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Automated Medical Algorithms Issues for
AnesthesiologyGareth S. Kantor, MB ChB,
FRCP(C) John R. Svirbely, MD M.G. Sriram,
PhD. Department of Anesthesiology, University
Hospitals of Cleveland McCullough-Hyde Memorial
Hospital, Oxford, Ohio HelloBrain Corp, Santa
Clara, California
31.01 Preoperative Patient Classification
Scales 31.02 Sedation Scales and Scores 31.03
Tracheal Intubation and Mask Ventilation 31.04
Risk of Complications after Anesthesia and
Surgery 31.05 Recovery Following Anesthesia or
Sedation 31.06 Guidelines for Conscious
Sedation 31.07 Malignant Hyperthermia Clinical
Grading Scale 31.08 Criteria for Inadequate
Level of Anesthesia 31.09 Cost of Anesthetic
Vapor 31.10 Anxiety Associated with Anesthesia
and Surgery 31.11 Patient Satisfaction with
Anesthesia Care 31.12 Error Prevention in
Anesthesiology 31.13 Postoperative Nausea and
Vomiting (PONV) 31.14 Sedation in Critical
Care 31.15 Perioperative Management of Sickle
Cell Disease 31.16 Postoperative Urinary
Retention 31.17 Post-Dural Puncture Headache
(PDPH) 31.18 Perioperative Pulmonary Aspiration
1.      Coding look-up tables 2.       
Comparison with normal population
standards 3.        Data conversion 4.       
Decision rules triaging 5.        Decision
trees flow diagrams 6.        Diagnostic
criteria 7.        Diaries symptom
tracking 8.        Functional state
description 9.        Grading and scaling 10.
Probability statistical analysis 11.     
Prognostic scores 12.      Questionnaires 13.     
Risk determination 14.      Simple
classification 15.      Simple formulas 16.     
Therapeutic indications contraindications
ABSTRACT   Introduction The use of medical
algorithms can enhance clinical judgment1 and
favorably influence patient outcome2.
Computerized algorithms can provide timely
clinical decision support, improve adherence to
evidence-based guidelines, and be a resource for
education and research. The Medical Algorithm
Project (MEDAL) The Medical Algorithm Project3
(http//www.medal.org) is a free, web-based,
compendium of over 2,650 algorithms, divided into
44 categories, that range from simple
calculations, such as body mass index, to
complicated outcome prediction formulas and
clinical decision rules. There are more than 50
anesthesiology algorithms, derived from
peer-reviewed literature, including risk
prediction models for postoperative nausea and
cardiac complications, functional status,
sedation and anesthesia recovery scores,
predictors of difficult tracheal intubation,
equipment and machine checklists, and patient
satisfaction measures. Additional algorithms
relevant to anesthesiology are found in sections
devoted to critical care (severity-of-illness and
organ dysfunction scores, outcome prediction
models) and other clinical and non-clinical
specialties, as well as to specific organ systems
(respiratory, cardiovascular, etc.), laboratory
calculations, unit conversions and decision
analysis. Algorithms are presented in a
Microsoft Excel spreadsheet format that is
designed for future automation. Each one is
represented as a) overview of documentation, b)
unit conversion, c) entry of data, d)
intermediate calculations, e) interpretation, and
f) data tables. This representation makes
explicit the logic, rules and thresholds for
variables, and generates explicit and executable
instructions. In time-pressured environments
like the operating room, medical algorithms
should ideally be integrated into "point of care"
decision support systems, such as handheld
computers and anesthesia workstations. Algorithms
could be triggered by data from monitors or
electronic record systems. MEDAL provides a
source of material for development of such
integrated decision support tools. Potential
Benefits of Algorithm Automation MEDAL brings
together at one site algorithms from diverse
sources, thus addressing one of the primary
barriers to the practice of evidence-based
medicine4. The use of more complex algorithms,
such as those involving logistic regression
analysis, is facilitated. Automation of medical
algorithms may decrease medical errors through
reduction of data entry and calculation mistakes,
elimination of incorrect recall of algorithm
details, and by providing documentation that
facilitates proper algorithm selection and
application. Algorithms can be used in
preoperative risk assessment and triage,
automation of routine patient questioning, and
decision support for preoperative testing, which
may improve the effectiveness and decrease the
cost of preoperative evaluation. MEDAL provides a
centralized algorithm repository for educators
and researchers, especially those interested in
anesthesia clinical outcome studies. Outcome
measurement algorithms may facilitate
departmental performance improvement programs.
Risk indices enable comparison between groups
("risk-adjustment") and can help clinicians
understand the elements of perioperative
risk5. Conclusions and Future Directions MEDAL
is open source software, with benefits for a
global audience. MEDAL may facilitate the
translation of knowledge into available and
effective perioperative clinical decision support
applications. A larger collaborative effort is
needed, so that algorithms are identified,
validated, updated, appropriately classified and
structured, and transferred into a web-accessible
database format. We encourage programmers to
incorporate algorithms into perioperative
clinical information systems. We hope clinicians
will contribute algorithms and make use of them
in practice, guided of course by appropriate
clinical judgement and experience. References
1. JAMA 2000 28479-84.2. JAMA 1998
2801360-61.3. Proc AMIA Symp 1999, p. 11724.
Anesth Analg 2001 92787-94.5. Anesthesiology
2001 94191.
Table 1 Types of Algorithms Used in Healthcare
Table 2 Anesthesia Algorithms in MEDAL

1. Misuse of the output of the algorithm
e.g. use for individual when intended for
populations e.g. use outside of specified
population. 2. Failure to use an algorithm when
one is appropriate (error of omission) 3.
Selection of an algorithm that is wrong for the
patient or situation 4. Opting for a simple
algorithm when a more complex one would be
better 5. Making an error in remembering the
algorithm (error of recall) 6. Making an error
during calculation or execution of the
algorithm 7. Failure to question the output of
the algorithm
Figure 2 Excel Spreadsheet to Calculate Risk of
Postoperative Nausea Vomiting (Sinclair)
Table 3 Errors Associated with Use of Medical
Algorithms
Figure 1 Excel Spreadsheet to Calculate Cardiac
Anesthesia Risk Evaluation (CARE) Score
1. Overview and reference to documentation 2.
Unit conversion 3. Data entry 4. Intermediate
calculations (variable) 5. Interpretation 6.
Data tables
Table 4 Component Sequencing in MEDAL Algorithms
(see Figures 1 and 2)

• Release 8.0
• Multiple collaborators
• gt 3000 algorithms
• Free download
• Algorithms MS-Excel 95
• Documentation MS-Word 95
• gt300 visitors/day
• Spanish language mirror site
• Internet discussion group
• Plans for software implementation

Table 5 Current MEDAL Project Status
Figure 3 Handheld Computer-Based Algorithm
Implementation
Figure 4 The Medical Algorithms Project (MEDAL)
Web Site - http//www.medal.org