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elapsed from when the patient enters the operating room until he or she is wheeled out, another may be asking for when surgery actually began and when it ended. T ese inconsistencies forced data collection into separate silos and had a negative impact on overall data integrity. All of these factors contributed to

Hunterdon’s decision to implement the HPM system.

Overcoming the hurdles

Not too surprisingly, the main road- blocks to successful implementation of real-time analytics – as is true of many enterprise-scale systems – come from people rather than from technology. At Hunterdon, for example, it was diffi cult to realize and accept that data integrity was not at the level we had previously assumed. Neither technologists nor end users realize exactly how much data mas- saging and adjustment is taking place when information from separate systems combines to deliver a strategic view until they see the information in real time. T ere was some pushback from those who had tried to use other dashboards from previous implementations and found the information delivered to be less than useful. T ese challenges are gradually dis- appearing as executives realize and accept that the data itself, rather than the system, is a major issue, and that real-time analytics makes it possible to understand and eff ectively utilize that data to drive informed decisions and improve outcomes. Equally important, we are discovering that embedded Deep- See analytics can help uncover missed opportunities that are inevitable with purely retrospective analysis – the only analysis that is possible when data is be- ing stored in a separate data warehouse and gathered for reporting via batch jobs run nightly rather than in real time.

Early-stage benefi ts delivery Hunterdon began the HPM rollout

in the fi rst quarter of 2012, which means

that any attempt at benefi ts quantifi ca- tion would be premature. At present, the focus is on measuring certain specifi c indicators, including: • Readmission indicators for pneu- monia, heart failure and acute myocardial infarction;

• Clinical indicators for patients with obstetric trauma, vaginal delivery with and without instru- ments, patients with postoperative respiratory failure and mortality during a hospital stay; and

• Lengths of a patient’s hospital stay by physician, by certain diagnoses and by payer.

Decisions concerning what should

be measured and how analytics should be displayed are made by a team that includes executive/fi nancial manage- ment, clinicians and IT specialists. It’s already evident that the information being delivered is going to prove very useful for responding to the ever- expanding reporting requirements of government agencies. After stopping reimbursement for preventable medi- cal errors, for example, the Center for Medicare and Medicaid Services (CMS) is now focusing on preventable readmis- sions and measuring readmissions in the Medicare fee-for-service program. In 2011, the National Committee for Quality Assurance added an all-cause readmission category to its measures, and in the second quarter of 2012, the National Quality Forum endorsed those metrics. While there is a fair amount of ongoing debate about the relationship between hospital readmissions and qual- ity of care delivery, Hunterdon is now in a better position to respond to agency information requests with accurate data delivered in a timely manner. Although evidence is anecdotal at this early stage of analytics implementa- tion, there is agreement among Hunter- don clinicians that the ability to access patient information on a continuous basis will enable improved care delivery and outcomes.


Recommendations for

laying out the analytics roadmap Selecting the appropriate software

product for analytics-based decision support is a critical success factor. However, before that decision is made, it is essential to ensure that the right people in the healthcare organization are involved from the start of the initiative. It is strongly recommended that the CIO identify the executives, clinicians and staff members who will benefi t most from a real-time analytics dashboard system. Once there is a clear understanding of the pain points of potential end users, it becomes easier to make them your champions within the organization. When there is suffi cient internal

support for the initiative, the impor- tance of selecting the right technology to provide the information foundation for the dashboards cannot be over- stated. DeepSee proved to enable fast development, supported very rapid in- formation retrieval and eliminated any need for a separate data repository. In addition, this technology choice made it possible to leverage Hunterdon’s existing investment in the InterSys- tems database system and integration/ development platform. InterSystems provided strategic support throughout the HPM project and recommended Cognizant, the consulting fi rm that teamed with Hunterdon personnel to develop and implement real-time analytics in our environment. In summary, building internal

support throughout the organiza- tion, closely examining the technol- ogy foundation of any DSS solution and pulling together an experienced development team are keys to suc- cessful analytics implementation. It means a significant investment of multiple resources, but Hunterdon is convinced that the payoff in terms of improved decision making and optimal care delivery makes that investment worthwhile.

HMT February 2013 17

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