (due to either cost or ethical considerations, among other

reasons), an opportunity arises that is ripe for solutions derived

from ML approaches. Multiple clinical registries are being

collected that contain large quantities of high-quality, spine

health care data, such as the 1000-patient Spinal Laminectomy

versus Instrumented Pedicle Screw (SLIP) II study.15-17 These

registries contain demographics, surgery-related variables,

patient-reported and complication outcome measures, and

notably, they even contain digital imaging with metadata.

Leveraging of these vast data repositories can help develop

predictive algorithms that are able to incorporate the full range

of variables (including complex imaging) in order to guide

treatment recommendations.

Because of this lack of high-level evidence, there remains

much heterogeneity in the current surgical treatment of

spinal disorders, with significant clinical and economic

implications.18-20 For instance, national surveys of US spine

surgeons conducted by Mroz et al21 found 69% disagreement

for recurrent lumbar disk herniation, while another study

demonstrated 75% disagreement among surgeons on the

approach to treat patients with lower back pain,22 implying that

2 similar patients with the same pathology could receive

entirely different care. Furthermore, a cost analysis based on

the results of the national survey mentioned above revealed that

there is also a variation in costs based on spine surgeon speci-

alty, practice type, surgical volume and geographical loca-

tion.23 Recent ML/AI approaches to this problem have been

published that attempt to assist surgeons’ decisions with pre-

dictions of patient outcomes. Utilizing data from repositories

created from AOSpine prospective, multicenter studies, Merali

et al17 developed a supervised ML model that accurately pre-

dicts a positive outcome on an individual patient after surgery

for degenerative cervical myelopathy, with an average area

under the curve of 0.70, classification accuracy of 77%, and

sensitivity of 78% on an independent testing cohort. Shah

et al24 were able to build an ML model that predicts probability

of failure of nonoperative management in spinal epidural

abscess, while Karhade et al25 successfully developed an ML

algorithm that predicts in-hospital and 90-post discharge mor-

tality in this patient group. The same group was able to predict

short-term postoperative mortality in individual patients with

spinal metastatic disease with an ML model, aiding in decision-

making and informed discussions with the patient regarding

surgical intervention this challenging patient population.26 All

of these previously mentioned studies have now published their

prognostic tools in an open-access, digital interface to be inte-

grated into practice, supporting clinicians in developing treat-

ment plans that are more standardized across the world.

Along with prediction of positive patient outcomes, clini-

cian researchers have also used AI/ML to forecast negative

outcomes as well, as recent publications have explored the

likelihood of complications from spine surgery. In multiple

articles, the same group led by Cho et al utilized an artificial

neural network-based ML algorithm to predict surgical com-

plications in patients undergoing elective anterior cervical dis-

cectomy and fusion, posterior lumbar fusion, and adult spinal

deformity surgeries. Their models were able to specifically

predict the risk of cardiac-related, wound-related, venous

thromboembolism–related, and mortality in these patients, out-

performing the American Society of Anesthesiologists Physical

Status Classification scoring in predicting individual risk prog-

nosis.27,28 Another publication by Sheer et al29 describes their

method to create a ML model that successfully predicts major

intraoperative/perioperative complications following adult

spinal deformity surgery with an accuracy of 87%. Utilizing

large databases of patient information, Han et al30 were able to

analyze over 1 000 000 patients that had previously undergone

spine surgery and developed multiple ML predictive models

that identify risk factors for postoperative complications. Kar-

hade et al24 were even able to predict prolonged opioid pre-

scription after surgery for lumbar disc herniation in an ML

algorithm. These surgery- and patient-specific models can help

100S Global Spine Journal 10(1S)

to aid in surgical planning, as well as patient counseling and

shared decision making. If these models identify modifiable

risk factors in the preoperative setting of a nonurgent surgery,

time and effort could be dedicated to improved medical man-

agement of that comorbidity prior to surgery, in effect reducing

the risk of complications and increasing the probability of a

good outcome.

In deciding if a patient is indicated for surgery, one area

where a surgeon’s subjectivity may still reign supreme is

review of the spine imaging. Utilizing classification techniques

from radiology literature, new research is revealing the applic-

ability of AI and ML algorithms to the analysis of spine ima-

ging. One technique involves the use of ML models utilizing

natural language processing to distinguish specific words and

phrases from unstructured radiology reports in order to classify

patients by imaging findings, as Tan et al31 were able to do in a

cohort of patients with low back pain. However, in more recent

publications, other groups were able to utilize the imaging itself

to detect and classify a variety of pathologies. Hopkins et al32

were able to predict both the diagnosis of cervical spondylotic

myelopathy and its severity with high sensitivity and specifi-

city (90.25% and 85.05%, respectively), utilizing magnetic

resonance imaging alone in an artificial neural network model.

