The efficacy of involuntary outpatient treatment in Massachusetts

Administration and Policy in Mental HealthVol. 25, No. S.January 1998

THE EFFICACY OF INVOLUNTARY OUTPATIENTTREATMENT IN MASSACHUSETTS

Jeffrey Geller, Albert J. Grudzinskas, Jr.,Melissa McDermeit, William H. Fisher, and Ted Lawlor

ABSTRACT: One means to address some of the unintended consequences of the shift oftreatment for individuals with serious mental illness from hospitals to communities hasbeen involuntary outpatient treatment (IOT). Using Massachusetts data, 19 patients withcourt orders for IOT were matched to all and to best fits on demographic and clinicalvariables, and then to individuals with the closest fit on utilization before the IOT date.Outcomes indicated the IOT group had significantly fewer admissions and hospital daysafter the court order. The full impact of IOT requires more study, particularly directedtoward lOT's effects on insight and quality of life.

The dilemma of reconciling the principles articulated by John StuartMill in On Liberty (Mill, 1859) with the need to correct the "greatest failingof the modern mental health system . . . the failure of continuity of care,"without resorting to "acts of civil disobedience" (Stone, 1982) looms largein contemporary American psychiatry. Mill stated, "The only purpose forwhich power can be rightfully exercised over any member of a civilizedcommunity, against his will, is to prevent harm to others. His own good,either physical or moral, is not a sufficient warrant." Mill was quite clear in

Jeffrey Geller, M.D., M.P.H., is Director of Public Sector Psychiatry, University of Massachusetts MedicalCenter, and Professor of Psychiatry, University of Massachusetts Medical School. Albert Grudzinskas,Jr.,J.D., is Assistant Professor of Psychiatry in Law, University of Massachusetts Medical Center. MelissaMcDermeit, M.S.W., is Research Associate, Chestnut Health Systems, Lighthouse Institute, Bloom-ington, IL. William H. Fisher, Ph.D., is Director, Center for Psychosocial and Forensic Services Re-search, University of Massachusetts Medical Center, and Associate Professor of Psychiatry, University ofMassachusetts Medical School. Ted Lawlor, M.D., is Medical Director, Connecticut Department ofMental Health and Addiction Services; Assistant Professor, University of Connecticut Health Center;and Assistant Professor of Psychiatry, University of Massachusetts Medical School.

The authors are grateful to Steven M. Banks, Ph.D., for statistical consultation.Address for correspondence: Jeffrey Geller, M.D., M.P.H., Department of Psychiatry, University of

Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA 01655.

271 © 1998 Human Sciences Press, Inc.

Administration and Policy in Mental Health

specifying limits, "He cannot rightfully be compelled to do or forbear be-cause it will be better for him to do so, because it will make him happier,because, in the opinion of others, .to do so would be wise, or even right"(Mill, 1859). Given these standards, it has become a paramount challengeto interrupt the revolving door of inpatient treatment, followed by recom-pensation and stabilization to such a degree as to permit discharge to acommunity setting, only to see decompensation, deterioration, and subse-quent re-admission (Stefan, 1987).

Breaking the cycle of recidivism requires a plan that is simultaneouslymedically efficacious and grounded in acceptable legal principles. Anyprotocol designed to ensure compliance with treatment must continue toprovide for the liberty interests and freedom from unwarranted interven-tion in people's lives that have become accepted tenets of contemporarytreatment of mentally ill individuals (Addington v. Texas, 426, 1979; O'Con-nor v. Donaldson, 1975)

MASSACHUSETTS

Massachusetts is one of approximately 15 states currently without a statu-tory or case law based provision for involuntary outpatient commitment(Torrey & Kaplan, 1995). Massachusetts' in-patient commitment requires ashowing that the person to be committed presents a "likelihood of seriousharm," further defined as "a substantial risk" of harm to self or others or "avery substantial risk" of harm to self due to an inability to care for andprotect oneself in the community (Massachusetts General Laws, Chapter123, Sections 1, 7 & 8). The commitment criteria also include a showingthat "there is no less restrictive alternative placement" to properly attendto the needs of the respondent in a commitment case (Siddel v. Marshall,1987).

