====== Neurosurgery Guidelines Development ======

see [[Guidelines Development]].
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[[Neurosurgery guidelines]] are developed based on evaluating the most [[up-to-date]] evidence. However, the current approach incompletely considers or altogether avoids [[cost-effectiveness]] when formulating these [[guidelines]]
((Stein SC. Cost-effectiveness research in neurosurgery: we can and we must. Neurosurgery. 2018;83(5):871-878.)).

[[Evidence-based guidelines]] (EBGs) are an early-phase model of a [[Clinical decision support system]] (CDSS). While they do aid the physician by presenting scientifically based evidence during the [[decision-making]] process
((Djulbegovic B, Guyatt GH. Progress in evidence-based medicine: a quarter century on. Lancet. 2017;390(10092):415-423.))
((Vachhrajani S, Kulkarni AV, Kestle JRW. Clinical practice guidelines. J Neurosurg Pediatrics. 2009;3(4):249-256.)).

Neurosurgical [[Evidence-based guidelines]] (EBGs) have been developed to address the problem of [[variance]] in neurosurgery
((Fehlings MG, Nater A. Development and implementation of guidelines in neurosurgery. Neurosurgery clinics of North America. 2015;26(2):271-282, x.))
((Esene IN, Baeesa SS, Ammar A. Evidence-based neurosurgery. Basic concepts for the appraisal and application of scientific information to patient care (Part II). Neurosciences (Riyadh). 2016;21(3):198-206.))
((Prabhu VC. Evidence-based clinical practice guidelines in neurosurgery. World Neurosurg. 2016;91:611-613.))

In neurosurgery, EBGs have been met with scrutiny, as 
((Vachhrajani S, Kulkarni AV, Kestle JRW. Clinical practice guidelines. J Neurosurg Pediatrics. 2009;3(4):249-256.))
neurosurgery-specific EBGs are rare and often formulated without neurosurgeon input
((Steudel WI, Schwerdtfeger K. Guidelines for guidelines. Acta Neurochir Suppl. 2001;78:217- 223.)).

[[Variance]] between [[provider]]s in the neurosurgical field leads to inefficiencies and poor patient [[outcome]]s. Evidence based [[guideline]]s (EBGs) have been developed as a means of pooling the body of [[evidence]] in the [[literature]] to provide [[clinician]]s with the most comprehensive [[data]]-driven [[recommendation]]s. However, these EBGs are not being implemented well into the clinician [[workflow]], and therefore clinicians are left to make [[decision]]s with incomplete [[information]]. Equally underutilized are [[electronic health record]]s (EHRs), which house enormous health [[data]], but which have failed to capitalize on the power of that '[[big data]].' Early attempts at EBGs were rigid and not adaptive, but with the current advances in data [[informatics]] and [[machine learning algorithm]]s, it is now possible to integrate 'big data' and rapid data processing into clinical decision support tools. As we strive towards variance reduction in healthcare, the integration of 'big data' and EBGs for decision-making are key. 

Stopa et al., proposed that EHRs are an ideal platform for integrating EBGs into the clinician workflow. With this model, it will be possible to build EBGs into the EHR software, to continuously update and optimize EBGs based on the flow of patient data into the EHR, and to present data-driven clinical decision support at the point of care. Variance reduction in neurosurgery through the integration of evidence-based decision support in [[electronic health record]]s will lead to improved patient [[safety]], reduction of medical [[error]]s, maximization of available data, and enhanced decision-making power for clinicians
((Stopa BM, Yan SC, Dasenbrock HH, Kim DH, Gormley WB. Variance reduction in
neurosurgical practice: The case for analytics driven decision support in the era
of Big Data. World Neurosurg. 2019 Feb 21. pii: S1878-8750(19)30414-0. doi:
10.1016/j.wneu.2019.01.292. [Epub ahead of print] PubMed PMID: 30797905.
)).
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Use of the term rapidly expanded to include a previously described approach that emphasized the use of evidence in the design of guidelines and policies that apply to populations ("evidence-based practice policies").

It has subsequently spread to describe an approach to decision making that is used at virtually every level of health care as well as other fields, yielding the broader term evidence-based practice.
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Management of delirium, traumatic brain injury; and intracranial hemorrhage.
brain stimulation for obsessive compulsive disorder; and surgery for Low-grade glioma,
ischemia and hemispheric stroke, glioblastoma, and brain metastases. 

In the area of indications for surgical interventions, there are EBGs for deep
surgery, including the diagnosis and treatment of lumbar disc herniation,
spondylolisthesis, and degenerative lumbar spondylolisthesis.
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In the current boom of [[technology]], the combination of ‘[[big data]]’ and [[artificial intelligence]] 
((Senders JT, Zaki MM, Karhade AV, et al. An introduction and overview of machine learning in neurosurgical care. Acta Neurochir (Wien). 2018;160(1):29-38.))
((Senders JT, Staples PC, Karhade AV, et al. Machine learning and neurosurgical outcome prediction: A systematic review. World Neurosurg. 2018;109:476-486.))
((Senders JT, Arnaout O, Karhade AV, et al. Natural and artificial intelligence in neurosurgery: A systematic review. Neurosurgery. 2018;83(2):181-192.))
creates the opportunity to comprehensively integrate [[evidence based]] [[decision making]] into the [[healthcare system]]. These factors are converging during a time when we are seeing significant increases in [[Electronic Health Record]] (EHR) adoption following the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009, from 3.2% among eligible hospitals before the Act to 14.2% after
((Adler-Milstein J, Jha AK. HITECH Act drove large gains in hospital electronic health record adoption. Health Aff. 2017;36(8):1416-1422.
)).