Further, work has been done to develop ML models in the

detection and grading of lumbar spinal stenosis33 and fracture

detection and classification with various types of imaging mod-

alities.34 AI/ML imaging analysis can even assist real time in

the outpatient clinic, where Sharif Bidabadi et al35 were able to

accurately identify foot drop of an L5 origin and classify

patients into various recover stages with an 85% accuracy.

While there is much work to be done, this initial work which

was all published in the past year, shows the feasibility of using

AI/ML-based approaches to analyzing spine imaging.

Common themes among large institutions and large spine

centers are tighter financial margins, less resources, and heigh-

tened payer scrutiny on indications, outcomes, and postproce-

dural treatments. This collectively creates real strain on the

departmental workforce (ie, secretaries, advanced practice pro-

viders, physicians). An AI platform that successfully predicts

patient and surgeon performance from financial, outcome, and

electronic medical record databases across an entire book of

business stands to provide the leverage to homogenize outcome

and cost. This, in turn, positions said organization optimally for

contract negotiations and population health initiatives. Further-

more, a fully integrated AI platform can also automate much of

what currently strains department assets. Postsurgical checks,

ordering medications and imaging, patient reminders, and

scheduling follow-up visits, are all some examples of how such

a platform can enhance overall spine center efficiencies and

performance, patient satisfaction (eg, more automated touch

points), and employee engagement.

Challenges Ahead

Even though current research described above highlights the

promise and potential of AI in spine surgery, the field as a

whole still face many challenges. First, in order to create an

AI-driven decision platform, very large and appropriately

labeled data sets are required, which the majority of centers

in the United States still lack. This becomes even more difficult

with imaging-based analysis. Second, some ML models require

manual labeling of the data for classification and learning to

occur. This presents a clear challenge in the analysis of spine

surgery pathologies, where there is still widespread disagree-

ment about what constitutes normal versus abnormal with

regard to certain exam/imaging findings, and subsequently the

appropriate treatment(s). This can be circumvented by allowing

the model itself to do the analysis and classification, such as is

the case with unsupervised algorithms. Given the vast quantity

of data analyzed, this can reveal links between variables that

experts would not have otherwise expected. However, it is

difficult to backtrack and get precise information regarding the

specifics of the data sorting in these types of models. And with

poor quality or quantity of data to learn from, the model may

make erroneous associations and/or can be “overfitted” to the

training dataset, producing a lack of external validity. Further-

more, many ML algorithms thus far are typically trained and

validated internally within one institution. Further work needs

to be carried out to examine if a predictive model is transferable

from one site to another, and what implications this holds as a

“live” ML model undergoes continuous calibration and evolu-

tion based on new sets of data.

As exponential expansion of computing capacity converges

with unsustainable healthcare spending, a hopeful opportunity

has emerged: the use of AI to enhance healthcare quality and

safety. AI-based, ML approaches to spinal pathologies are

already distinguishing relevant from irrelevant data regarding

a particular patient, assisting with appropriate hospital-based

care, interventions or even surgeries, predicting cost of care,

and predicting future outcomes on a variety of anchored mea-

sures. While many shortcomings still exist as the technology is

in early development, extrapolating from today’s progress and

fully implemented into the healthcare system, AI could help

solve a number of problems in spine surgery by improving

outcomes, minimizing cost, standardizing care for a given

pathology, and driving efficiencies within a spine service line

in large centers. These types of approaches could deliver on the

value equation while serving as a resource for improving phy-

sician performance and promoting appropriate, efficient care in

this era of financial uncertainty in health care.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to

the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for

the research, authorship, and/or publication of this article: This sup-

plement was supported by funding from AO Spine North America.