Without some degree of legislative assistance to establish an outpatientcommitment procedure for Massachusetts, large numbers of patients couldremain caught in the revolving door, or even worse, "dying with theirrights on" (Treffert, 1973). While remaining free from intrusion into theirpersonal liberty interests, individuals had been unable to maintain accept-able freedoms from risks while living in outpatient settings. This leads tore-hospitalization, thereby denying these individuals an opportunity tomaintain themselves in the least restrictive setting their particular disabil-ities would allow. While the threat of coercion may be undesirable to mostat most times, it is interesting that, in at least one study of severely men-tally ill outpatients, a majority of the respondents, looking back later, feltthat it was in their best interest to have been pressured into communitytreatment (Luckstead & Coursey, 1995). This is not to say that an involun-

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tary outpatient treatment (IOT) remedy would solve the crises of continuityof care, for involuntary community treatment cannot replace the need foradequate community resources (Geller, 1993; Schwartz & Costanzo, 1987).

Even without further legislative assistance, the law currently is not blindto the needs of persons who are mentally ill. The U. S. Supreme Courtnoted, "One who is suffering from a debilitating mental illness and inneed of treatment is neither wholly at liberty nor free of stigma" (Add-ington u Texas, 429, 1979). Further, not all rights-based attacks on intrusioninto peoples lives have resulted in improvements in either lives or rights(Haycock, Finkelman, & Presskreischer, 1994).

Since Massachusetts law did not provide for IOT, attention was turned tothe state's guardianship laws. The Massachusetts guardianship statute pro-vides that if a court ". . . finds that [a person] is incapable of taking care ofhimself by reasons of mental illness, it shall appoint a guardian of his per-son . . ." (Massachusetts General Laws, Chapter 201, Section 6). A judgemay adjudicate a person to be "competent to make some decisions, butnot others" (Rogers v. Commissioner of Department of Mental Health, 495,1983). An informed decision about medical treatment "requires knowl-edge of the available (treatment) options and the risks attendant on each"(Harnish u Children's Hospital Medical Center, 1982). Further, the ward's in-ability to fairly "weigh the risks and benefits of treatment," because of hisinability to accept being mentally ill can support a finding of incompe-tency (Guardianship of John Roe, 1992).

Involuntary community treatment cannot replace the need for adequate communityresources.

The Massachusetts Supreme Judicial Court has held that "a person has aright to refuse" to submit to invasive and potentially harmful medical treat-ment, such as the administration of antipsychotic drugs (Guardianship ofWeedon, 1991). This right extends to incompetent as well as competentpersons "because the value of human dignity extends to both" (Superinten-dent of Belchertown State School u Saikewicz, 1977). In order to override apatient's refusal of antipsychotic medication, a judge must find that thepatient is incompetent to make this decision. The judge may then addresswhat the patient would choose, if he were competent, using a substitutedjudgment standard (Rogers v. Commissioner of the Department of MentalHealth, 500-506, 1983). This standard requires the judge to weigh at leastsix distinct factors: the patient's preference; beliefs (including religiousconvictions); family situation; probability of adverse side effects; and prog-nosis, with and without treatment (Rogers v. Commissioner of the Depart-ment of Mental Health, 500-506, 1983).

In addition to the substituted judgment factors, a number of other fac-

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tors had to be considered when deciding which patients would be selectedfor IOT. It was first necessary to consider clinical factors that have beenidentified as prerequisites for successful IOT (Geller, 1990; Geller, 1986).It was then necessary to develop a profile that would fit the legal require-ments for guardianship in Massachusetts, and to develop a protocol forenforcement that was within the permissible scope of the existing law.

