Capturing Senior Legal Hotline Elder Abuse Data

by Tom Bedall

Domestic and financial abuse data had been captured by Pro Seniors’ Senior Legal Hotline ever since its inception in 1990.  However, the expanded scope of elder abuse data collection requested for 2010 by the Senior Legal Helplines/Hotlines’ Report to the AoA required us to take a much more systemic approach to gathering such data, as described below.

The first abuse data request asked that victims of financial exploitation be divided into two groups, the number of clients who were financially exploited by family members or relatives and those who were victimized by non-family members or non-relatives.  In response, as suggested, we began capturing abuse data under the following legal problem codes:

·       Code #37 – Domestic Violence

·       Code #39 – Other Family (Financial Exploitation by Family/Relatives)

·       Code #89 – Other Individual Rights (Financial Exploitation by Non-Family/Non-Relatives)

For calendar year 2011, the AoA Report abuse data collection expanded to six data points:  financial abuse, physical abuse and neglect by either a family or non-family member.  The easy solution was to expand our Pika special problem codes, a.k.a. sub-problem codes, for problem code 37 to include abuse and neglect by non-family members, which we did.  However, we quickly realized that we needed a more systemic method of capturing elder abuse across all legal problem codes.

Prior to AoA Report abuse data collection, we typically used legal problem code 37 – Domestic Abuse to tag elder abuse cases, and calculated the number of abuse cases by totaling the number of legal problem code 37 cases.  We soon realized we were too limited in our data collection.  Just as abuse is not always perpetrated domestically (37 – Domestic Abuse), it is often present in cases where abuse is not the primary legal problem.  For instance, the most frequent reason for nursing facility discharge is failure-to-pay caused by either the Medicaid application not being submitted or if submitted and approved, nursing facility payment is denied due to an improper transfer of assets.  In either case, the common theme is an uncooperative relative keeping the client’s money or refusing to sell the client’s house because the relative is living in it.

In both scenarios, the primary legal problem presented is 51 – Medicaid.  Anecdotally, we knew that elder abuse was present in any number of non-legal problem code 37 cases, but it was the Senior Legal Helplines/Hotlines expanded abuse data collection that forced us to look for a systemic solution.  Thus we decided that the abuse component of a case could not be exclusively captured by a problem code or even a special problem code, as every possible problem code could have an abuse component.

Though we are always reluctant to require intake staff or hotline attorneys take the time to capture more data, we realized we had no choice but to add a drop-down to our Pika case management system (CMS) case closing screen.  By putting the AoA Report’s six abuse data points – financial abuse, physical abuse and neglect by either a family or non-family member – into a drop-down menu, only two mouse clicks were required, one click to drop the menu and one click to choose the menu item.

  • None [default]
  • Fin. Exploitation by Family Member
  • Fin. Exploitation by Non-Family Member
  • Neglect by Family Member
  • Neglect by Non-Family Member
  • Abuse by Family Member
  • Abuse by Non-Family Member

 As a safety check we also required a few words describing the abuse in a text box so management could review the abuse facts for definitional correctness without having to peruse the case notes.  A brief statement, such as “FPOA took money” or “Son verbally abusive and threatening,” was sufficient.

The last component of accurate data collection is a common understanding of what meets the definition of abuse.  The standard definition is met when a person has been hurt mentally, emotionally, financially or physically.  Whether the client has been hurt by another can be subjective and for our purposes must be self-reported.  If the client considers the behavior abusive, then we count it as such so long as it passes a basic reasonableness test.

Probably the most prevalent financial abuse is a relative’s breach of fiduciary duty under a FPOA.  Although non-domestic financial abuses such as fraud, theft or scams are easy to spot, there are cases that are harder to categorize.  In the supplier-consumer area, we decided that Ohio’s Consumer Sales Practices Act definitions of deceptive and unconscionable transactions also met the definition of financial abuse.

Systemically capturing elder abuse data for the AoA Report was an eye-opener for Pro Seniors.  Even though we knew our legal problem codes were not capturing all cases involving abuse, we were surprised by the sheer number of cases involving some form of abuse.  After taking the more comprehensive approach described above, we found that 57% of our cases that had an abuse component were not being captured by our prior data collection method.  This proved to us the value and necessity of adding new fields to our CMS.

As funding and reporting requirements change, so must the data captured by our CMS.  It must be dynamic.  Every new funding request we consider includes the funder’s reporting requirements, which takes into account the new data points to be captured including the cost to add them to the CMS and the staff time for data entry.

All programs must have the ability, either internally or through an outside vendor, to modify the CMS to meet new reporting requirements.  The same policy applies to reporting data from the CMS.  Static hard-coded CMS reports can be useful, but may not meet the needs of a new funder.  Once you have used a dynamic change-on-the-fly dedicated reporting application like Crystal Reports (Crystal Reports 2011, $55 at Techsoup.org), I guarantee you will never return to the CMS’ built-in report writer.  Plus, Crystal Reports opens new dimensions for reporting, like including census data and formulas that compare percent of minority clients served to percent of minority elderly in your service area.

Capturing and reporting new data is always challenging, but having the talent and systems in place to accomplish it easily reduces the stress and routinizes the process that will definitely be repeated in the future.

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