Section 4.4 Algorithms Tables

Table 1. Potential Benefits of Algorithms

Benefit

Explanation

Facilitate clinical decision making Clinicians must regularly sort through hundreds of research articles, evaluate the quality, integrate the findings into a coherent model, and incorporate this into their practices. Algorithms, appropriately developed and regularly updated, aid clinicians in this task.
Reduce clinically inappropriate or cost-inefficient variation in clinical practice patterns Research has documented large inconsistencies in the rate in which specific procedures are performed by physicians.10-14 Algorithms can help clinicians reduce the magnitude of this variation and improve quality of treatment.
Provide consistent treatment across different environments With shorter inpatient stays, clinicians are unlikely to know at discharge whether the treatment selected is the best for the patient. Algorithms provide a basis for developing consistent medication plans to communicate treatments across different treatment venues and practitioners.
Individualize treatment One treatment is not best for all patients. By incorporating the concept of different treatment paths depending on individual response (symptoms, functioning, and side effects), algorithms inform treatment decision making to achieve, if possible, full remission.
Increase cost-efficiency of treatment Costs may decrease if the point of treatment shifts from efficiency of emergency room and hospitals. Indirect costs are likely to decrease as reflected in a faster return to work and a positive impact for nonmedication treatments especially if treatment response is more complete15.
Make clinical decisions explicit Algorithms enable clinicians to identify the components and pathways of their clinical judgments, which makes clinical decision making explicit.16 This facilitates communication among physicians, which can enhance treatments.
Provide a metric to compare patient progress Algorithms that use patient outcome as basis for recommending key treatment decisions enable clinicians to compare the progress of treatment.
Provide a metric for evaluating when and whether to adopt new medications New psychotropic agents may have equal or greater efficacy and be better tolerated, safer in overdose, or effective for patients failing to respond to other agents. Algorithms can define empirically where, in the sequence of steps, the new agent may afford the most clinical benefit, thus informing physicians in their use of new medications.
Provide a framework for defining cost of treatment As a framework for clinical decision making, algorithms are a means to document the costs associated with care, which allows mental health systems to delineate the costs associated with specific treatment interventions and to link costs with patient outcomes.

Table 2. Potential Risks Associated With Algorithms

Risk

Explanation

Insufficient evidence Poorly developed guidelines (i.e., those with insufficient reliance on empirical evidence) may lead to poor quality of care and inferior clinical outcomes.
Biased opinions Guidelines developed by consensus panels may not always reflect a broader consensus of experts.
Increased cost and utilization of services Algorithms may increase costs, with or without increasing benefits18.
Substitute for clinical judgment If an algorithm is too rigid and inflexible, clinicians may not be able to use appropriate expertise and judgment in making decisions in the best interest of the individual patient.
Poor standard of care Ill-conceived guidelines may render poor treatment care outcomes.
Inappropriate use by administrators Administrators may inappropriately assume that algorithms can be used by inadequately educated and trained clinicians.
Tort liability Malpractice attorneys may use deviation from algorithms as evidence in malpractice cases.
aAdapted from reference 17.

Excerpted from:

Rush, A. John et al, Medication Treatment for the Severely and Persistently Mentally Ill: The Texas Medication Algorithm Project. The Journal of Clinical Psychiatry 60;5:284-291, May 1999.