In order to be included in the compulsory outpatient treatment pro-gram, a patient would have to be: (1) a repeat user of inpatient services,who recompensated to the point of discharge to the community; (2) some-one whose failure to remain stable in the community could be traced tonon-compliance with recommended treatment; (3) someone whose de-compensation after non-compliance was followed by re-admission due torisk of harm to him/herself and/or others; (4) living in an area servicedboth by outpatient providers who would track compliance and by policewho would execute pick-up orders; (5) treated with a decanoate prepara-tion since failure to attend the evaluation/medication session was the trig-gering mechanism for intervention; and (6) incapable of meeting the Mas-sachusetts competency standards relating to guardianship.

The final prerequisite was the development of a treatment plan, orderedby the court, that would allow for (1) a reasonable effort to achieve volun-tary treatment compliance; (2) a notification process to alert the guardianto the potential need for action; (3) an enforcement provision that eventu-ally involved local police action; (4) authorization for police action (whenall else failed); (5) authorization for such restraints as were necessary toaccomplish medication compliance when deemed appropriate; and (6) areasonable degree of acceptance by counsel who represented the pro-posed wards.

Patients were first selected from the acute admission service of Worces-ter State Hospital and later from other services by their own treating psy-chiatrist; no patient entered the program without providers' adherence tothe due process protections provided by the Massachusetts guardianshiplaws and court decisions relating to competency, substituted judgment andthe right to refuse treatment. A person could be entered into the programonly after (1) notice; (2) a hearing for which counsel was appointed torepresent the ward; (3) a judicial finding of incompetency; (4) a substi-tuted judgment finding by the Court; (5) appointment of a guardian and acourt monitor (who reported to the Court); and (6) development of a casemanaged outpatient treatment plan, that became part of the ProbateCourt Decree.

At each step of the procedure, there are substantial safeguards and re-quirements for assessment by those who are well situated by virtue of theirrelationship to the ward, either therapeutic or otherwise, to make evalua-tions with respect to potential for harm. The proposed court order con-tains a provision for review on at least an annual basis. The plan also calls

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for reasonable restraint on the part of caregivers in exercising the author-ity requested. The order that was developed is included in Appendix A.

The involvement of the police as an enforcement agent is perhaps themost controversial and ethically vexing element of the plan. Massachusettslaw empowers police to "prevent all disturbances and disorder" (Massa-chusetts General Laws, Chapter 41, Section 98) and to seek hospitalizationfor those who would create a likelihood of serious harm by reason of men-tal illness (Massachusetts General Law, Chapter 23, Section 12). It wasthought police involvement would produce the safest and most effectiveway to compel attendance in outpatient treatment if coercion became nec-essary. Such intervention, if made pursuant to an established policy in sup-port of a "special need" (such as prevention of harm by those mentally illwho become dangerous) has been permitted even in cases where no courthas previously reviewed a request for authority to enter the premise (Mc-Cabe v. Life-Line Ambulance Services, Inc., 1996).

The frequency with which one needs to utilize this final police involve-ment enforcement provision has never been determined. Anecdotal infor-mation in Massachusetts indicates the police have been utilized only onrare occasions. This parallels observations of patients' responses to IOT inother states (Geller, 1986; Hiday, 1996).

The proposed plan is an attempt to intervene in a recurring processwhereby an individual's conduct had repeatedly exposed him/her orothers to risk. This intervention sought to demonstrate that compliancecould be enforced with positive results, at least with respect to decreases innumber of hospital admissions and in length of stay when such admissionswere required. Improved quality of life was expected to be an importantsubsidiary outcome of prolonged community tenure.

METHOD

The data used for this analysis come from the Massachusetts Depart-ment of Mental Health (DMH) Client Tracking System (CTS). This dataset contains information on demographic, clinical and services utilizationfor case-managed clients statewide, beginning July 1, 1991. We first com-pared the IOT patients during the pre-treatment period with their ownpost-treatment data. Second, we compared the IOT patients with patientsmatched on demographic variables. Finally, we compared the IOT patientswith others matched on inpatient service use.