ORCID iD

Matthew M. Grabowski https://orcid.org/0000-0001-8550-0124

Lee et al 101S

References

1. Cuckler GA, Sisko AM, Poisal JA, et al. National Health Expen-

diture Projections, 2017-26: despite uncertainty, fundamentals

primarily drive spending growth. Health Aff (Millwood). 2018;

37:482-492. doi:10.1377/hlthaff.2017.1655

2. McGirt MJ, Resnick D, Edwards N, Angevine P, Mroz T, Fehl-

ings M. Background to understanding value-based surgical spine

care. Spine (Phila Pa 1976). 2014;39(22 suppl 1):S51-S52.

doi:10.1097/BRS.0000000000000544

3. Davis MA, Onega T, Weeks WB, Lurie JD. Where the United

States spends its spine dollars: expenditures on different ambula-

tory services for the management of back and neck conditions.

Spine (Phila Pa 1976). 2012;37:1693-1701. doi:10.1097/BRS.

0b013e3182541f45

4. Agency for Healthcare Research and Quality. Healthcare Cost

and Utilization Project (HCUP). https://www.ahrq.gov/data/

hcup/index.html. Published April 2, 2015. Accessed February

27, 2019.

5. Wang Y, Kung L, Wang WYC, Cegielski CG. An integrated big

data analytics-enabled transformation model: application to

health care. Inf Manag. 2018;55:64-79. doi:10.1016/j.im.2017.

04.001

6. Ristevski B, Chen M. Big data analytics in medicine and health-

care. J Integr Bioinform. 2018;15:20170030. doi:10.1515/jib-

2017-0030

7. Goodfellow I, Bengio Y, Courville A. Deep Learning.

Cambridge, MA: MIT Press; 2017.

8. Chollet F. Deep Learning With Python. Shelter Island, NY:

Manning; 2018.

9. Rajkomar A, Oren E, Chen K, et al. Scalable and accurate deep

learning for electronic health records. NPJ Digit Med. 2018;1:18.

doi:10.1038/s41746-018-0029-1

10. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level clas-

sification of skin cancer with deep neural networks. Nature. 2017;

542:115-118. doi:10.1038/nature21056

11. Ogink PT, Karhade AV, Thio QCBS, et al. Predicting discharge

placement after elective surgery for lumbar spinal stenosis using

machine learning methods. Eur Spine J. 2019;28:1433-1440. doi:

10.1007/s00586-019-05928-z

12. Goyal A, Ngufor C, Kerezoudis P, Mccutcheon B, Storlie C,

Bydon M. Can machine learning algorithms accurately predict

discharge to nonhome facility and early unplanned readmissions

following spinal fusion? Analysis of a national surgical registry

[published online June 7, 2019]. J Neurosurg Spine. doi:10.3171/

2019.3.spine181367

13. Stopa BM, Robertson FC, Karhade AV, et al. Predicting nonrou-

tine discharge after elective spine surgery: external validation of

machine learning algorithms [published online July 26, 2019].

J Neurosurg Spine. doi:10.3171/2019.5.spine1987

14. Kalagara S, Eltorai AEM, Durand WM, Depasse JM, Daniels AH.

Machine learning modeling for predicting hospital readmission

following lumbar laminectomy. J Neurosurg Spine. 2019;30:

344-352. doi:10.3171/2018.8.spine1869

15. Ghogawala Z, Dunbar MR, Essa I. Lumbar spondylolisthesis:

modern registries and the development of artificial intelligence.

J Neurosurg Spine. 2019;30:729-735. doi:10.3171/2019.2.

spine18751

16. Ghogawala Z, Dunbar M, Essa I. Artificial intelligence for the

treatment of lumbar spondylolisthesis. Neurosurg Clin N Am.

2019;30:383-389.

17. Merali ZG, Witiw CD, Badhiwala JH, Wilson JR, Fehlings MG.

Using a machine learning approach to predict outcome after sur-

gery for degenerative cervical myelopathy. PLoS One. 2019;14:

e0215133. doi:10.1371/journal.pone.0215133

18. Irwin ZN, Hilibrand A, Gustavel M, et al. Variation in surgical

decision making for degenerative spinal disorders. Part I: lumbar

spine. Spine (Phila Pa 1976). 2005;30:2208-2213. https://jour

nals.lww.com/spinejournal/Fulltext/2005/10010/Variation_in_

Surgical_Decision_Making_for.14.aspx.

19. Arnold PM, Brodke DS, Rampersaud YR, et al; Spine Trauma

Study Group. Differences between neurosurgeons and orthopedic

surgeons in classifying cervical dislocation injuries and making

assessment and treatment decisions: a multicenter reliability

study. Am J Orthop (Belle Mead NJ). 2009;38:E156-E161.

http://europepmc.org/abstract/MED/20011745.

