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The American Journal of Managed Care November 2015
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Using Sequence Discovery to Target Outreach for Diabetes Medication Adherence
April Lopez, MS; Charron Long, PharmD; Laura E. Happe, PharmD, MPH; and Michael Relish, MS
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Using Sequence Discovery to Target Outreach for Diabetes Medication Adherence

April Lopez, MS; Charron Long, PharmD; Laura E. Happe, PharmD, MPH; and Michael Relish, MS
Sequence discovery techniques identified sequences of events that led to gaps in diabetic therapies and were used to identify outreach opportunities aimed at improving medication adherence.
Overall, 602 exposure sequences were identified in the gap group and 271 in the no-gap group, with 1069 shared between both groups (Figure 1). As shown in Table 2, exposure sequences with the highest support and confidence for the gap group included inpatient hospital stays in 3 of 5 exposure sequences; sequences of 4, 5, or 6 exposure lengths all had multiple hospitalizations or hospitalizations with multiple inpatient days. Hospitalizations were not present in sequences for the no-gap group (data not shown). 

Outbound voice-activated technology (VAT) calls and natural disasters were more common singular exposures in the 90-day lookback period studied for the gap group compared with the same lookback period for the no-gap group, with a between-group difference of 18.5% and 6.9%, respectively (data not shown). Exposures more common in the no-gap group included annual physical exams, outbound calls from the mail order pharmacy, and reversed prescription claims, with frequencies 2.1%, 2.3%, and 3.0% higher, respectively, than the gap group (data not shown).

Exposures and Sequences for Targeted Outreach

The most frequent initial and final exposures in sequences for the gap group were: 1) specialty care physician visit, 2) new prescription, 3) out-of-network service claim, 4) hospitalization, 5) outbound VAT call, and 6) prescription claim reversal (Figure 2). Based on the criteria for targeting outreach, 3 sequences for possible interventions were identified (Table 3). Outreach opportunities identified from these sequences included individuals taking diabetes medications who are prescribed a new medication—especially those who have multiple out-of-network claims and/or visit a specialty physician after the new medication is prescribed. Those taking diabetes medications who have a prescription claim reversed should receive an outreach—especially if they subsequently are prescribed a new medication or visit a specialty physician. Finally, individuals taking diabetes medications who have multiple out-of-network claims should receive an outreach—specifically those who also have a hospitalization.

Gap Group Sub-Analysis: Evaluation of the Gap and No-Gap Period in the Same Patient

Comparing the gap versus no-gap periods in the same patients, out-of-network claims were noted in nearly all top sequences for the gap period. Out-of-network claims, natural disaster in the area, and inpatient hospital stays were more frequent singular exposures in the gap period. Exposures identified in the gap period of the sub-analysis (ie, out-of-network claims and inpatient hospital stays) were also among the top exposures identified in the primary gap period analysis. Outbound VAT calls, change in prescription delivery channel, inbound mail order pharmacy calls, and being prescribed a new medication were more frequent during the no-gap period (data not shown).

DISCUSSION
Contrary to traditional epidemiologic methods, which quantify associations between outcomes and postulated exposures, this study used data mining techniques to explore associations between diabetes medication adherence and a large set of exposures without regard to whether the exposures had a hypothesized relationship with the outcome. Since no previous studies have applied this approach in medication adherence, this work provides unique insights into several exposures, which have not previously been investigated. To illustrate this point, the top exposures identified in this study were contrasted with 18 exposures reported (regardless of whether an association was found) in a 2014 systematic literature review of 27 studies evaluating factors associated with adherence to diabetes medications, and there was no overlap.27 The literature review reported great variability in factors predictive of nonadherence, highlighting the importance of a new approach to evaluating this topic.