The IOT Group

The study population consists of the first 23 patients from Central Mas-sachusetts who received involuntary outpatient treatment (IOT) orders.The IOT orders occurred between May 23, 1991 and November 30, 1993.

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Two of the patients were not discharged during the 6 months followingtheir order, and two others were admitted under criminal court order.These four were excluded from this analysis, the first two for lack of oppor-tunity to be readmitted, and the latter two because their admission wasdependent upon criminal court decisions rather than clinical criteria. Foreach of the 19 remaining patients, we computed the number of admissionsand total number of hospital days for the 6 months before and after thedate of their IOT order.

The Demographic Matches

The 19 IOT patients were matched with other GTS clients from theCentral Massachusetts Area on demographic and clinical variables. Thesevariables included: gender, diagnosis (primary and where available, sec-ondary diagnoses), age, and the quarter of the year they first appeared inGTS. For each match we computed the number of admissions and hospitaldays in the same two 6-month periods as their IOT counterpart based onthe IOT patient's order date. For example, for the IOT patient whoseorder date was May 23, 1991 and his/her matches, the pre-treatment pe-riod began November 23, 1990, and the post-treatment period ended No-vember 23, 1991. We compared the number of psychiatric admissions andpsychiatric inpatient days for the IOT patients with all patients whomatched, and with the single best match.

The Inpatient Use Matches

Since the IOT group, by definition, consisted of persons with an aboveaverage number of admissions, extended stays, or both, it is likely thatmatching solely on demographic data would bias the sample. The resultscould simply represent "regression toward the mean." In this phenome-non, cases exhibiting extreme values on a given variable will, over time,drift back toward the center of the distribution due to statistical probabilityrather than substantive interventions (Cook & Campbell, 1979). To elimi-nate this possibility, we performed a further analysis matching on the num-ber of admissions and hospital days during the pre-treatment period. In asmall number of cases, the number of admissions and days were the samefor the several IOT patients (e.g., five IOT patients had one admission forthe full 183 days of the pre-treatment period). In these cases, we usedgender and then age to further specify matches.

It was not possible to use the IOT patient order date for the identifiedmatches to compute number of admissions and days. In order to identifythe match, the number of admissions and days needed to be computedfirst. Therefore, we used August 11, 1992 (midway between the first andthe last of the 19 IOT orders) as the midpoint for the matched patients(non-IOT). For this analysis, we again compared IOT patients with all of

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their matches, and with the single best match. In several cases, there weremultiple "exact" matches on number of admissions and hospital days. Weselected the best inpatient match based first upon number of admissionsand days, then gender and age.

The Comparisons

Figure 1 shows the four significance tests used to compare the IOTgroup and the four match groups (all/best demographic and all/best in-patient use), on number of admissions and inpatient days within the twotime periods. These tests, (numbered one through four in the figure),respectively compare: (1) the pre-treatment data for the IOT patients withthe post-treatment data for the IOT patients; (2) the pre-treatment datafor the match patients with the post-treatment data for the match patients;(3) the pre-treatment data for the IOT patients with the pre-treatmentdata for the match patients; and (4) the post-treatment data for the IOTpatients with the post-treatment data for the match patients.

We hypothesized a significant decrease for comparison 1, indicating thatthe IOT patients have decreased inpatient days and admissions followingthe IOT order. Comparison 2 gives an indication of system changes thataffect all patients. If these differences are significant and of similar sizeand direction as those seen in Comparison 1, it is possible that a variable

FIGURE 1


other than IOT status affected the observed changes. Comparison 3 indi-cates a baseline comparison of the IOT group with the controls. Compari-son 4, for which we hypothesized that the IOT group's days and number ofadmissions would be equal to or less than their matches, is of particularinterest. To compare the differences over time for the two groups, we com-puted difference scores by subtracting the post-treatment values from thepre-treatment values. We then compared these difference scores for thetwo groups.