20. Rampersaud YR, Fisher C, Wilsey J, et al. Agreement between

orthopedic surgeons and neurosurgeons regarding a new algo-

rithm for the treatment of thoracolumbar injuries: a multicenter

reliability study. J Spinal Disord Tech. 2006;19:477-482.

doi:10.1097/01.bsd.0000211237.14211.21

21. Mroz TE, Lubelski D, Williams SK, et al. Differences in the

surgical treatment of recurrent lumbar disc herniation among

spine surgeons in the United States. Spine J. 2014;14:

2334-2343. doi:10.1016/j.spinee.2014.01.037

22. Lubelski D, Williams SK, O’Rourke C, et al. Differences in the

surgical treatment of lower back pain among spine surgeons in

the United States. Spine (Phila Pa 1976). 2016;41:978-986.

doi:0.1097/BRS.0000000000001396

23. Alvin MD, Lubelski D, Alam R, et al. Spine surgeon treatment

variability: the impact on costs. Global Spine J. 2018;8:498-506.

doi:10.1177/2192568217739610

24. Shah AA, Karhade AV, Bono CM, Harris MB, Nelson SB,

Schwab JH. Development of a machine learning algorithm for

prediction of failure of nonoperative management in spinal

epidural abscess [published online May 4, 2019]. Spine J.

doi:10.1016/j.spinee.2019.04.022

25. Karhade AV, Shah AA, Bono CM, et al. Development of machine

learning algorithms for prediction of mortality in spinal epidural

abscess [published online June 27, 2019]. Spine J. doi:10.1016/j.

spinee.2019.06.024

26. Karhade AV, Thio QCBS, Ogink PT, et al. Development of

machine learning algorithms for prediction of 30-day mortality

after surgery for spinal metastasis. Neurosurgery. 2019;85:

E83-E91.

27. Kim JS, Arvind V, Oermann EK, et al. Predicting surgical com-

plications in patients undergoing elective adult spinal deformity

procedures using machine learning. Spine Deform. 2018;6:

762-770. doi:10.1016/j.jspd.2018.03.003

28. Arvind V, Kim JS, Oermann EK, Kaji D, Cho SK. Predicting

surgical complications in adult patients undergoing anterior

102S Global Spine Journal 10(1S)

cervical discectomy and fusion using machine learning. Neuro-

spine. 2018;15:329-337. doi:10.14245/ns.1836248.124

29. Scheer JK, Smith JS, Schwab F, et al; International Spine Study

Group. Development of a preoperative predictive model for major

complications following adult spinal deformity surgery. J Neuro-

surg Spine. 2017;26:736-743. doi:10.3171/2016.10.spine16197

30. Han SS, Azad TD, Suarez PA, Ratliff JK. A machine learning

approach for predictive models of adverse events following spine

surgery [published online June 20, 2019]. Spine J. doi:10.1016/j.

spinee.2019.06.018

31. Tan WK, Hassanpour S, Heagerty PJ, et al. Comparison of natural

language processing rules-based and machine-learning systems to

identify lumbar spine imaging findings related to low back pain.

Acad Radiol. 2018;25:1422-1432. doi:10.1016/j.acra.2018.03.008

32. Hopkins BS, Weber KA 2nd, Kesavabhotla K, Paliwal M, Can-

trell DR, Smith ZA. Machine learning for the prediction of

cervical spondylotic myelopathy: a post hoc pilot study of

28 participants. World Neurosurg. 2019;127:e436-e442.

doi:10.1016/j.wneu.2019.03.165

33. Huber FA, Stutz S, de Martini IV, et al. Qualitative versus quan-

titative lumbar spinal stenosis grading by machine learning sup-

ported texture analysis—experience from the LSOS study cohort.

Eur J Radiol. 2019;114:45-50. doi:10.1016/j.ejrad.2019.02.023

34. Langerhuizen DWG, Janssen SJ, Mallee WH, et al. What are the

applications and limitations of artificial intelligence for fracture

detection and classification in orthopaedic trauma imaging? A

systematic review [published online June 21, 2019]. Clin Orthop

Relat Res. doi:10.1097/corr.0000000000000848

35. Sharif Bidabadi S, Tan T, Murray I, Lee G. Tracking foot drop

recovery following lumbar-spine surgery, applying multiclass gait

classification using machine learning techniques. Sensors. 2019;

19:E2542. doi:10.3390/s19112542

Lee et al 103S

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