December 2022 – Clinical Informatics

A 34-year-old man with a family history of premature cardiac disease and ankle xanthoma presented to his primary care provider’s office for annual exam. A glance at the patient’s chart in the electronic health record showed a Best Practice Advisory (BPA) alert. Patient’s LDL-C level was 195 mg/dL.

Figure 1

Which of the following is the best point to implement this BPA alarm?

  • When the patient leaves the office.
  • When the patient is being roomed.
  • When the patient checks in for their appointment.
  • When the provider is seeing the patient.

The correct answer is ...

When the provider is seeing the patient.

With a prevalence ranging from 0.25% (1:400) to 0.52% (1:192) depending on ethnicity. Many patients with familial hypercholesterolemia (FH) are unaware of their diagnosis and consequently miss proper medical treatment. A provider can establish the diagnosis based on history, physical examination, and simple laboratory tests. Applying Dutch criteria, this patient presented with a family history of premature CVD (1 point), tendon xanthoma (6 points), and elevated LDL-C in the range of 190-249 mg/dL (5 points), resulting in a “definite” diagnosis overall score (>8) for FH. Genetic testing is available for this condition but may not be needed unless there is a need to evaluate risk in other family members. Pharmacological therapy is indicated along with lifestyle modifications (diet, exercise, and smoking cessation). 

Physicians may rely on clinical decision support (CDS) tools embedded within the electronic medical record (EMR) to detect abnormal LDL-C results that are associated with FH. This Best Practice Advisory (BPA) is an example of one such CDS tool. This tool will take an input (e.g., lipid panel blood results), apply one or more rules to that input, and use the results of those rules to decide to execute (or not to execute) a specific action. In this example, the BPA triggers an alert offering the provider the option to prescribe high-intensity statin to this patient given the patient’s LDL-C was 190 mg/dL or greater.

The CDS Five Rights Model is one framework to use for the proper design, implementation, and sustainability of a CDS tool. The model consists of the right information, the right person, the right CDS intervention format, the right channel, and the right time in a workflow. Success of the CDS intervention is contingent on the success of each of these components individually. 

The right information is the data and evidence being used to generate a BPA. Adhering to data and interoperability standards assures the timeliness and the integrity of data. The right person is the user who needs to make an intervention based on the CDS; in this case it is the provider. CDS has various formats (reminders, alarms, and order sets). Reminders can be silent and may not interrupt workflows. Alarms may be needed for high-impact instances of care. Right channels can be EHR, paper flowsheets, or mobile device apps. In this case, EHR was better suited as a channel due to regulatory and business needs. 

The last component is the right timing. The provider mostly needs to see this alarm when the patient is present at the point of care. Firing the alarm not at the point of care may disrupt other workflows and may not lead to effective intervention and redundancy. Silent reminders can be used outside the point of care, as they are less likely to interrupt the workflow. 

Firing the alarm when the patient checks in or out is inappropriate due to unsuitable timing (patient is in the waiting room) and inappropriate person (front desk staff). Similarly, interruption may occur to nursing staff if this alarm fires. The alarm is most productive when it fires when the provider is at the point of care and the patient is being roomed (right time and right person). Using an alarm BPA in an EHR environment based on quality data standards and evidence-based rules will complete the full house of the Five Rights in a CDS. 

References

  1. Toft-Nielsen F, Emanuelsson F, Benn M. Familial hypercholesterolemia prevalence among ethnicities-systematic seview and meta-analysis. Front Genet. 2022 Feb 3;13:840797. doi:10.3389/fgene.2022.840797. PMID: 35186049; PMCID: PMC8850281. 
  2. https://www.cdc.gov/genomics/disease/fh/medical_options.htm 
  3. McGowan MP, Hosseini Dehkordi SH, Moriarty PM, Duell PB. Diagnosis and treatment of heterozygous familial hypercholesterolemia. J Am Heart Assoc. 2019 Dec 17;8(24):e013225. doi:10.1161/JAHA.119.013225. Epub 2019 Dec 16. PMID: 31838973; PMCID: PMC6951065.
  4. https://www.mayoclinic.org/diseases-conditions/familial-hypercholesterolemia/diagnosis-treatment/drc-20353757 
  5. Stone NJ, Robinson JG, Lichtenstein AH, et al. American College of Cardiology/American Heart Association Task Force on Practice Guidelines. 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2014 Jul 1;63(25 Pt B):2889-934. doi:10.1016/j.jacc.2013.11.002. Epub 2013 Nov 12. Erratum in: J Am Coll Cardiol. 2014 Jul 1;63(25 Pt B):3024-3025. Erratum in: J Am Coll Cardiol. 2015 Dec 22;66(24):2812. PMID: 24239923.
  6. Safarova MS, Kullo IJ (2016, June 1). My approach to the patient with familial hypercholesterolemia. Mayo Clin. Proc. Retrieved Nov. 9, 2022; https://www.mayoclinicproceedings.org/article/S0025-6196(16)30121-5/fulltext 
  7. Fry C. Development and evaluation of best practice alerts: Methods to optimize care quality and clinician communication. AACN Adv Crit Care. 2021 Dec 15;32(4):468-472. doi:10.4037/aacnacc2021252. PMID: 34879138.
  8. Semler, SC. LOINC: Origin, development of and perspectives for medical research and biobanking – 20 years on the way to implementation in Germany. Journal of Laboratory Medicine, vol. 43, no. 6, 2019, pp. 359-382. https://doi.org/10.1515/labmed-2019-0193 
  9. Sirajuddin AM, Osheroff JA, Sittig DF, Chuo J, Velasco F, Collins DA. Implementation pearls from a new guidebook on improving medication use and outcomes with clinical decision support. Effective CDS is essential for addressing healthcare performance improvement imperatives. J Healthc Inf Manag. 2009 Fall;23(4):38-45. PMID: 19894486; PMCID: PMC3316472.
  10. Douthit BJ, Musser RC, Lytle KS, Richesson RL. A closer look at the "right" format for Clinical Decision Support: methods for evaluating a storyboard bestpractice advisory. J Pers Med. 2020 Sep 23;10(4):142. doi:10.3390/jpm10040142. PMID: 32977564; PMCID: PMC7712422.

    EzzAddin Al Wahsh, M.D., M.B.A.

    Fellow, Clinical Informatics
    Mayo Clinic

    Justin Juskewitch, M.D., Ph.D.

    Senior Associate Consultant, Transfusion Medicine
    Mayo Clinic
    Assistant Professor of Laboratory Medicine and Pathology
    Mayo Clinic College of Medicine and Science

    MCL Education (@mmledu)

    MCL Education

    This post was developed by our Education and Technical Publications Team.