We used both parametric (t-tests) and non-parametric tests (Wilcoxontwo-sample tests) of differences between the matched pairs. The Wilcoxontest requires no assumption of normality, and eliminates concerns aboutdistributional properties of the data given samples of the size used here(Siegel & Castellan, 1988). The results of the parametric and non-parame-tric tests were equivalent, assuring us that these factors did not affect ourresults. We have reported the t-test throughout, however, because of itsease of interpretation. Repeated measures analysis of variance is also ap-propriate in this type of analysis. However, the use of only two periodsmakes repeated measures analysis of variance reduce to differences overtime which may be compared using a standard t-test.

RESULTS

IOT Patients

The 19 IOT patients averaged 38.5 years of age. Sixty-three percent weremale. Fifty-eight percent had a diagnosis of schizophrenia. The averageGTS start date was July, 1993. These patients averaged 1.63 admissions(SD=1.16, range = l-5, total admissions = 31) and 112.73 days (50 = 54.1,range = 30-183, total days = 2142) during the 6 months before their treat-ment order. During the 6 months following the order, both of these fig-ures decreased significantly (comparison 1 in Figure 1) to a mean of .58admissions (paired «est = 2.97, df=18, p=.008, 2 tailed test; SD=2.06,range = 0-9, total admissions = 11) and 44.3 days (paired Rest = 3.53,df=18, p=.002, 2 tailed test; SD=59.3, range 0 to 183, total days = 842).

Demographic Matches

All Matches. We found 53 demographic matches for the IOT patients.The matches did not differ significantly on any of the matching variables.On average, they were 37.6 years old, and started in CTS in May, 1993.Sixty-eight percent were male, and 70% had a diagnosis of schizophrenia.

These matches had an average of 19.42 days (SD=44.9, range = 0-183,total days=1029), and .49 admissions (5D=.95, range = 0-5, total admis-sions = 26) during the pre-period. During the post-period, this group aver-

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aged 22.1 days (SD=54.2, range = 0-183, total days=1171) and .21 admis-sions (SO =.57, range = 0-3, total admissions = 11).

Comparison 2, comparing the matched patients during the pre- andpost-treatment periods, showed no significant difference in days, but a sig-nificant decrease in number of admissions (/=2.60, d/=52, p=.012, 2tailed test). In comparison 3, both days and number of admissions differedsignificantly. Thus, the two groups were significantly different during thepre-treatment period (days: 2=6.73, df= 27.39, 2 tail p=.000 (unequal vari-ance); admission: t=4.22, d/=70, 2 tail p=.000 (equal variance)). Finally,comparison 4, (IOT and matches during the post-treatment period) showedno statistically significant difference for either days or admissions.

The difference scores for the IOT and match groups showed signifi-cantly greater decrease for the IOT group on both days and number ofadmissions. The IOT group showed an average decrease of 68.42 dayscompared to an average increase of 2.67 days for the match group (t=-3.59, df= 19.62, 2 tail />=.002 (unequal variance)). Similarly, the IOTgroup showed an average decrease of 1.05 admissions, while the matchedgroup decreased by .28 admissions (t= -2.77, d/=70, />=.007(equal vari-ance)).

These data show that the IOT group differed significantly from theirmatches during the pre-treatment period. Both groups showed some de-crease in their use of inpatient services, although the matched group'sdecrease was only for number of admission. The IOT group showed signifi-cantly greater decreases than their matches, with the result that the twogroups were statistically the same on both days and number of admissionsduring the post-treatment period.