Importantly, this study went beyond simply identifying associations to investigating sequences of exposures, upon which a health plan can intervene to potentially prevent gaps in prescription refills before they occur. Since health plans have unique access to more data about a given patient and their medical providers than any other part of the system, the insights provided by this study can assist plans in intervening to positively affect diabetes medication adherence. Health plans can use automated systems to create electronic alerts when an event or series of events—that can only be seen at the health plan level—occur, as defined in Table 3. Those alerts can trigger a variety of actions; for example, they may alert care managers to incorporate actions into a patient’s care management plan to address the potential for nonadherence or prompt a medication therapy management pharmacist to contact the patient for a medication consultation. A variety of automated interventions could also be generated. Humana is utilizing the information from this study to inform intervention strategies, and future research should quantify the effectiveness of acting on the events identified in this work.

It is important to emphasize both the clinical and economic relevance of efforts to improve adherence to diabetes medications. It is well documented that poor medication adherence in diabetes is associated with increased hospitalizations and ED visits,31-34 which are often manifestations of poor glycemic control. Illustrating the relationship between glycemic control and medication adherence, a 1-year study by Kaiser Permanente of 1560 patients with type 2 diabetes reported that glycated hemoglobin was reduced by 0.34% for every 25% increase in medication adherence (P = .0009).32  This same Kaiser study found higher all-cause mortality in nonadherent patients compared with their adherent counterparts. Worsening health outcomes are almost ubiquitously accompanied by increasing costs, as is the case in nonadherence. Reports have suggested that eliminating poor adherence to insulin and oral medicines would generate over $13,000 in savings, on average, to each newly diagnosed patient, or $10.7 billion in aggregate.35 Finally, CMS recognizes the importance of adherence to oral diabetes medications, as it is among the patient safety outcomes measures for the CMS Plan Quality and Performance Program, or Stars Rating program.36 A 2014 study by Medicare Advantage Part D pharmacy benefit manager, MedImpact, reported a positive impact of a coordinated, member-directed medication adherence intervention program on adherence and star rating adherence measures.37

Limitations

Although this study provides novel and actionable insights for health plans to potentially improve adherence to diabetes medications, there are limitations to the work. This study was conducted within a single health plan population, which has members in all 50 states, but is highly concentrated in southern regions. The study relied upon data inputs available within this health plan; therefore, the generalizability to other populations or health plans without the same data elements available may be limited. This study also exclusively evaluated oral antidiabetic medications; future work should evaluate other therapeutic classes to determine if these findings can be applied to a broader array of chronic conditions. The data mining technique applied in this study is subject to the risk of finding spurious associations, but risk was limited by the use of statistically sound association techniques. This technique is exploratory in nature; the criteria for selecting exposure sequences for subsequent intervention were subjective and may not be practical in other settings. As with all studies which rely on retrospective review of electronic data captured for other purposes, there may have been unmeasured exposures, coding errors, or missing data; however, given the size of the data set, the impact of the latter would be minimal.

CONCLUSIONS
Medication adherence is a persisting challenge that has substantial clinical and economic consequences; yet, traditional epidemiologic methods and interventions have had limited ability to influence adherence at a population level. This novel application of sequence discovery techniques identified unique sequences of events with opportunities for health plan outreach. The health plan’s unique access to the breadth of data, coupled with the novel sequences of events identified as precursors to gaps in therapy in this study, present a promising new approach to preventing nonadherence.

Acknowledgments

The authors would like to thank Victor Lawnicki, Shane Rathbun, and Peinie Young for their contributions to this research. 

Author Affiliations: Consumer Analytics (AL, MR), Humana Inc (CL, LEH), Louisville, KY.

Source of Funding: None.

Author Disclosures: All authors are employed by Humana, which is a national health insurer and commissioned this work to improve the quality of care for its population.

Authorship Information: Concept and design (AL, MR); acquisition of data (AL); analysis and interpretation of data (AL,CL, LEH); drafting of the manuscript (AL, CL, LEH, MR); critical revision of the manuscript for important intellectual content (AL, CL, LEH, MR); statistical analysis (AL); administrative, technical, or logistic support (CL, MR); and supervision (CL).

Address correspondence to: April Lopez, MS, 500 W Main St, Louisville, KY 40202. E-mail: alopez18@humana.com.
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