Best Matches. The 19 GTS patients who best matched the IOT patients ondemographic characteristics were identical in age (38.5 years), gender(63.2% male), and diagnosis (57.9% schizophrenic). They did differ onCTS start date with the match group starting somewhat earlier (November,1992) than the IOT group (July, 1993) (*=2.68, rf/=36, 2-tail p=.OU(equal variances)). The start date was used to assure that the matches didnot have less time in CTS. This shows that they actually had slightly moretime to accumulate admissions and days than the IOT patients

The best matches had an average of 15.1 days (SD=43.8, range = 0 to175, total days = 287) and .21 admissions (SD=.54, range = 0 to 2, totaladmissions = 4) prior to the IOT order date. In the post-period, they hadan average of 11.4 days (SD=41.8, range = 0 to 183, total days = 217) and.11 admissions (SD.32, range = 0 to 1, total admissions = 2). These changeswere not significant.

Again, the IOT group differed significantly from the best match groupduring the pre-treatment period (comparison 3), for both days and num-

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her of admissions (days: £=6.11, df= 34.49, /?=.000 (unequal variance);admissions: £=4.83, d/=25.28, j&=.000 (unequal variance)). For bothgroups, days and number of admissions decreased in the post-treatmentperiod, (comparisons 1 and 2) although the difference was significant onlyfor the IOT group. In the post-treatment period, although the IOT groupremained higher, the two groups were statistically no different on eitherdays or number of admissions (comparison 4).

Comparison of the difference scores shows that the IOT patients de-creased significantly more than their best demographic matches for bothdays and admissions. The average decrease in days was 68.42 days for theIOT patients compared to 3.68 days for the matched patients (£= —3.27,df= 19.5, 2-tail p— .004 (unequal variance)). Similarly, the number of ad-missions decreased by 1.05 for the IOT patients, but only one tenth asmuch (.105) for the matches (t= -2.56, d/=21.15, 2-tail p-.018 (unequalvariance)).

Inpatient Use Matches

All Matches. Thirty-eight CTS patients matched the IOT group on thenumber of admissions and number of inpatient days used during the 6months prior to the IOT order date. These 38 patients matched at a rateof one, two, or three per IOT patient. On the match variables, this grouphad an average of 119.9 days (SD=55.2, range = 32-183, total days =4,555) and 1.53 admissions (SD=1.05, range = 0-5, total admissions = 58)during the matching time period. By design, these two groups were notsignificantly different on either days or number of admissions (comparison3) during this period. The matches did not differ significantly on gender(55.3% male). However, they were somewhat older than the IOT patients(45.6 years, t= -2.46, d/=55, 2-tail />=.017 (equal variance)).

During the post-treatment period, the match group reduced (compari-son 2) their average days to 69.9 (SD=73.5, range = 0-183, total days =2,656), while the number of admissions decreased to .68 (5D=1.44,range = 0-6, total admissions = 26). Both of these were significant de-creases (days: £=3.9, d/=37, 2-tail p= .000; admissions: £=3.38, d/=37,2-tail /»=.002). Comparison 4 showed no difference between the twogroups for either days or number of admissions. In addition, the differ-ence scores for days and admissions were statistically the same for bothgroups.

The IOT and Inpatient Use Matches groups originally looked statisticallysimilar. Both groups decreased significantly and similarly. For both daysand number of admissions, the IOT patients ended up numerically lowerthan the matched patients during the post-treatment period, although nei-ther difference was significant.


J. Getter, AJ. Grudzinskas, Jr., M. McDermeit, W.H. Fisher, and T. Lawlor

Best Matches. The 19 best matches based on inpatient use did not differsignificantly by age or gender. However, these matches were not alwaysexact. Twelve IOT patients were male, while only 11 matched patients weremale. The matched patients were still numerically, but not statistically,older than the IOT patients (mean = 43.4 years). For the inpatient usevariables, the best matches averaged 111.9 days (SD=54.7, range = 32-183,sum = 2,126) which did not differ from the IOT patients (comparison 3).The number of admissions during the pre-treatment period is an exactmatch (comparison 3) in every case (mean = 1.6, SD=1.2, range = l-5,sum = 31).

During the post-treatment period, the matched patients decreased sig-nificantly (comparison 2) in both days and number of admissions. Daysdecreased to 64.1 (SD=68.3, range = 0-183, sum = 1,217; J-2.45, d/=18,2-tail />=.025), while admissions decreased to .58 (SD=1.02, range = 0-3,sum = 11; t— 3.75, df= 18, 2-tail, ̂ =.001). Neither days nor admissions dif-fered from the IOT patients during the post-treatment period (compari-son 4). The mean (.58) and total number of admissions (11) as well as thedifferences scores (1.05) for the groups were identical. The IOT groupdecreased numerically, though not significantly, more than the matchedgroup. The difference scores for the IOT patients averaged 68.4 days,while the mean for matched patients decreased by 47.8 days.

Using an identically-sized group of patients who best matched the IOTpatients on days and admissions in the pre-treatment period and who didnot differ on gender or age, we found that both groups declined similarlyon both inpatient variables. The two groups were numerically and statis-tically identical in number of admissions during both the pre- and post-treatment periods. Thus it appears that, based on analyses using this groupof matches, claims that IOT has effected a change in inpatient use arecalled into question.

DISCUSSION

In the analyses presented here we have attempted to learn whether theimposition of IOT has had the desired effect of reducing the use of hospi-talization, measured as number of psychiatric admissions experienced andpsychiatric inpatient days used, in individuals who came to the attention ofthe mental health system because of their excessive use of inpatient treat-ment. In an effort to discern the effects of this intervention from statisticalartifact and from extraneous factors in the mental health services and pol-icy environment, we attempted to compare the service use patterns of IOTpatients with those of persons with similar hospital use patterns who had

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not received the IOT intervention. The results of this analysis are unfor-tunately equivocal. The trends observed in our data seem to suggest thatIOT has had an effect, particularly with respect to reduced use of hospitaldays, that was less pronounced for patients not under an IOT order. Butthese differences did not achieve statistical significance. Given the emerg-ing significance of IOT, it is important that we examine factors that mighthave shaped the patterns we observe here.

There are two areas we can look toward in attempting to understandthese findings. One has to do with the data used in our study and theapproach used in analyzing it. The second has to do with the changingnature of the mental health services and policy environment—what Cookand Campbell (1979) refer to as "history effects"—in which the observedservice use patterns actually occurred.

A number of methodological problems arise in a study such as this. Be-cause IOT has been used sparingly, the number of cases and, thereforematched pairs, was small. Because small sample sizes yield low statisticalpower, the likelihood that observed differences would reach statistical sig-nificance in a sample such as ours is greatly reduced. But the fact that theobserved trends indicated differences that statistical tests showed to benon-significant suggests that analyses based on larger samples might yieldsignificant results.

It should also be noted that our matching process may not have gener-ated an ideal control group Persons in the IOT group were placed underthis authority ostensibly because of their service use patterns. Individualsin the control group were chosen because their service use patterns"matched" those in the IOT group, yet these individuals had not been thefocus of IOT. While the matches had psychotic diagnoses, as did the IOTpatients, there may have been some major but unmeasured feature of thematches that distinguishes them from the IOT group, but one for whichwe have not controlled. Such a factor would contaminate our matchingprocess, and thus the findings of our analyses. Our matching process canattenuate some threats to validity, but by itself cannot solve the problem oftreatment selection that likely afflicts this study.

The "history" effects are events in the study environment that are ofsufficient strength to "wash out" the effect of the treatment intervention.The Massachusetts mental health system has indeed experienced any num-ber of such events during the time period studied here. Among these arethe downsizing and closure of state hospitals, increased allocation of re-sources to community based services, and the introduction of managedcare under a Medicaid behavioral health carve out. All of these interven-tions have focused, in part, on reducing the use of hospitalization, and allarguably are of sufficient import to attenuate excessive inpatient use byboth IOT patients and their matches.


These confounding factors and methodological issues have impactedupon our efforts to assess the effects of a competency based, non-stat-utorial approach to IOT. Clearly, however, this line of research must bepursued. The need to manage the symptoms of those individuals in thecommunity who will not willingly do so themselves becomes increasinglyexigent as the goal of reduced hospitalization continues to be pursued.IOT may itself effectively facilitate this outcome. Our results need to becompared to efforts in IOT in other states (Fernandez, 1992; Fernandez &Nygard, 1990; Geller, 1986; Hiday & Goodman, 1982; Hiday & Scheid-Cook, 1987, 1989; Lamb & Weinberger, 1992, 1993; Schneider-Braus, 1986;VanPutten, 1988; Zanni, 1986), most of which use dangerous rather thancompetency standards. In so doing, it will be important to keep focused ondistinguishing between conditional release, outpatient commitment, andguardianship proceedings. In all instances, a risks/benefits analysis is war-ranted (Mulvey, Geller, & Roth, 1987).

Our study focused on the involuntary use of pharmacotherapy. We arewell aware that good treatment of psychiatric patients requires a full bio-psychosocial approach in each case. The Massachusetts approach is predi-cated on the assumption that the minimal amount of involuntariness is tocompel compliance with medication, thereby restoring the capacity tomake competent decisions, and thus allowing individuals to freely choosethe other components of their treatment. The validity of these assump-tions can and should be tested.

In fact, research must be continued in Massachusetts and elsewhere(Geller, 1996), to discern whether all the "costs" of IOT, including abroga-tions of freedom and autonomy; involvement of courts, police and otheragents; and potential stigma are justified by the benefits of reduced hospi-talization. And far more important is the larger research question, doesIOT improve insight and quality of life for those who are subjected to it?

APPENDIX A

State Hospital (SH) and any other inpatient psychiatric facilities, are hereby authorizedto administer antipsychotic medication to (the ward), as clinically indicated, in accordancewith the treatment plan as approved on (date). Upon (the ward's) discharge from an inpa-tient psychiatric facility, such medication may be administered by SH or such other facilitiesas may be deemed appropriate by the ward's treatment team.

If while in the community, (the ward), should fail to appear when scheduled to receivesuch medication, or refuse to accept such medication, reasonable efforts shall be made by(the ward's) case manager to contact the ward in order to attempt to persuade him toappear and to accept such medication.

Should such efforts be unsuccessful, or should (the ward's) case manager be unable tolocate him within 48 hours of his failure to appear, (the ward's) case manager shall notifythe ward's outpatient psychiatrist or his designee and his guardian.

If, in the opinion of (the ward's) outpatient psychiatrist or his designee, the failure orrefusal to so appear or accept medication would pose a likelihood of serious harm, the

J. Geller, A.]. Grudzinskas, Jr., M. McDermeit, W.H. Fisher, and '!'. Lawlor 283


psychiatrist or his designee shall so inform the guardian. The guardian is hereby em-powered to take such steps as may be reasonable, to detain and transport the ward to atreatment location in order to administer medication in accordance with the treatmentplan.

Such reasonable steps may include notifying the local police of the extant circumstances,including the terms of this Order, and requesting that the police detain and transport theward, to a facility designated by the psychiatrist or his designee, for the sole purpose ofadministering such medication. Upon arrival at the treatment location, the medicationauthorized under this Order shall, if then clinically indicated, be administered; provided,however, that should the ward resist, the treatment team may authorize the use of the leastintrusive, least restrictive form of control or restraint necessary to safely administer suchmedication. In no event, however, shall mechanical or chemical restraint be utilized otherthan in accordance with Massachusetts law.

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The efficacy of involuntary outpatient treatment in Massachusetts